├── Documents ├── README.md ├── DEMO CV.pdf ├── Python Roadmap.pdf ├── data - job responsibilities.pdf ├── Roadmap on Data Analysis with Python.pdf ├── Software Engineering with Machine Learning.pdf ├── Roadmap on Data science for Non-CSE Students.pdf └── Roadmap on Machine Learning and Deep Learning.pdf ├── README.md ├── AI Agent Developer └── Readme.md ├── Software Engineering with ML-AI └── Readme.md ├── Computer Vision Engineer └── README.md ├── NLP Engineer └── README.md ├── Domain-Specific ML for Researchers └── README.md ├── Data Engineer └── README.md ├── Data Analyst └── README.md ├── Data Scientist └── README.md ├── Generative AI Engineer └── README.md ├── Machine Learning Engineer └── README.md └── AI Engineer └── README.md /Documents/README.md: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /Documents/DEMO CV.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rashakil-ds/Roadmap-Docs/main/Documents/DEMO CV.pdf -------------------------------------------------------------------------------- /Documents/Python Roadmap.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rashakil-ds/Roadmap-Docs/main/Documents/Python Roadmap.pdf -------------------------------------------------------------------------------- /Documents/data - job responsibilities.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rashakil-ds/Roadmap-Docs/main/Documents/data - job responsibilities.pdf -------------------------------------------------------------------------------- /Documents/Roadmap on Data Analysis with Python.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rashakil-ds/Roadmap-Docs/main/Documents/Roadmap on Data Analysis with Python.pdf -------------------------------------------------------------------------------- /Documents/Software Engineering with Machine Learning.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rashakil-ds/Roadmap-Docs/main/Documents/Software Engineering with Machine Learning.pdf -------------------------------------------------------------------------------- /Documents/Roadmap on Data science for Non-CSE Students.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rashakil-ds/Roadmap-Docs/main/Documents/Roadmap on Data science for Non-CSE Students.pdf -------------------------------------------------------------------------------- /Documents/Roadmap on Machine Learning and Deep Learning.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rashakil-ds/Roadmap-Docs/main/Documents/Roadmap on Machine Learning and Deep Learning.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Data Science & AI Career Roadmaps 2025/26 2 | 3 | Best Data Science, Data Analytics, AI, and SDE roadmaps. [This repository is continually updated](https://github.com/rashakil-ds/Roadmap-Docs) based on the top `job postings` on **LinkedIn** and **Indeed**. Please pray for my family and me. Your prayers are all I ask for as a token of appreciation. 4 | 5 | To succeed in any career, it's **very important** to understand the **roadmap, required skills, and timeline** for the role. This approach minimizes wasted time and ensures you focus on learning the skills needed to get a job. This repository aims to provide comprehensive roadmaps for various roles in the **Data Analytics**, **Data Science**, and **Artificial Intelligence** industry. Whether you're a student, a professional, or just curious about AI, these guides are designed to help you navigate your learning and career journey with clarity and confidence. 6 | 7 | ## About the Author 8 | 9 | **Rashedul Alam Shakil** 10 | - 🌐 [LinkedIn Profile](https://www.linkedin.com/in/kmrashedulalam/) 11 | - 🎓 Founder of [Study Mart](https://www.youtube.com/@StudyMart) 12 | - 🤖 Founder of [aiQuest Intelligence](https://aiquest.org/) 13 | 14 | With a passion for education and technology, I have created this resource to empower students and professionals to achieve their goals in AI, Data Science, and other tech domains. 15 | 16 | ## What You’ll Find Here 17 | 18 | This repository contains roadmaps for the following roles: 19 | 20 | - **Data Analyst** 21 | - **Data Engineer** 22 | - **Data Scientist** 23 | - **AI Engineer** 24 | - **AI Agent Developer** 25 | - **Software Engineer with Machine Learning** 26 | - **Computer Vision Engineer** 27 | - **Generative AI Engineer** 28 | - **Machine Learning Engineer** 29 | - **NLP Engineer** 30 | - **Domain-Specific ML Topics for Researchers** 31 | 32 | Each roadmap is designed to guide you through the skills, tools, and knowledge required for the respective roles. These roadmaps are your blueprint for success, from foundational concepts to advanced techniques. 33 | 34 | 35 | ## How to Use This Repository 36 | 37 | 1. Browse through the folders to find the roadmap for your desired role. 38 | 2. Start with the README file in each folder, which serves as a guide for the roadmap. 39 | 3. Use the suggested learning materials, projects, and career tips to enhance your skills. 40 | 41 | ## Feedback and Contributions 42 | 43 | Your feedback is invaluable! Feel free to suggest improvements, share additional resources, or contribute to the roadmaps. 44 | 45 | - **Create an issue** for feedback. 46 | - **Submit a pull request** to contribute content. 47 | 48 | ## Let’s Grow Together 49 | 50 | Using the resources in this repository, we hope to inspire and guide you toward a successful career in technology. Together, let's make learning accessible and meaningful for everyone. 51 | -------------------------------------------------------------------------------- /AI Agent Developer/Readme.md: -------------------------------------------------------------------------------- 1 | # AI Agent Developer Roadmap in 2025/26 2 | 3 | > **Job Category:** `Emerging & High-Demand` 4 | > **Best for:** `AI Engineers`, `Software Developers`, `Data Scientists`, `Automation Enthusiasts`. 5 | 6 | --- 7 | 8 | ## What is an AI Agent Developer? 9 | An **AI Agent Developer** designs and builds **autonomous AI systems** that can reason, plan, and act. 10 | They integrate **LLMs with tools/APIs**, develop **multi-agent workflows**, and ensure **scalable, secure, and ethical AI deployments**. 11 | 12 | ### Responsibilities 13 | - Build **task-driven AI agents** with autonomy and reasoning. 14 | - Connect agents to **APIs, databases, and external tools**. 15 | - Develop **multi-agent systems** for collaboration. 16 | - Ensure **scalability, safety, and ethical compliance**. 17 | 18 | --- 19 | 20 | ### **Step 1: Core Programming Skills** 21 | - **Languages**: Python (primary), TypeScript/JS (for integrations). 22 | - **Key Libraries**: `requests`, `asyncio`, `httpx`, `pydantic`, `FastAPI`. 23 | 24 | ## Resources: 25 | - [Python Docs](https://docs.python.org/3/tutorial/index.html) 26 | - [Python Tutorial](https://www.youtube.com/playlist?list=PLKdU0fuY4OFf7qj4eoBtvALAB_Ml2rN0V) 27 | - [FastAPI](https://fastapi.tiangolo.com/) 28 | - [FastAPI Tutorials](https://www.youtube.com/playlist?list=PLKdU0fuY4OFeNGB1m9pbvPukND6NhjK4L) 29 | 30 | --- 31 | 32 | ### **Step 2: AI & LLM Fundamentals** 33 | - Transformer architecture & embeddings. 34 | - Pretrained LLMs: GPT-4, Claude, Gemini, Mistral, LLaMA. 35 | - Fine-tuning, RAG, and Prompt Engineering. 36 | 37 | ## Resources: 38 | - [Agentic AI with LLMs](https://aiquest.org/agentic-ai-with-llms/) 39 | - [LLMs from Scratch](https://youtu.be/p3sij8QzONQ) 40 | - [Hugging Face Transformers](https://huggingface.co/transformers/) 41 | - [OpenAI Cookbook](https://github.com/openai/openai-cookbook) 42 | 43 | --- 44 | 45 | ### **Step 3: Prompt Engineering & Orchestration** 46 | - Zero-shot, Few-shot, Chain-of-Thought. 47 | - Frameworks: **LangChain**, **LlamaIndex**, **LangGraph**. 48 | - Safety filters & guardrails. 49 | 50 | ## Resources: 51 | - [LangChain Docs](https://python.langchain.com/docs/introduction/) 52 | - [LangGraph](https://www.langchain.com/langgraph) 53 | - [Deep Learning & Generative AI](https://aiquest.org/courses/deep-learning-and-generative-ai/) 54 | 55 | --- 56 | 57 | ### **Step 4: Vector Databases & Knowledge Integration** 58 | - Tools: **FAISS**, **ChromaDB**, **Pinecone**, **Weaviate**. 59 | - Skills: Embeddings, semantic search, hybrid search. 60 | 61 | ## Resources: 62 | - [FAISS Guide](https://github.com/facebookresearch/faiss) 63 | - [Pinecone Docs](https://docs.pinecone.io/) 64 | - [Agentic AI with LLMs](https://aiquest.org/agentic-ai-with-llms/) 65 | 66 | --- 67 | 68 | ### **Step 5: Building AI Agents** 69 | - **Single-Agent**: chatbots, code assistants, research bots. 70 | - **Multi-Agent**: role-based teamwork (AutoGPT, CrewAI). 71 | - Skills: Planning, tool usage, memory, reasoning. 72 | 73 | ## Frameworks: 74 | - [AutoGPT](https://github.com/Torantulino/Auto-GPT) 75 | - [CrewAI](https://github.com/joaomdmoura/crewai) 76 | - [Microsoft Autogen](https://github.com/microsoft/autogen) 77 | - [Agentic AI with LLMs](https://aiquest.org/agentic-ai-with-llms/) 78 | 79 | --- 80 | 81 | ### **Step 6: Reinforcement Learning (RL) for Agents** 82 | - Core: MDPs, Bellman Equations, Q-Learning. 83 | - Advanced: PPO, DQN, Actor-Critic. 84 | - Applications: Tool selection, adaptive agents. 85 | 86 | ## Resources: 87 | - [Stable Baselines3](https://stable-baselines3.readthedocs.io/) 88 | - [Spinning Up RL](https://spinningup.openai.com/) 89 | - [Agentic AI with LLMs](https://aiquest.org/agentic-ai-with-llms/) 90 | 91 | --- 92 | 93 | ### **Step 7: Deployment & MLOps** 94 | - Frameworks: **FastAPI**, **Flask**, **Streamlit**, **Gradio**. 95 | - Containerization: Docker, Kubernetes. 96 | - Cloud: AWS Bedrock, Azure AI, Google Vertex AI. 97 | 98 | ## Resources: 99 | - [Ray Serve](https://docs.ray.io/en/latest/serve/index.html) 100 | - [Deploy AI with FastAPI](https://fastapi.tiangolo.com/) 101 | - [FastAPI Tutorials](https://www.youtube.com/playlist?list=PLKdU0fuY4OFeNGB1m9pbvPukND6NhjK4L) 102 | - [Agentic AI with LLMs](https://aiquest.org/agentic-ai-with-llms/) 103 | 104 | --- 105 | 106 | ## Project Ideas 107 | 1. **AI Researcher Agent** → AutoGPT-style web research assistant. 108 | 2. **Customer Support Agent** → Conversational bot with memory + DB lookup. 109 | 3. **AI Email & Calendar Assistant** → Summarization + scheduling. 110 | 4. **Multi-Agent Workflow** → Manager + Developer + Tester roles. 111 | 5. **Code Assistant Agent** → Integrates with GitHub repos. 112 | 113 | --- 114 | 115 | ## Collaboration & Portfolio 116 | - Use **GitHub** to showcase projects. 117 | - Contribute to open-source (LangChain, CrewAI, Autogen). 118 | - Share demos via **Hugging Face Spaces, Gradio, Streamlit**. 119 | 120 | ## Useful Repos: 121 | - [Awesome-LLM](https://github.com/Hannibal046/Awesome-LLM) 122 | - [CrewAI GitHub](https://github.com/joaomdmoura/crewai) 123 | 124 | --- 125 | 126 | ## Final Workflow for AI Agent Developer 127 | 1. Pick the right **LLM** (OpenAI, DeepSeek, LLaMA, Mistral). 128 | 2. Orchestrate with **LangChain / Autogen**. 129 | 3. Add **Vector DB for context**. 130 | 4. Deploy with **FastAPI + Docker**. 131 | 5. Secure with **guardrails** & monitor. 132 | 133 | --- 134 | - [Agentic AI with LLMs](https://aiquest.org/agentic-ai-with-llms/) 135 | 136 | ## About 137 | **Author:** [Rashedul Alam Shakil](https://www.linkedin.com/in/kmrashedulalam/) 138 | Industry Expert | Educator | AI Practitioner 139 | 140 | --- 141 | 142 | -------------------------------------------------------------------------------- /Software Engineering with ML-AI/Readme.md: -------------------------------------------------------------------------------- 1 | [Software Engineering with Machine Learning](https://youtu.be/0I0eRhg9ReE) – এই ক্যারিয়ার ট্রাকটা যে দিন দিন এত জনপ্রিয় হচ্ছে তা বলার বাইরে। LinkedIn যত মেশিন লার্নিং রিলেটেড জব দেখবেন তার বড় অংশ হলো সফটওয়্যার ইন্জিনিয়ারিং এর সাথে মেশিন লার্নিং, কিছু জায়গায় সরাসরি লেখা থাকে API Development স্কিলের সাথে মেশিন লার্নিং must! তারপরে Software Engineering with AI Agent. 2 | 3 | তারমানে হলো এখন Machine Learning শুধু একজায়গায় সীমাবদ্ধ নাই। বিভিন্ন রকম জব ফিল্ডের একটাা কমন স্কিল হলো `মেশিন লার্নিং`। 4 | 5 | --- 6 | 7 | **🔴 Software Engineering with Machine Learning = Software Engineering Skills + Machine Learning & Deep Learning Skills** 8 | 9 | --- 10 | 11 | ### ছোট করে একটা **রোডম্যাপ** দেয়ার চেস্টা করবো। যদি কারও উপকার হয় 🙂 12 | 13 | **১।** এই ট্র্যাকে আসতে হলে প্রথমেই দরকার শক্ত *Python* ব্যাকগ্রাউন্ড। Functions, OOP, error handling, এবং data structures (list, dict, set, tuple) ভালোভাবে বুঝে নিতে হবে। এরপর প্রয়োজন ওয়েব ডেভেলপমেন্ট স্কিল, বিশেষ করে Django বা FastAPI শেখা, যেগুলোর মাধ্যমে API তৈরি করা যায়। Django-তে views, models, templates এবং REST API development (DRF) শেখার পাশাপাশি Authentication, Permissions আর ORM ভালোভাবে আয়ত্তে আনতে হবে অবশ্যই। 14 | 15 | **২।** Git এবং Docker হলো সফটওয়্যার ইঞ্জিনিয়ারের অন্যতম হাতিয়ার। Git দিয়ে version control, Docker দিয়ে অ্যাপ কনটেইনারাইজ এবং GitHub Actions-এর মাধ্যমে basic CI/CD pipeline তৈরির অভ্যাস গড়ে তুলতে হয়। এই স্কিলগুলো ছাড়া production-level কোড মেইনটেইন করা কষ্টকর। ওকে? 16 | 17 | **৩।** এরপর আসলেই আসে Machine Learning এর জায়গা। scikit-learn দিয়ে classification, regression এর মত basic মডেল তৈরি করা, pandas ও numpy দিয়ে ডেটা প্রিপ্রসেসিং, TensorFlow, pytorch দিয়ে ডীপ লার্নিং/জেনারেটিভ এআই এবং মডেল pickle বা joblib দিয়ে save/load করার স্কিল দরকার হয়। কিন্তু সবচেয়ে গুরুত্বপূর্ণ অংশ হলো, এই মডেলগুলোকে Django বা FastAPI API-তে serve করা, মানে ইউজার যখন ইনপুট দেয়, তখন ব্যাকএন্ড সেই মডেল দিয়ে output তৈরি করে পাঠায়। [Machine Learning & AI - Required Skills](https://youtu.be/69VhZXAEqjw) 18 | 19 | **৪।** এই কাজের সময় API performance নিয়ে ভাবতে হয়। যেমন latency কমানো, invalid ইনপুট হ্যান্ডেল করা, এবং নিরাপদ API বানানোর জন্য token-based authentication বা rate limiting ব্যবহারের প্রয়োজন হয়। 20 | 21 | **৫।** ডাটাবেইজের দিক থেকেও প্রস্তুত থাকতে হয়। সাধারণত PostgreSQL বা MongoDB বেশি ব্যবহৃত হয়। PostgreSQL বেশি রিলায়েবল relational কাজের জন্য, আর MongoDB কাজ দেয় যখন ডেটা structure একটু dynamic হয়। Django ORM দিয়ে PostgreSQL ব্যবহারে সুবিধা হয়, এবং FastAPI + MongoDB একটা modern stack হিসেবে কাজ করে। 22 | 23 | **৬।** Deployment-এর সময় Docker দিয়ে অ্যাপ কনটেইনারাইজ করে Render, Railway বা AWS-এর মত সার্ভারে আপলোড করতে হয়। সেখানেও ML মডেলের compatibility, scaleability এবং logging দেখতে হয়। চাইলে Redis বা Celery ব্যবহার করে async background task প্রসেস করাও সম্ভব। 24 | 25 | এই ট্র্যাক ফলো করলে আপনি চাইলে SaaS অ্যাপ তৈরি করতে পারো যেখানে মেশিন লার্নিং কাজ করে behind-the-scenes। আর এর demand এখন ক্রমেই বাড়ছে—both freelancing and job market-এ। 26 | 27 | --- 28 | 29 | ### রোডম্যাপ এক নজরে: 30 | ### 1. Python Proficiency 31 | - Basic, Loops, Functions, OOP (Object-Oriented Programming) 32 | - Data Structures: `list`, `dict`, `set`, `tuple` 33 | - Error Handling 34 | 35 | ### 2. Web Development 36 | - **Frameworks:** Django or FastAPI 37 | - REST API Development (DRF for Django) 38 | - Authentication, Permissions, ORM 39 | 40 | ### 3. Dev Tools 41 | - Git for Version Control 42 | - Docker for Containerization 43 | - GitHub Actions for Basic CI/CD Pipelines 44 | 45 | ### 4. Machine Learning & Deep Learning 46 | - **Statistics & Linear Algebra** for Machine Learning & Deep Learning 47 | - **Libraries:** scikit-learn, pandas, numpy 48 | - **DL Frameworks:** TensorFlow, PyTorch 49 | - **Save/Load Models:** `pickle`, `joblib`, `.h5` 50 | - **Serve models** through Django/FastAPI APIs 51 | 52 | ### 5. API Performance & Security 53 | - Minimize Latency 54 | - Handle Invalid Inputs Gracefully 55 | - Token-Based Authentication 56 | - Rate Limiting 57 | 58 | ### 6. Database Knowledge 59 | - **Relational:** PostgreSQL (best with Django ORM) 60 | - **NoSQL:** MongoDB (modern choice with FastAPI) 61 | - ORM Integration and Optimization 62 | 63 | ### 7. Deployment & Scalability 64 | - Dockerized App Deployment (Render, Railway, AWS) 65 | - Background Tasks with Redis & Celery 66 | - Model compatibility, scalability, and logging 67 | 68 | --- 69 | 70 | ## **Optional Skill (But Important): Generative AI with AI Agent** 71 | ### Why Learn Generative AI with Agents? 72 | - Generative AI powers state-of-the-art applications like chatbots, image generation, summarization, and code generation. 73 | - Agentic AI systems (e.g., AutoGPT, BabyAGI) take GenAI to the next level by **autonomously planning, reasoning, and executing tasks**. 74 | - Enables developers to build intelligent systems that interact with users, APIs, and environments **independently**. 75 | 76 | --- 77 | 78 | ### What to Learn? 79 | #### 1. **LLMs (Large Language Models) Fundamentals** 80 | - Understanding architecture: Transformer, Attention Mechanism 81 | - Pretraining vs Fine-tuning vs Prompt Engineering 82 | - Tokens, Embeddings, and Context Window 83 | - Popular Models: GPT-4, LLaMA, Mistral, Claude, Gemini 84 | 85 | #### 2. **Prompt Engineering** 86 | - Zero-shot, Few-shot, and Chain-of-Thought prompting 87 | - System vs User prompts 88 | - Prompt tuning and injection techniques 89 | 90 | #### 3. **LangChain Framework** 91 | - Chains: Sequential, Conditional, and Custom Chains 92 | - Tools and Agents 93 | - Memory and Retrieval-Augmented Generation (RAG) 94 | - Integration with APIs, Databases, and Filesystems 95 | 96 | #### 4. **Vector Databases** 97 | - FAISS, ChromaDB, Pinecone, Weaviate 98 | - Embedding models (OpenAI, Hugging Face, Sentence Transformers) 99 | - Indexing, similarity search, and metadata filtering 100 | 101 | #### 5. **Agentic AI Concepts** 102 | - Planning, Tool-Usage, Task Decomposition 103 | - Tools: AutoGPT, BabyAGI, LangGraph 104 | - Building multi-step autonomous agents with LangChain Agents or OpenAI Function Calling 105 | 106 | --- 107 | 108 | ### Tools & Libraries 109 | 110 | - [`LangChain`](https://github.com/langchain-ai/langchain) – Framework for building LLM-powered apps 111 | - [`Transformers`](https://github.com/huggingface/transformers) – State-of-the-art models from Hugging Face 112 | - [`FAISS`](https://github.com/facebookresearch/faiss) – Vector similarity search 113 | - [`Chroma`](https://www.trychroma.com/) – Lightweight local vector DB 114 | - [`OpenAI API`](https://platform.openai.com/) – GPT-4/3.5 access 115 | 116 | --- 117 | 118 | ### কিভাবে শিখব? 119 | Software Engineering এর পার্টটুকর জন্য aiquest এর [Backend API Development with Python](https://aiquest.org/courses/backend-api-development-with-python/) কোর্সটি ফলো করতে পারেন। 120 | 121 | [মেশিন লার্নিং](https://aiquest.org/courses/data-science-machine-learning/) ও 122 | [ডীপ লার্নিং](https://aiquest.org/courses/deep-learning-and-generative-ai/) সহ সকল কোর্স www.aiquest.org/courses ওয়েবসাইটেই পাবেন। 123 | 124 | --- 125 | 126 | ### Watch: [Reality of Software Engineering and Machine Learning/AI Jobs](https://youtu.be/MA_JrNr3cvk) 127 | 128 | ভালো লাগলে শেয়ার করবেন। ধন্যবাদ। 129 | 130 | **শুধু পরিশ্রম করলেই যদি সফল হওয়া যেত, তাহলে বনের রাজা হতো গাধা** তাই **স্মার্টলি** পরিশ্রম করুন। 131 | 132 | #softwareengineering 133 | 134 | #machinelearning 135 | 136 | #aiquest 137 | 138 | #studymart 139 | 140 | ---- 141 | ## About the Author 142 | **Rashedul Alam Shakil** 143 | - 🌐 [LinkedIn Profile](https://www.linkedin.com/in/kmrashedulalam/) 144 | - 🎓 Industry Expert | Educator 145 | -------------------------------------------------------------------------------- /Computer Vision Engineer/README.md: -------------------------------------------------------------------------------- 1 | # Computer Vision Engineer Roadmap 2025/26 2 | 3 | - **Job Type:** `Domain Specific` 4 | - **Opportunity:** `Less Job Circular` 5 | 6 | --- 7 | 8 | **Computer Vision** is a field of Artificial Intelligence that enables machines to interpret and understand `visual information` from the world, such as **images** and **videos**. It focuses on tasks like object detection, image recognition, facial recognition, motion analysis, and image generation, aiming to `replicate human vision capabilities`. 9 | 10 | ## **Understand the Role of a Computer Vision Engineer** 11 | 12 | ### **What does a Computer Vision Engineer do?** 13 | - Develop and deploy AI models for image and video analysis. 14 | - Work on tasks like object detection, image classification, and image segmentation. 15 | - Collaborate with hardware and software teams for optimized solutions. 16 | 17 | ### **Responsibilities** 18 | - Building and fine-tuning deep learning models for vision tasks. 19 | - Developing pipelines for large-scale image and video data processing. 20 | - Integrating vision systems into real-world applications. 21 | 22 | --- 23 | 24 | ## **Step 1: Programming Fundamentals (Python)** 25 | 26 | ### **Why Learn Python?** 27 | - AI Engineers need a strong programming foundation for implementing AI models and systems. 28 | 29 | ### **What to Learn?** 30 | - **Python Basics:** 31 | - Variables, data types, loops, conditionals, functions, error handling, debugging, and OOPs. 32 | - **Libraries:** 33 | - **NumPy:** Numerical computations. 34 | - **Pandas & Polars:** Data manipulation and cleaning. 35 | - **Matplotlib/Seaborn/Plotly:** Data visualization. 36 | 37 | ### **Resources** 38 | - [Official Python Docs](https://docs.python.org/3/tutorial/index.html) 39 | - [Python Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFf7qj4eoBtvALAB_Ml2rN0V) 40 | - [Learn Pandas](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdsmcM817qp1L3ngU5amkak) 41 | - [Basic to Advanced Python](https://aiquest.org/courses/become-a-python-developer/) 42 | 43 | --- 44 | 45 | ## **Step 2: Mathematics for AI & Machine Learning** 46 | 47 | ### **Why Learn Mathematics for AI?** 48 | - Provides the foundation for understanding and implementing AI algorithms. 49 | 50 | ### **What to Learn?** 51 | - **Linear Algebra:** 52 | - Matrices, vectors, eigenvalues, eigenvectors. 53 | - **Calculus:** 54 | - Differentiation and integration for optimization. 55 | - **Probability and Statistics:** 56 | - Probability distributions, Bayes' theorem, hypothesis testing. 57 | - **Optimization:** 58 | - Gradient descent, convex and non-convex optimization. 59 | 60 | ### **Resources** 61 | - [A-Z Linear Algebra & Calculus for AI & Data Science](https://www.youtube.com/playlist?list=PLKdU0fuY4OFct6HdBIszzy-jZlicLlkIw) 62 | 63 | --- 64 | 65 | ## **Step 3: Statistical Machine Learning** 66 | 67 | ### **Why Learn Machine Learning?** 68 | - AI Engineers build on ML techniques to create intelligent systems. 69 | 70 | ### **What to Learn?** 71 | - **Statistics:** 72 | - Correlation, Data Distributions, Hypothesis Testing. 73 | - **Supervised Learning:** 74 | - Linear, Polynomial, Logistic Regression. 75 | - Decision Trees, Random Forest, Gradient Boosting (XGBoost, LightGBM, CatBoost). 76 | - **Unsupervised Learning:** 77 | - Clustering (K-means, DBSCAN). 78 | - Dimensionality Reduction (PCA, t-SNE). 79 | - **Model Optimization:** 80 | - Cross-validation, Gradient Descent Variants. 81 | 82 | ### **Resources** 83 | - [Machine Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFfWY36nDJDlI26jXwInSm8f) 84 | - [Machine Learning Module](https://aiquest.org/courses/data-science-machine-learning/) 85 | - [Scikit-learn (`sklearn`)](https://scikit-learn.org/stable/) 86 | - [Statsmodels](https://www.statsmodels.org/stable/index.html) 87 | - [SciPy](https://docs.scipy.org/doc/scipy/reference/index.html) 88 | 89 | --- 90 | 91 | ## **Step 4: Basics of Computer Vision** 92 | 93 | ### **Why Learn Vision Basics?** 94 | - Fundamental concepts are critical for understanding complex vision systems. 95 | 96 | ### **What to Learn?** 97 | - Image processing (resizing, filtering, transformations). 98 | - Feature extraction (HOG, SIFT, SURF). 99 | - Edge detection, color spaces, and histograms. 100 | 101 | ### **Resources** 102 | - [OpenCV Documentation](https://docs.opencv.org/) 103 | - [Computer Vision Playlist](https://youtube.com/playlist?list=PLKdU0fuY4OFfjjFpSfCYzvsMKhZIWL43v) 104 | 105 | --- 106 | 107 | ## **Step 5: Deep Learning for Computer Vision** 108 | 109 | ### **Why Learn Deep Learning for Vision?** 110 | - Deep learning powers the majority of state-of-the-art vision applications. 111 | 112 | ### **What to Learn?** 113 | - Convolutional Neural Networks (CNNs). 114 | - Architectures: 115 | - ResNet, VGG, MobileNet. 116 | - YOLO, Faster R-CNN (object detection). 117 | - Image Segmentation: 118 | - U-Net, Mask R-CNN. 119 | - GANs for Vision (StyleGAN, CycleGAN). 120 | 121 | ### **Resources** 122 | - [TensorFlow Tutorials](https://www.tensorflow.org/tutorials) 123 | - [PyTorch Tutorials](https://pytorch.org/tutorials/beginner/basics/intro.html) 124 | - [Deep Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdFUCFcUp-7VD4bLXr50hgb) 125 | - [Hugging Face Vision Models](https://huggingface.co/models?other=vision) 126 | 127 | --- 128 | 129 | ## **Step 6: Generative AI for Computer Vision** 130 | 131 | ### **Why Learn Generative AI for Vision?** 132 | Generative AI enables machines to create, transform, and understand visual content, opening opportunities in design, entertainment, AR/VR, and more. 133 | 134 | ### **What to Learn?** 135 | - **Generative Models:** 136 | - GANs (StyleGAN, CycleGAN, BigGAN) 137 | - VAEs (Variational Autoencoders) 138 | - Diffusion Models (Stable Diffusion, DALL·E 2, Imagen) 139 | - **Key Tasks:** 140 | - Text-to-image generation 141 | - Image-to-image translation 142 | - Image super-resolution 143 | - Inpainting & outpainting 144 | - Style transfer 145 | - **Fine-tuning Techniques:** 146 | - LoRA (Low-Rank Adaptation) 147 | - DreamBooth 148 | - **Ethics & Responsibility:** 149 | - Bias detection, watermarking, copyright 150 | 151 | ### **Tools** 152 | - [Hugging Face Diffusers](https://huggingface.co/docs/diffusers) 153 | - [Stable Diffusion](https://stability.ai/) 154 | - [OpenAI CLIP](https://github.com/openai/CLIP) 155 | 156 | ### **Resources** 157 | - [Diffusion Models Explained](https://lilianweng.github.io/posts/2021-07-11-diffusion-models/) 158 | - [Diffusion Models](https://huggingface.co/models?pipeline_tag=text-to-image&sort=trending) 159 | 160 | ### **Project Ideas** 161 | 1. Text-to-image art generator 162 | 2. Domain-specific diffusion model 163 | 3. GAN-based photo restoration 164 | 4. Video style transfer with temporal consistency 165 | 166 | --- 167 | 168 | ## **Step 7: Learn GitHub** 169 | - Version control & collaboration 170 | - Hosting projects and portfolios 171 | 172 | **Resources:** 173 | - [GitHub for CV Engineers](https://www.youtube.com/playlist?list=PLKdU0fuY4OFcK__Q5tjqZY5mSx_u7ghUx) 174 | 175 | --- 176 | 177 | ## **Step 8: Learn SQL & NoSQL** 178 | - SQL for structured data 179 | - MongoDB for unstructured data 180 | 181 | **Resources:** 182 | - [SQL Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFduhpa23Wy5fRv6SGxp2ho0) 183 | - [NoSQL Course](https://youtu.be/tww-gbNPOcA) 184 | 185 | --- 186 | 187 | ## **Step 9: Projects** 188 | 189 | **Ideas:** 190 | 1. CNN-based image classification 191 | 2. Object detection for autonomous vehicles 192 | 3. Medical image segmentation 193 | 4. GAN for image-to-image translation 194 | 195 | **Datasets:** 196 | - [Kaggle](https://www.kaggle.com/datasets) 197 | - [COCO Dataset](https://cocodataset.org/#download) 198 | 199 | --- 200 | 201 | ## **Step 10: Additional Languages (Optional)** 202 | - **Java** — enterprise AI systems, big data 203 | - **C++** — robotics, real-time vision, performance-critical systems 204 | 205 | --- 206 | 207 | ## **Final Note: Workflow Integration** 208 | 1. Preprocess with OpenCV/PIL 209 | 2. Train with TensorFlow/PyTorch 210 | 3. Optimize & deploy 211 | 4. Integrate into real-world apps 212 | 213 | --- 214 | 215 | ## **Recommended Courses at aiQuest Intelligence** 216 | 1. [Basic to Advanced Python](https://aiquest.org/courses/become-a-python-developer/) 217 | 2. [Machine Learning Concepts](https://aiquest.org/courses/data-science-machine-learning/) 218 | 3. [Advanced Deep Learning for Computer Vision & GenAI](https://aiquest.org/courses/deep-learning-and-generative-ai/) 219 | 220 | > If you can’t afford premium courses, search YouTube for each topic above. There are excellent free resources, too! 221 | 222 | --- 223 | 224 | **Author:** [Rashedul Alam Shakil](https://www.linkedin.com/in/kmrashedulalam/) 225 | Industry Expert | Educator 226 | 227 | --- 228 | 229 | [Read More Roadmaps](https://github.com/rashakil-ds/Roadmap-Docs) 230 | -------------------------------------------------------------------------------- /NLP Engineer/README.md: -------------------------------------------------------------------------------- 1 | # NLP Engineer Roadmap 2025/26 2 | 3 | - Job Type: `Domain Specific`, `Linguists` 4 | - Opportunity: `Less Job Circular` 5 | 6 | --- 7 | 8 | **Natural Language Processing (NLP)** is a specific field of `Artificial Intelligence (AI)` focused on enabling machines to understand, interpret, and respond to human language meaningfully. NLP `bridges` the gap between human communication and machine understanding, making it possible for computers to process and analyze large amounts of natural language data. 9 | 10 | --- 11 | ## **Understand the Role of NLP Engineer** 12 | 13 | ### **What does an NLP Engineer do?** 14 | - Develop, fine-tune, and deploy NLP models for language understanding and generation. 15 | - Work on translation, sentiment analysis, chatbots, and summarization tasks. 16 | - Collaborate with data scientists and software engineers to integrate NLP systems into products. 17 | 18 | ### **Responsibilities** 19 | - Preprocessing text data (tokenization, stemming, lemmatization). 20 | - Build and optimize NLP models for specific tasks. 21 | - Deploying NLP solutions and integrating them into applications. 22 | - Researching and applying cutting-edge advancements in NLP. 23 | 24 | --- 25 | 26 | ## **Step 01: Programming and Python Libraries** 27 | 28 | ### **Why Learn Python for NLP?** 29 | - Python has robust libraries for text processing, NLP, and machine learning. 30 | 31 | ### **What to Learn?** 32 | - **Python Basics:** 33 | - Variables, data types, loops, conditionals, functions, and OOPs. 34 | - **Libraries:** 35 | - **Pandas/Polars:** DataFrame library. 36 | - **NLTK & SpaCy:** For text preprocessing. 37 | 38 | ### **Resources** 39 | - [Official Docs of Python](https://docs.python.org/3/tutorial/index.html) 40 | - [Python Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFf7qj4eoBtvALAB_Ml2rN0V) 41 | - [Basic to Advanced Python](https://aiquest.org/courses/become-a-python-developer/) 42 | - [Hugging Face Tutorials](https://huggingface.co/transformers/) 43 | 44 | --- 45 | 46 | ## **Step 02: Foundations of Natural Language Processing (NLP)** 47 | 48 | ### **Why Learn NLP Basics?** 49 | - Understanding foundational concepts is critical for building advanced models. 50 | 51 | ### **What to Learn?** 52 | - Tokenization, Stemming, Lemmatization. 53 | - Stopwords removal, Part-of-Speech tagging, Named Entity Recognition (NER). 54 | - Bag of Words, TF-IDF. 55 | - Word Embeddings (Word2Vec, GloVe, FastText). 56 | 57 | ### **Resources** 58 | - [Natural Language Toolkit Docs](https://www.nltk.org/) 59 | - [SpaCy Tutorials](https://spacy.io/) 60 | - [NLP Videos - Machine Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFfWY36nDJDlI26jXwInSm8f) 61 | 62 | --- 63 | 64 | ## **Step 03: Machine Learning for NLP** 65 | 66 | ### **Why Learn ML for NLP?** 67 | - Classical ML techniques are the basis for many NLP tasks. 68 | 69 | ### **What to Learn?** 70 | - Text Classification (Naive Bayes, SVM). 71 | - Sentiment Analysis, Topic Modeling (Latent Dirichlet Allocation). 72 | - Feature Engineering for Text Data. 73 | 74 | ### **Resources** 75 | - [NLP Videos - Machine Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFfWY36nDJDlI26jXwInSm8f) 76 | - [NLP Module](https://aiquest.org/courses/data-science-machine-learning/) 77 | - **Libraries:** 78 | - **NLTK & SpaCy:** For text preprocessing. 79 | - **Scikit-learn:** For classical machine learning tasks. 80 | --- 81 | 82 | ## **Step 04: Deep Learning for NLP** 83 | 84 | ### **Why Learn Deep Learning for NLP?** 85 | - Powers advanced NLP models for understanding and generating text. 86 | 87 | ### **What to Learn?** 88 | - Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), GRU. 89 | - Transformer Architectures (BERT, GPT, T5). 90 | - Sequence-to-Sequence Models (Seq2Seq, Attention Mechanisms). 91 | - Fine-tuning Pre-trained Models for Custom Tasks. 92 | 93 | ### **Resources** 94 | - [Deep Learning Playlist (ANN, RNN, LSTM, GRU, Transformers)](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdFUCFcUp-7VD4bLXr50hgb) 95 | - [Hugging Face Tutorials](https://huggingface.co/transformers/) 96 | - [Basic to Advanced DL & GenAI](https://aiquest.org/courses/deep-learning-and-generative-ai/) 97 | - **Libraries:** 98 | - **NLTK & SpaCy:** For text preprocessing. 99 | - **Hugging Face Transformers:** For state-of-the-art NLP models. 100 | - **TensorFlow/PyTorch:** For custom deep learning-based NLP solutions. 101 | --- 102 | 103 | ## **Step 05: Generative Models** 104 | 105 | ### **Why Learn Generative Models?** 106 | - Generative models drive content creation in text, audio, and more. 107 | 108 | ### **What to Learn?** 109 | - **Variational Autoencoders (VAEs):** 110 | - Applications in text generation and compression. 111 | - **Transformers:** 112 | - GPT, DALL-E, T5. 113 | - **Fine-Tuning and Custom Training:** 114 | - Domain-specific adaptations of pre-trained models. 115 | 116 | ### **Resources** 117 | - [Generative AI Guide](https://huggingface.co/models) 118 | - [LangChain](https://python.langchain.com/docs/introduction/) 119 | - [Generative AI Course](https://aiquest.org/courses/deep-learning-and-generative-ai/) 120 | - [Stable Diffusion](https://github.com/CompVis/stable-diffusion) 121 | 122 | ----------------------------------------------- 123 | ## **Step 06: Learn GitHub** 124 | - GitHub is a crucial platform for version control and collaboration. 125 | - Enables you to showcase your projects and build a portfolio. 126 | - Facilitates teamwork on data science projects. 127 | 128 | ### **What to Learn?** 129 | - **Git Basics:** 130 | - Version control concepts, repositories, branches, commits, pull requests. 131 | - **GitHub Skills:** 132 | - Hosting projects, collaboration workflows, managing issues. 133 | - **Best Practices:** 134 | - Writing READMEs, structuring repositories, using `.gitignore` files. 135 | 136 | ### **Resources** 137 | - [Complete GitHub for NLP Engineers](https://www.youtube.com/playlist?list=PLKdU0fuY4OFcK__Q5tjqZY5mSx_u7ghUx) 138 | - Use GitHub to practice hosting Python, SQL, and machine learning projects. 139 | 140 | ----------------------------------------------- 141 | ## **Step 07: SQL** 142 | 143 | ### **Why Learn SQL?** 144 | - Essential for querying, extracting, and joining data from relational databases. 145 | - Used to preprocess and prepare data before modeling. 146 | 147 | ### **What to Learn?** 148 | - Basics: SELECT, INSERT, UPDATE, DELETE. 149 | - Intermediate: Joins (INNER, LEFT, RIGHT, FULL), subqueries. 150 | - Advanced: Window functions, CTEs (Common Table Expressions), and query optimization. 151 | 152 | ### **Resources** 153 | - [SQL Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFduhpa23Wy5fRv6SGxp2ho0) 154 | - [Programming with Mosh - SQL Playlist](https://youtu.be/7S_tz1z_5bA) 155 | - Tools like MySQL Workbench, SQLite, or PostgreSQL. 156 | 157 | --- 158 | ## **Step 08: Projects** 159 | 160 | ### **Why Work on Projects?** 161 | - Projects showcase your ability to apply NLP techniques in real-world scenarios. 162 | 163 | ### **Ideas for Projects** 164 | 1. Build a sentiment analysis tool for customer reviews. 165 | 2. Create a chatbot using Transformer models. 166 | 3. Design an automatic summarizer for news articles. 167 | 4. Fine-tune BERT for a domain-specific NER task. 168 | 169 | ### **Where to Find Data?** 170 | - [Kaggle](https://www.kaggle.com/datasets) 171 | - [Hugging Face Datasets](https://huggingface.co/datasets) 172 | 173 | --- 174 | 175 | ## **Final Note: Workflow Integration** 176 | 1. Preprocess text data using tools like NLTK or SpaCy. 177 | 2. Train models using Scikit-learn, TensorFlow, or PyTorch. 178 | 3. Fine-tune Transformer models for advanced NLP tasks. 179 | 4. Deploy and integrate NLP models into applications. 180 | 181 | By following this roadmap, you’ll develop the skills needed to become a successful `NLP Engineer`. 182 | 183 | --- 184 | # Recomended Courses at aiQuest Intelligence 185 | 1. [Basic to Advanced Python](https://aiquest.org/courses/become-a-python-developer/) 186 | 2. [Machine Learning Concepts](https://aiquest.org/courses/data-science-machine-learning/) 187 | 3. [Advanced Deep Learning for NLP & Generative AI](https://aiquest.org/courses/deep-learning-and-generative-ai/) 188 | 189 | *`Note:`* We suggest these premium courses because they are well-organized for absolute beginners and will guide you step by step, from basic to advanced levels. Always remember that `T-shaped skills` are better than `i-shaped skill`. However, for those who cannot afford these courses, don't worry! Search on YouTube using the topic names mentioned in the roadmap. You will find plenty of `free tutorials` that are also great for learning. Best of luck! 190 | 191 | --- 192 | 193 | ## About the Author 194 | **Rashedul Alam Shakil** 195 | - 🌐 [LinkedIn Profile](https://www.linkedin.com/in/kmrashedulalam/) 196 | - 🎓 Industry Expert | Educator 197 | 198 | --- 199 | 200 | ## Other Roadmaps 201 | - [Read More Roadmaps](https://github.com/rashakil-ds/Roadmap-Docs) 202 | 203 | -------------------------------------------------------------------------------- /Domain-Specific ML for Researchers/README.md: -------------------------------------------------------------------------------- 1 | # Research-Oriented AI Domains Roadmap 2025/26 2 | 3 | - **Purpose:** Research-focused learning for specific domains to write papers and conduct studies. 4 | - **Target Audience:** Researchers, Academics, and ML/AI Enthusiasts. 5 | 6 | This roadmap is designed for `domain-specific research`, so you don’t need to learn everything related to Machine Learning or AI. Instead, focus only on the topics relevant to your chosen research domain. This approach ensures a streamlined and efficient learning path tailored to your goals. 7 | 8 | ### Our covered domains include: 9 | - Statistical Machine Learning (Classification & Regression) 10 | - Computer Vision 11 | - NLP & Sentiment Analysis 12 | - Time Series Analysis 13 | - Generative AI with Pretrained Models 14 | 15 | You can dive deep into your selected area without worrying about unrelated concepts from other domains. This specificity will save time and enhance your expertise in the chosen field. Best of luck 😊 16 | 17 | --- 18 | 19 | ## **Step 1: Programming Fundamentals (Python)** 20 | 21 | ### **Why Learn Python?** 22 | - AI Engineers need a strong programming foundation for implementing AI models and systems. 23 | 24 | ### **What to Learn?** 25 | - **Python Basics:** 26 | - Variables, data types, loops, conditionals, functions, error handling, debugging, and OOPs. 27 | - **Libraries:** 28 | - **NumPy:** Numerical computations. 29 | - **Pandas & Polars:** Data manipulation and cleaning. 30 | - **Matplotlib/Seaborn/Plotly:** Data visualization. 31 | 32 | ### **Resources** 33 | - [Official Python Docs](https://docs.python.org/3/tutorial/index.html) 34 | - [Python Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFf7qj4eoBtvALAB_Ml2rN0V) 35 | - [Learn Pandas](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdsmcM817qp1L3ngU5amkak) 36 | - [Basic to Advanced Python](https://aiquest.org/courses/become-a-python-developer/) 37 | 38 | ----------------------------------------------- 39 | 40 | ## **Step 2: Mathematics for AI** 41 | 42 | ### **Why Learn Mathematics for AI?** 43 | - Provides the foundation for understanding and implementing AI algorithms. 44 | 45 | ### **What to Learn?** 46 | - **Linear Algebra:** 47 | - Matrices, vectors, eigenvalues, eigenvectors. 48 | - **Calculus:** 49 | - Differentiation and integration for optimization. 50 | - **Probability and Statistics:** 51 | - Probability distributions, Bayes' theorem, hypothesis testing. 52 | - **Optimization:** 53 | - Gradient descent, convex and non-convex optimization. 54 | 55 | ### **Resources** 56 | - [A-Z Linear Algebra & Calculus for AI & Data Science](https://www.youtube.com/playlist?list=PLKdU0fuY4OFct6HdBIszzy-jZlicLlkIw) 57 | - [Gradient Descent](https://www.youtube.com/playlist?list=PLKdU0fuY4OFe7mmYIb6NYCPRbljkE6is8) 58 | 59 | --- 60 | 61 | ## **Domain 01: Statistical Machine Learning (Classification & Regression)** 62 | 63 | ### **Why Learn Machine Learning?** 64 | - AI Engineers build on ML techniques to create intelligent systems. 65 | 66 | ### **What to Learn?** 67 | - **Statistics** 68 | - Correlation, Data Distributions, Hypothesis Testing 69 | - **Supervised Learning:** 70 | - Linear, Polynomial, Logistic Regression. 71 | - Decision Trees, Random Forest, Gradient Boosting (XGBoost, LightGBM, CatBoost). 72 | - **Unsupervised Learning:** 73 | - Clustering (K-means, DBSCAN). 74 | - Dimensionality Reduction (PCA, t-SNE). 75 | - **Model Optimization:** 76 | - Cross-validation, Gradient Descent Variants. 77 | 78 | ### **Resources** 79 | - [Machine Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFfWY36nDJDlI26jXwInSm8f) 80 | - [Machine Learning Module](https://aiquest.org/courses/data-science-machine-learning/) 81 | - [Scikit-learn (`sklearn`):](https://scikit-learn.org/stable/) For statistical machine learning models. 82 | - [Statsmodels:](https://www.statsmodels.org/stable/index.html) For statistical analysis. 83 | - [SciPy:](https://docs.scipy.org/doc/scipy/reference/index.html) For statistical analysis. 84 | - Practice using Python's `sklearn` and Kaggle competitions. 85 | --- 86 | 87 | 88 | ## **Domain: 02. Computer Vision** 89 | 90 | ### **What is Computer Vision?** 91 | - A field of AI enabling machines to interpret visual data. 92 | - Includes object detection, image segmentation, and classification. 93 | 94 | ### **Responsibilities in Research** 95 | - Experiment with deep learning models for vision tasks. 96 | - Evaluate performance using standard datasets. 97 | - Publish findings in journals or conferences. 98 | 99 | --- 100 | 101 | ### **Step 1: Basics of Computer Vision** 102 | 103 | **Why Learn Vision Basics?** 104 | - Foundational knowledge is essential for image processing tasks. 105 | 106 | **What to Learn?** 107 | - Image Processing: Resizing, filtering, transformations. 108 | - Feature Extraction: HOG, SIFT, SURF. 109 | - Edge Detection and Histograms. 110 | 111 | **Resources** 112 | - [OpenCV Docs](https://docs.opencv.org/) 113 | - [Video Tutorials](https://www.youtube.com/playlist?list=PLKdU0fuY4OFfjjFpSfCYzvsMKhZIWL43v) 114 | 115 | --- 116 | 117 | ### **Step 2: Deep Learning for Vision** 118 | 119 | **Why Learn Deep Learning?** 120 | - Deep learning powers state-of-the-art vision research. 121 | 122 | **What to Learn?** 123 | - CNN Architectures: ResNet, VGG, MobileNet. 124 | - Specialized Models: YOLO, Faster R-CNN (object detection), U-Net (segmentation). 125 | - GANs for Vision: StyleGAN, CycleGAN. 126 | 127 | **Resources** 128 | - [Deep Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdFUCFcUp-7VD4bLXr50hgb) 129 | - [Differential Calculus](https://youtube.com/playlist?list=PLKdU0fuY4OFdKwJ4EBxqk2DfePO6KM2GL&si=LczAiWR0HtcneSYu) 130 | - [Video Tutorials](https://www.youtube.com/playlist?list=PLKdU0fuY4OFfjjFpSfCYzvsMKhZIWL43v) 131 | - [Deep Learning for Computer Vision](https://aiquest.org/courses/deep-learning-and-generative-ai/) 132 | 133 | --- 134 | 135 | ### **Step 3: Research & Paper Writing** 136 | 137 | **What to Focus On?** 138 | - Comparing CNN architectures. 139 | - Evaluating object detection algorithms on standard datasets. 140 | - Publishing findings with visualization results. 141 | 142 | **Resources** 143 | - [COCO Dataset](https://cocodataset.org/) 144 | - [LaTeX for Paper Writing](https://www.overleaf.com/) 145 | 146 | --- 147 | 148 | ## **Domain: 03. NLP & Sentiment Analysis** 149 | 150 | ### **What is NLP?** 151 | - A branch of AI that focuses on understanding and generating human language. 152 | 153 | ### **Responsibilities in Research** 154 | - Study language models for tasks like sentiment analysis and NER. 155 | - Evaluate model performance on NLP benchmarks. 156 | - Publish findings related to language understanding. 157 | 158 | --- 159 | 160 | ### **Step 1: Basics of NLP** 161 | 162 | **Why Learn NLP Basics?** 163 | - Foundational concepts help in preprocessing and understanding text data. 164 | 165 | **What to Learn?** 166 | - Tokenization, Stemming, Lemmatization. 167 | - Word Embeddings: Word2Vec, GloVe, FastText. 168 | - Advanced Concepts: TF-IDF, NER. 169 | 170 | **Resources** 171 | - [Natural Language Toolkit (NLTK)](https://www.nltk.org/) 172 | - [Hugging Face Tutorials](https://huggingface.co/transformers/) 173 | - [Module - NLP](https://aiquest.org/courses/data-science-machine-learning/) 174 | 175 | --- 176 | 177 | ### **Step 2: Deep Learning for NLP** 178 | 179 | **Why Learn Deep Learning?** 180 | - Powers advanced models for tasks like sentiment analysis and text generation. 181 | 182 | **What to Learn?** 183 | - RNN, LSTM, GRU for Sequence models 184 | - Transformer Architectures: BERT, GPT. 185 | - Sequence-to-Sequence Models: Attention Mechanisms, Seq2Seq. 186 | 187 | **Resources** 188 | - [Deep Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdFUCFcUp-7VD4bLXr50hgb) 189 | - [Differential Calculus](https://youtube.com/playlist?list=PLKdU0fuY4OFdKwJ4EBxqk2DfePO6KM2GL&si=LczAiWR0HtcneSYu) 190 | - [Hugging Face Tutorials](https://huggingface.co/transformers/) 191 | 192 | --- 193 | 194 | ### **Step 3: Research & Paper Writing** 195 | 196 | **What to Focus On?** 197 | - Evaluating sentiment analysis models. 198 | - Analyzing pre-trained models for domain-specific tasks. 199 | - Writing papers highlighting advancements in NLP. 200 | 201 | **Resources** 202 | - [Kaggle Datasets](https://www.kaggle.com/datasets) 203 | - [Hugging Face Datasets](https://huggingface.co/datasets) 204 | 205 | --- 206 | 207 | ## **Domain: 04. Time Series Analysis** 208 | 209 | ### **What is Time Series Analysis?** 210 | - Focuses on analyzing data indexed in time order for forecasting and pattern detection. 211 | 212 | ### **Responsibilities in Research** 213 | - Experiment with time series forecasting models. 214 | - Evaluate model performance on historical datasets. 215 | - Publish findings in journals or conferences. 216 | 217 | --- 218 | 219 | ### **Step 1: Basics of Time Series Analysis** 220 | 221 | **What to Learn?** 222 | - Concepts: Stationarity, Seasonality, Trends. 223 | - Techniques: ACF, PACF, Moving Averages. 224 | 225 | **Resources** 226 | - [Module - Time Series](https://aiquest.org/courses/data-science-machine-learning/) 227 | 228 | --- 229 | 230 | ### **Step 2: Deep Learning for Time Series** 231 | 232 | **What to Learn?** 233 | - LSTMs and GRUs for sequence modeling. 234 | - ARIMA, SARIMA for classical forecasting. 235 | 236 | **Resources** 237 | - [Deep Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdFUCFcUp-7VD4bLXr50hgb) 238 | - [Module - Time Series](https://aiquest.org/courses/data-science-machine-learning/) 239 | 240 | --- 241 | 242 | ## **Domain: 05. Generative AI with Pretrained Models** 243 | 244 | ### **What is Generative AI?** 245 | - Focuses on generating text, images, and other content using advanced models. 246 | 247 | ### **Responsibilities in Research** 248 | - Fine-tune pre-trained models for creative applications. 249 | - Evaluate performance on generative benchmarks. 250 | 251 | --- 252 | 253 | ### **Step 1: Generative Models** 254 | 255 | **What to Learn?** 256 | - GANs: StyleGAN, DCGAN. 257 | - Transformers: GPT, Stable Diffusion. 258 | 259 | **Resources** 260 | - [Generative AI Tutorials](https://huggingface.co/models) 261 | - [Generative AI Course](https://aiquest.org/courses/deep-learning-and-generative-ai/) 262 | 263 | --- 264 | 265 | # **Final Note: Workflow (Important)** 266 | 1. Understand domain-specific prerequisites. 267 | 2. Apply ML/DL techniques to datasets. 268 | 3. Evaluate models and publish findings. 269 | 270 | --- 271 | 272 | ## Note (Warning!): 273 | I am not a professional researcher but have a working knowledge of Machine Learning. While I can guide you on relevant topics and concepts in ML for your chosen domain, I may not be able to assist with the detailed aspects of research methodologies or paper writing. For deeper guidance, consider collaborating with experienced researchers or referring to academic resources. 😊 274 | 275 | --- 276 | # Recomended Courses at aiQuest Intelligence 277 | 1. [Basic to Advanced Python](https://aiquest.org/courses/become-a-python-developer/) 278 | 2. [Statistical Machine Learning](https://aiquest.org/courses/data-science-machine-learning/) 279 | 3. [Advanced Deep Learning & Generative AI](https://aiquest.org/courses/deep-learning-and-generative-ai/) 280 | 281 | *`Note:`* We suggest these premium courses because they are well-organized for absolute beginners and will guide you step by step, from basic to advanced levels. Always remember that `T-shaped skills` are better than `i-shaped skill`. However, for those who cannot afford these courses, don't worry! Search on YouTube using the topic names mentioned in the roadmap. You will find plenty of `free tutorials` that are also great for learning. Best of luck! 282 | 283 | --- 284 | 285 | ## About the Author 286 | **Rashedul Alam Shakil** 287 | - 🌐 [LinkedIn Profile](https://www.linkedin.com/in/kmrashedulalam/) 288 | - 🎓 Industry Expert | Educator 289 | 290 | --- 291 | 292 | ## Other Roadmaps 293 | - [Explore More Roadmaps](https://github.com/rashakil-ds/Roadmap-Docs) 294 | 295 | -------------------------------------------------------------------------------- /Data Engineer/README.md: -------------------------------------------------------------------------------- 1 | # Big Data Engineer Roadmap 2025/26 2 | 3 | - Job Category: `Entry level` 4 | 5 | ## **Understand the Role of a Data Engineer** 6 | 7 | - **What is a Data Engineer?** 8 | - A professional responsible for building, managing, and optimizing data pipelines `for` analytics and machine learning systems. 9 | - **Key Responsibilities:** 10 | - Design and maintain scalable data architecture. 11 | - Build and manage ETL processes. 12 | - Ensure data quality, reliability, and security. 13 | - **Why Data Engineering?** 14 | - Increasing demand for data-driven decision-making across industries. 15 | - Critical for enabling advanced analytics and AI systems. 16 | 17 | #### **Resources:** 18 | - [Watch Tutorials](https://www.youtube.com/playlist?list=PLKdU0fuY4OFeZwPhMLQ-1njPnsCGB42is) 19 | 20 | --- 21 | 22 | ### **Step 1: Learn Programming for Data Engineering** 23 | 24 | #### **Why?** 25 | Programming is essential for automating data workflows, building pipelines, and integrating tools. 26 | 27 | #### **What to Learn?** 28 | - Python Basics: 29 | - Syntax, variables, loops, and conditionals. 30 | - Data structures: Lists, tuples, dictionaries, sets. 31 | - Libraries: 32 | - **NumPy**: Numerical computing. 33 | - **Pandas**: Data manipulation and cleaning. 34 | - **Polars**: High-performance DataFrames. 35 | - Working with SQL Databases: 36 | - Connect and query databases using `SQLAlchemy` or `psycopg2`. 37 | 38 | #### **Resources:** 39 | - [Official Python Docs](https://docs.python.org/3/tutorial/index.html) 40 | - [Python Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFf7qj4eoBtvALAB_Ml2rN0V) 41 | - [Pandas Tutorials](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdsmcM817qp1L3ngU5amkak) 42 | - [Module - Python for Data Engineering](https://aiquest.org/courses/become-a-big-data-engineer/) 43 | - [Become a Python Developer](https://aiquest.org/courses/become-a-python-developer/) 44 | 45 | --- 46 | ### 2nd Language: Scala for Data Engineers (Optional) 47 | 48 | Scala is an optional but valuable skill for data engineers working with distributed data systems like Apache Spark. Its concise syntax and compatibility with the JVM ecosystem make it a preferred choice for high-performance data engineering tasks. 49 | 50 | --- 51 | 52 | #### **Why Learn Scala?** 53 | 54 | - **Native Language for Apache Spark:** Scala is the original language of Apache Spark, offering better performance and compatibility. 55 | - **Functional and Object-Oriented Paradigm:** Combines functional programming features with object-oriented principles for concise and robust code. 56 | - **JVM Compatibility:** Integrates seamlessly with Java libraries and tools. 57 | 58 | --- 59 | 60 | #### **Topics to Learn** 61 | 62 | #### **1. Scala Basics** 63 | - Overview of Scala and its use in data engineering. 64 | - Setting up the Scala environment. 65 | - Syntax and structure: Variables, Data Types, and Control Flow. 66 | 67 | #### **2. Functional Programming in Scala** 68 | - Higher-order functions. 69 | - Immutability and working with immutable data. 70 | - Closures, Currying, and Partially Applied Functions. 71 | 72 | #### **3. Working with Collections** 73 | - Lists, Sets, Maps, and Tuples. 74 | - Transformation operations: `map`, `flatMap`, `filter`. 75 | - Reductions and Aggregations: `reduce`, `fold`, `aggregate`. 76 | 77 | #### **4. Concurrency in Scala** 78 | - Futures and Promises. 79 | - Introduction to Akka for building distributed systems. 80 | 81 | #### **5. Apache Spark with Scala** 82 | - Setting up Spark with Scala. 83 | - Working with RDDs, DataFrames, and Datasets. 84 | - Writing Spark jobs in Scala. 85 | 86 | #### **6. Advanced Topics** 87 | - Pattern Matching and Case Classes. 88 | - Traits and Abstract Classes. 89 | - Type System and Generics. 90 | 91 | ### **Resources** 92 | 93 | #### Online Tutorials 94 | - [Scala Official Documentation](https://docs.scala-lang.org/) 95 | - [Scala for the Impatient (Book)](https://www.amazon.com/Scala-Impatient-Cay-S-Horstmann/dp/0134540565) 96 | 97 | #### Spark Integration 98 | - [Apache Spark with Scala Documentation](https://spark.apache.org/docs/latest/api/scala/index.html) 99 | - [Databricks Scala Tutorials](https://www.databricks.com/) 100 | 101 | --- 102 | ### **Step 2: Master SQL for Data Engineering** 103 | 104 | #### **Why?** 105 | SQL is critical for querying and managing relational databases efficiently. 106 | 107 | #### **What to Learn?** 108 | - Basics: SELECT, INSERT, UPDATE, DELETE. 109 | - Intermediate: Joins (INNER, OUTER), subqueries. 110 | - Advanced: Window functions, CTEs, query optimization. 111 | 112 | #### **Hands-On Tools:** 113 | - PostgreSQL, MySQL Workbench. 114 | 115 | #### **Resources:** 116 | - [SQL Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFduhpa23Wy5fRv6SGxp2ho0) 117 | - [Programming with Mosh - SQL Playlist](https://youtu.be/7S_tz1z_5bA) 118 | - [Module - SQL for Data Engineers](https://aiquest.org/courses/become-a-big-data-engineer/) 119 | - Practice SQL on platforms like [LeetCode](https://leetcode.com/) or [HackerRank](https://www.hackerrank.com/). 120 | 121 | --- 122 | 123 | ### **Step 3: Understand Data Warehousing and ETL Processes** 124 | 125 | #### **Why?** 126 | Data warehousing is vital for storing and analyzing structured data at scale. 127 | 128 | #### **What to Learn?** 129 | - Data Warehousing: 130 | - Concepts: OLAP vs. OLTP. 131 | - Schemas: Star and Snowflake. 132 | - Fact and Dimension Tables. 133 | - ETL vs. ELT: 134 | - Extract, Transform, and Load processes. 135 | - Tools: Apache Airflow, Talend. 136 | 137 | #### **Resources:** 138 | - [Data Warehousing & ETL](https://aiquest.org/courses/become-a-big-data-engineer/) 139 | - [YouTube - Data Warehouse](https://www.youtube.com/playlist?list=PLxCzCOWd7aiHexyDzYgry0YZN29e7HdNB) 140 | 141 | --- 142 | 143 | ### **Step 4: Workflow Orchestration with Apache Airflow** 144 | 145 | #### **Why?** 146 | Automates data workflows and ensures scalability of pipelines. 147 | 148 | #### **What to Learn?** 149 | - Directed Acyclic Graphs (DAGs) for task scheduling. 150 | - Task dependencies, operators, monitoring pipelines. 151 | - Automating ETL workflows. 152 | 153 | #### **Resources:** 154 | - [Apache Airflow Documentation](https://airflow.apache.org/) 155 | - [Module - Workflow Orchestration Tool - Apache Airflow](https://aiquest.org/courses/become-a-big-data-engineer/) 156 | 157 | --- 158 | 159 | ### **Step 5: Big Data Technologies** 160 | 161 | #### **Why?** 162 | It is essential for processing and analyzing large datasets effectively. 163 | 164 | #### **What to Learn?** 165 | - Hadoop Ecosystem: 166 | - HDFS (distributed storage). 167 | - MapReduce (data processing). 168 | - Apache Spark: 169 | - Spark with Python (PySpark). 170 | - Databricks: 171 | - Delta Lake, data versioning. 172 | 173 | #### **Resources:** 174 | - [Module - Big Data Technologies](https://aiquest.org/courses/become-a-big-data-engineer/) 175 | - [Apache Spark Documentation](https://spark.apache.org/) 176 | - [PySpark](https://youtu.be/XGrKYz_aapA?list=PLKdU0fuY4OFeaY8dMKkxmhNDyijI-0H5L) 177 | 178 | --- 179 | 180 | ### **Step 6: Explore NoSQL Databases** 181 | 182 | #### **Why?** 183 | To handle unstructured and semi-structured data effectively, especially when relational databases aren't the best fit. 184 | 185 | #### **What to Learn?** 186 | - **Basics of NoSQL:** 187 | - Understand the types of NoSQL databases: Key-value, Document-based, Column-family, and Graph databases. 188 | - Learn their use cases and differences from relational databases. 189 | - **MongoDB:** 190 | - CRUD operations (Create, Read, Update, Delete). 191 | - Query operators and expressions. 192 | - Aggregation pipelines for data processing. 193 | - Document-oriented data model: Collections and Documents. 194 | 195 | #### **Hands-On Tools:** 196 | - MongoDB for document-based NoSQL. 197 | - DynamoDB for key-value stores (AWS). 198 | 199 | #### **Resources:** 200 | - [MongoDB University](https://university.mongodb.com/) 201 | - [MongoDB Documentation](https://www.mongodb.com/docs/) 202 | - [Module - NoSQL](https://aiquest.org/courses/become-a-big-data-engineer/) 203 | 204 | --- 205 | 206 | ### **Step 7: Cloud Platforms and BigQuery** 207 | 208 | #### **Why?** 209 | Cloud platforms are widely used for data storage, processing, and analytics. 210 | 211 | #### **What to Learn?** 212 | - Cloud Computing Basics: 213 | - Types of clouds: Public, private, hybrid. 214 | - Google BigQuery: 215 | - Querying and analyzing datasets. 216 | - Integrating BigQuery with other tools. 217 | 218 | #### **Resources:** 219 | - [Module - GCP & Google BigQuery](https://aiquest.org/courses/become-a-big-data-engineer/) 220 | - [BigQuery Tutorials](https://www.youtube.com/playlist?list=PLIivdWyY5sqLAbIdmcMwsxWg-w8Px34MS) 221 | - [Google BigQuery Documentation](https://cloud.google.com/bigquery) 222 | 223 | --- 224 | 225 | ### **Step 8: Capstone Project** 226 | 227 | #### **Why?** 228 | Hands-on experience with end-to-end data engineering workflows. 229 | 230 | #### **Project Scope:** 231 | - Extract data from a public API. 232 | - Preprocess and clean the data using Python. 233 | - Load data into a warehouse (BigQuery). 234 | - Schedule workflows using Apache Airflow. 235 | 236 | --- 237 | 238 | ## **Final Workflow Integration** 239 | 1. Use **SQL** for data extraction. 240 | 2. Preprocess and transform data using **Python**. 241 | 3. Store data in **data warehouses** or **NoSQL databases**. 242 | 4. Automate workflows with **Apache Airflow**. 243 | 5. Process large datasets with **big data tools** like Spark. 244 | 6. Visualize and analyze data for insights. 245 | 246 | --- 247 | 248 | # **Additional Skills Recommendations (Optional)** 249 | ## **1. Real-Time Data Processing with Apache Kafka** 250 | ### **Why Kafka?** 251 | - Many modern applications require real-time data streaming. 252 | - Enables real-time data ingestion, processing, and event-driven architectures. 253 | - Essential for applications like fraud detection, recommendation systems, and IoT analytics. 254 | ### **What to Learn?** 255 | - **Kafka Architecture** – Topics, partitions, brokers, producers, consumers. 256 | - **Kafka Streaming** – Stream processing with Kafka Streams and KSQL. 257 | - **Integration** – Kafka with Spark, Flink, and Data Lakes. 258 | --- 259 | 260 | ## **2. DataOps & DevOps for Data Pipelines with Terraform** 261 | ### **Why Terraform?** 262 | - Automates infrastructure provisioning and deployment of scalable data pipelines. 263 | - Ensures reliability, version control, and security in cloud environments. 264 | ### **What to Learn?** 265 | - **Infrastructure as Code (IaC)** – Automating cloud setup with Terraform. 266 | - **CI/CD Pipelines** – Automating data workflow deployments (GitHub Actions, Jenkins). 267 | - **Monitoring & Security** – Observability with Prometheus, Grafana, and cloud logging. 268 | --- 269 | 270 | By following this roadmap step-by-step, you’ll be well-prepared to excel as a **Data Engineer**. Let me know if you'd like further guidance on any step! Please write an email to me. 271 | 272 | [**Search Data Engineer Jobs**](https://www.google.com/search?q=remote+big+data+engineer+jobs+near+me) 273 | 274 | --- 275 | # Recomended Courses at aiQuest Intelligence 276 | 1. [Basic to Advanced Python](https://aiquest.org/courses/become-a-python-developer/) 277 | 2. [Become a Big Data Engineer](https://aiquest.org/courses/become-a-big-data-engineer/) 278 | 279 | *`Note:`* We suggest these premium courses because they are well-organized for absolute beginners and will guide you step by step, from basic to advanced levels. Always remember that `T-shaped skills` are better than `i-shaped skill`. However, for those who cannot afford these courses, don't worry! Search on YouTube using the topic names mentioned in the roadmap. You will find plenty of `free tutorials` that are also great for learning. Best of luck! 280 | 281 | ## About the Author 282 | **Rashedul Alam Shakil** 283 | - 🌐 [LinkedIn Profile](https://www.linkedin.com/in/kmrashedulalam/) 284 | - 🎓 Industry Expert | Educator 285 | 286 | # Other Roadmaps 287 | - [Read Now](https://github.com/rashakil-ds/Roadmap-Docs) 288 | 289 | -------------------------------------------------------------------------------- /Data Analyst/README.md: -------------------------------------------------------------------------------- 1 | # Data Analyst Roadmap 2025/26 2 | - Job Category: `Entry level` or `Mid Level` 3 | 4 | ## **Understand the Role of Data Analyst** 5 | 6 | ### **What does a Data Analyst do?** 7 | - Collect, clean, and analyze data to provide actionable insights. 8 | - Use tools to create reports, dashboards, and visualizations. 9 | - Collaborate with stakeholders to make data-driven decisions. 10 | 11 | ### **Responsibilities** 12 | - Data collection and preparation. 13 | - Data analysis and visualization. 14 | - Reporting and communication. 15 | 16 | [Must Watch This Video!](https://youtu.be/pR3Hi0lJSTo) 17 | 18 | ------------------------------------------------- 19 | 20 | ## **Step 01: Python & Python Libraries** 21 | 22 | ### **Why Learn Python?** 23 | - Python is the most versatile language for data analysis and manipulation. 24 | - Widely used in data analytics for automating tasks, manipulating data, and creating visualizations. 25 | - Also used for **Statistical Machine Learning** with the `sklearn`, `SciPy` and `statsmodels` library. 26 | 27 | ### **What to Learn?** 28 | - **Python Basics** 29 | - Variables, data types, loops, conditionals, and functions. 30 | - **Libraries** 31 | - **NumPy:** For numerical computations. 32 | - **Pandas:** For data manipulation (DataFrames, cleaning data, handling datasets). 33 | - **Matplotlib/Seaborn:** For creating visualizations. 34 | - **Plotly:** For interactive visualizations. 35 | - **Jupyter Notebooks:** These are used for organizing and presenting your code. 36 | 37 | ### **Resources** 38 | - [Official Docs](https://docs.python.org/3/tutorial/index.html) 39 | - [This Python Playlist is Sufficient for Data Analyst](https://www.youtube.com/playlist?list=PLKdU0fuY4OFf7qj4eoBtvALAB_Ml2rN0V) 40 | - [Pandas Tutorials](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdsmcM817qp1L3ngU5amkak) 41 | - Practice with Python datasets. 42 | 43 | ----------------------------------------------- 44 | 45 | ### **R Language (Optional)** 46 | 47 | #### **Why Learn R?** 48 | - Excellent for statistical computing, data visualization, and academic research. 49 | - Ideal for creating interactive dashboards with **R Shiny**. 50 | 51 | #### **What to Learn?** 52 | - **Basics:** Variables, data types, loops, functions. 53 | - **Data Manipulation:** `dplyr`, `tidyr`. 54 | - **Visualization:** `ggplot2`, `plotly`. 55 | - **Statistical Modeling:** Regression, hypothesis testing. 56 | - **Dashboards:** Build interactive apps with **R Shiny**. 57 | 58 | #### **Resources** 59 | - [Complete Applied Statistics for Data Scientists with R](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdcvSMgwilt99n81IhhaHSX) 60 | 61 | ----------------------------------------------- 62 | 63 | ## **Step 02: Statistics** 64 | 65 | ### **Why Learn Statistics?** 66 | - Provides the foundation for understanding data, patterns, and trends. 67 | - Core skills for hypothesis testing and decision-making. 68 | 69 | ### **What to Learn?** 70 | - Descriptive Statistics: Mean, median, mode, variance, standard deviation. 71 | - Inferential Statistics: Hypothesis testing, confidence intervals, t-tests. 72 | - Probability: Probability distributions, Bayes' theorem. 73 | - Data Distributions: Different kinds of distributions. 74 | - Correlation vs. Causation: Understand relationships between variables. 75 | 76 | ### **Resources** 77 | - [Statistics for Data Analytics, Data Science & AI](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdom1IbG2HDJdZtga5PFjOO). 78 | - [Complete Statistics for Data Science](https://www.youtube.com/playlist?list=PLKdU0fuY4OFeZCSF6aax3Uvwl6nWLIdSs) 79 | - [Statistics Module of Data Analysis Specialization](https://aiquest.org/courses/data-analysis-specialization/) 80 | - Practice using Python (NumPy and SciPy) or Excel. 81 | 82 | ------------------------------------------------------- 83 | 84 | ## **Step 03: Excel** 85 | 86 | ### **Why Learn Excel?** 87 | - A widely used tool for quick data analysis and reporting. 88 | - Versatile for creating dashboards and handling structured data. 89 | 90 | ### **What to Learn?** 91 | - Basics: Data cleaning, sorting, and filtering. 92 | - Advanced: Pivot tables, VLOOKUP, HLOOKUP, and conditional formatting. 93 | - Formulas: SUM, AVERAGE, IF, COUNTIF, etc. 94 | - Data Analysis ToolPak: Regression, histograms, and other statistical tools. 95 | 96 | ### **Resources** 97 | - [Microsoft Excel for Data Analysis](https://www.youtube.com/watch?v=OOWAk2aLEfk) 98 | - [Excel Module of Data Analysis Specialization](https://aiquest.org/courses/data-analysis-specialization/) 99 | - Practice creating small dashboards and reports. 100 | 101 | 102 | ------------------------------------------------ 103 | 104 | ## **Step 04: Power BI** 105 | 106 | ### **Why Learn Power BI?** 107 | - A powerful business intelligence tool for creating dynamic dashboards and reports. 108 | - Helps in visualizing and sharing insights interactively. 109 | 110 | ### **What to Learn?** 111 | - Importing data from multiple sources. 112 | - Building and customizing dashboards. 113 | - Creating calculated columns and measures (using DAX). 114 | - Designing interactive reports with filters and slicers. 115 | 116 | ### **Resources** 117 | - [Power BI's tutorials](https://www.youtube.com/playlist?list=PLUaB-1hjhk8HqnmK0gQhfmIdCbxwoAoys). 118 | - [PowerBi Module of Data Analysis Specialization](https://aiquest.org/courses/data-analysis-specialization/) 119 | - Practice by replicating real-world dashboards using sample datasets. 120 | 121 | ------------------------------------------ 122 | 123 | ## **Step 05: Tableau / Data Studio** 124 | 125 | ### **Why Learn Tableau or Data Studio?** 126 | - Both are user-friendly visualization tools widely used for creating impactful reports. 127 | - Google Data Studio integrates well with Google’s ecosystem (Sheets, BigQuery). 128 | 129 | ### **What to Learn?** 130 | - Tableau: Data blending, creating charts, and dashboards. 131 | - Data Studio: Connecting Google Sheets, customizing reports, and sharing insights. 132 | - Advanced: Adding calculated fields, and creating interactive visuals. 133 | 134 | ### **Resources** 135 | - [Tableau](https://youtu.be/K3pXnbniUcM). 136 | - [Google Data Studio](https://www.youtube.com/playlist?list=PL6_D9USWkG1BDcnnbw4gfolY0zms3svSi). 137 | - [Tableau/Looker Studio Module of Data Analysis Specialization](https://aiquest.org/courses/data-analysis-specialization/) 138 | 139 | --------------------------------------------- 140 | 141 | ## **Step 06: Statistical Machine Learning for Advanced Analytics (Recommended)** 142 | 143 | ### **Why Learn Statistical Machine Learning?** 144 | - To understand predictive analytics, regression, and time series forecasting. 145 | - Provides a statistical foundation for machine learning models. 146 | 147 | ### **What to Learn?** 148 | - **Regression Analysis**: 149 | - Linear, Non-Linear, and Polynomial Regression. 150 | - Multivariate Regression techniques. 151 | - **Time Series Analysis**: 152 | - Forecasting trends and seasonality. 153 | - ARIMA models and other time series techniques. 154 | - **Classification**: 155 | - Logistic Regression. 156 | - Decision Tree, Random Forest, SVM, etc. 157 | 158 | ### **Resources** 159 | - [Statistical Machine Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFfWY36nDJDlI26jXwInSm8f) 160 | - Practice with Python's `sklearn` and `statsmodels` libraries. 161 | 162 | ----------------------------------------------- 163 | ## **Step 07: SQL** 164 | 165 | ### **Why Learn SQL?** 166 | - Essential for querying and extracting data from databases. 167 | - Most companies store their data in relational databases like MySQL, PostgreSQL, or SQL Server. 168 | 169 | ### **What to Learn?** 170 | - Basics: SELECT, INSERT, UPDATE, DELETE. 171 | - Intermediate: Joins (INNER, LEFT, RIGHT, FULL), subqueries. 172 | - Advanced: Window functions, CTEs (Common Table Expressions), and optimizing queries. 173 | 174 | ### **Resources** 175 | - [SQL Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFduhpa23Wy5fRv6SGxp2ho0) 176 | - [Programming with Mosh - SQL Playlist](https://youtu.be/7S_tz1z_5bA) 177 | - [SQL Module of Data Analysis Specialization](https://aiquest.org/courses/data-analysis-specialization/) 178 | - Tools like MySQL Workbench, SQLite, or PostgreSQL. 179 | 180 | ----------------------------------------------------------- 181 | 182 | ## **Step 08: Projects** 183 | 184 | ### **Why Work on Projects?** 185 | - Practical experience to apply all the skills learned. 186 | - Helps in building a strong portfolio for job applications. 187 | 188 | ### **Ideas for Projects** 189 | 1. **Python:** 190 | - Analyze sales data to find trends and patterns. 191 | - Clean and visualize COVID-19 datasets. 192 | 2. **SQL:** 193 | - Create queries to analyze e-commerce sales or HR databases. 194 | 3. **Excel:** 195 | - Build a sales or financial dashboard. 196 | 4. **Power BI/Tableau:** 197 | - Design an interactive dashboard for customer segmentation or marketing performance. 198 | 5. **End-to-End Project:** 199 | - Use Python for data cleaning, SQL for data extraction, and Power BI/Tableau for visualization. 200 | 201 | ## More Project Ideas for Data Analyst/BI Analyst 202 | 1. **Sales Trend Analysis**: Use **SQL** and **Tableau**/**PowerBi** to analyze sales trends and visualize top-performing products. 203 | 2. **Customer Segmentation**: Perform clustering with **Python (Pandas, Scikit-learn)** and visualize results in **Tableau**/**PowerBi**. 204 | 3. **Website Traffic Analysis**: Analyze Google Analytics data with **Excel** and create dashboards in **Looker Studio**/**Tableau**/**PowerBi**. 205 | 4. **Retail Inventory Optimization**: Use **R** for inventory analysis and visualization to improve stock management. 206 | 5. **Marketing Campaign Analysis**: Evaluate campaign performance using **Tableau**/**PowerBi** and statistical analysis in **Excel**. 207 | 6. **HR Attrition Dashboard**: Build an interactive dashboard using **Power BI** with data insights from **SQL**. 208 | 7. **Financial Statement Analysis**: Perform financial ratio analysis in **Excel** and automate reporting with **Python**. 209 | 8. **Logistics and Supply Chain Analysis**: Use **Tableau**/**PowerBi** to visualize supply chain delays and **SQL** for querying data. 210 | 9. **Customer Support Ticket Analysis**: Analyze customer support trends with **Python (Pandas, Matplotlib)** and create KPIs in **Power BI**. 211 | 10. **Stock Market Trend Analysis**: Perform stock trend analysis using **Python (yFinance, Matplotlib)** and dashboards in **Tableau**. 212 | 213 | ### **Where to Find Data?** 214 | - [Kaggle](https://www.kaggle.com/datasets) 215 | - [UCI Machine Learning Repository](https://archive.ics.uci.edu/datasets) 216 | - [Data.gov](https://catalog.data.gov/dataset/) 217 | - [World Bank](https://data.worldbank.org/) 218 | - [GitHub Repo](https://github.com/rashakil-ds/Public-Datasets) 219 | 220 | --------------------- 221 | 222 | ## **Final Note: Workflow Integration** 223 | 1. Extract data using **SQL**. 224 | 2. Clean and manipulate it using **Python** or **Excel**. 225 | 3. Analyze the data using **Statistics**. 226 | 4. Visualize insights with **Power BI**, **Tableau**, or **Data Studio**. 227 | 5. Showcase results in a project or portfolio. 228 | 229 | By following this roadmap step-by-step, you’ll gain the skills needed to succeed as a `Data Analyst` or `BI Analyst`. Let me know if you’d like additional resources or specific examples! Just write an `email` to me. 230 | 231 | --- 232 | # Recomended Courses at aiQuest Intelligence 233 | 2. [SQL, Statistics & Data Analysis Tools](https://aiquest.org/courses/data-analysis-specialization/) 234 | 3. [Basic to Advanced Python](https://aiquest.org/courses/become-a-python-developer/) 235 | 4. [Machine Learning & Data Science Core Concepts](https://aiquest.org/courses/data-science-machine-learning/) 236 | 237 | *`Note:`* We suggest these premium courses because they are well-organized for absolute beginners and will guide you step by step, from basic to advanced levels. Always remember that `T-shaped skills` are better than `i-shaped skill`. However, for those who cannot afford these courses, don't worry! Search on YouTube using the topic names mentioned in the roadmap. You will find plenty of `free tutorials` that are also great for learning. Best of luck! 238 | 239 | ## About the Author 240 | **Rashedul Alam Shakil** 241 | - 🌐 [LinkedIn Profile](https://www.linkedin.com/in/kmrashedulalam/) 242 | - 🎓 Industry Expert | Educator 243 | 244 | # Other Roadmaps 245 | - [Read Now](https://github.com/rashakil-ds/Roadmap-Docs) 246 | 247 | -------------------------------------------------------------------------------- /Data Scientist/README.md: -------------------------------------------------------------------------------- 1 | # Data Scientist Roadmap 2025/26 2 | 3 | ## **Understand the Role of Data Scientist** 4 | - `Data Scientist` ≈ `Data Analysis Skills` + `Machine Learning & AI Knowledge` 😊 Yes, It's True! 5 | 6 | ### **What does a Data Scientist do?** 7 | - Collect, clean, analyze, and interpret large datasets to provide actionable insights. 8 | - Build predictive models and machine learning algorithms. 9 | - Communicate results to stakeholders using visualizations and storytelling. 10 | - Collaborate with cross-functional teams to solve business problems using data. 11 | 12 | ### **Responsibilities** 13 | - Data collection, cleaning, and preparation. 14 | - Statistical analysis and predictive modeling. 15 | - Machine learning model development and evaluation. 16 | - Communicating insights through dashboards and visualizations. 17 | 18 | ----------------------------------------------- 19 | 20 | ## Step 1: Maths, Statistics and Probability 21 | 22 | ### Why Learn Math? 23 | - Builds problem-solving and analytical thinking skills. 24 | - Forms the foundation for ML algorithms, models, and data analysis. 25 | - Essential for understanding functions, optimization, and quantitative reasoning in data science and machine learning. 26 | 27 | ### Why Learn Statistics and Probability? 28 | - Understand data, patterns, and trends. 29 | - Essential for hypothesis testing, distributions, and inference. 30 | 31 | ### What to Learn? 32 | - **Descriptive Statistics**: Mean, median, mode, variance, standard deviation, percentiles. 33 | - **Inferential Statistics**: Hypothesis testing, confidence intervals, t-tests, z-tests, ANOVA. 34 | - **Probability**: Basics, conditional probability, Bayes' theorem. 35 | - **Distributions**: Normal, binomial, Poisson, uniform, exponential. 36 | - **Correlation vs. Causation** 37 | - **Regression**: Linear, multiple, logistic. 38 | - **Calculus**: Partial Derivatives, integrals, applications in optimization and area. 39 | - **Matrix Algebra**: Operations, inverses, eigenvalues. 40 | - **Combinatorics**: Permutations, combinations. 41 | - **Sampling**: Random, stratified, sample size, bias. 42 | - **Central Limit Theorem** 43 | - **Law of Large Numbers** 44 | - **Random Variables**: Discrete, continuous, expected value. 45 | - **Markov Chains** Basics. 46 | 47 | 48 | ### **Resources** 49 | - [A-Z Linear Algebra & Calculus for AI & Data Science](https://www.youtube.com/playlist?list=PLKdU0fuY4OFct6HdBIszzy-jZlicLlkIw) 50 | - [Complete Applied Statistics for Data Scientists with R](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdcvSMgwilt99n81IhhaHSX) 51 | - Practice using Python (`NumPy`, `SciPy`, and `statsmodels`). 52 | ------------------------------------------------- 53 | 54 | ## **Step 2: Python & Python Libraries** 55 | 56 | ### **Why Learn Python?** 57 | - Python is the go-to programming language for data science due to its simplicity and robust libraries. 58 | - Used for data cleaning, manipulation, and building machine learning models. 59 | - Supports deep learning frameworks and statistical analysis. 60 | 61 | ### **What to Learn?** 62 | - **Python Basics** 63 | - Variables, data types, loops, conditionals, functions, and OOPs. 64 | - **Libraries** 65 | - **NumPy:** For numerical computations. 66 | - **Pandas & Polars:** For data manipulation (DataFrames, cleaning data, handling datasets). 67 | - **Matplotlib/Seaborn:** For creating visualizations. 68 | 69 | ### **Resources** 70 | - [Official Python Docs](https://docs.python.org/3/tutorial/index.html) 71 | - [Python Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFf7qj4eoBtvALAB_Ml2rN0V) 72 | - [For Basic to Advanced Python](https://aiquest.org/courses/become-a-python-developer/) 73 | - [Notes/Books](https://github.com/rashakil-ds/Top-Data-Science-AI-Book-Collection) 74 | - [Pandas Tutorials](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdsmcM817qp1L3ngU5amkak) 75 | - Practice with Python datasets on Kaggle or public repositories. 76 | 77 | ----------------------------------------------- 78 | ### **R Language (Optional)** 79 | 80 | #### **Why Learn R?** 81 | - Excellent for statistical computing, data visualization, and academic research. 82 | - Ideal for creating interactive dashboards with **R Shiny**. 83 | 84 | #### **What to Learn?** 85 | - **Basics:** Variables, data types, loops, functions. 86 | - **Data Manipulation:** `dplyr`, `tidyr`. 87 | - **Visualization:** `ggplot2`, `plotly`. 88 | - **Statistical Modeling:** Regression, hypothesis testing. 89 | - **Dashboards:** Build interactive apps with **R Shiny**. 90 | 91 | #### **Resources** 92 | - [Complete Applied Statistics for Data Scientists with R](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdcvSMgwilt99n81IhhaHSX) 93 | 94 | ------------------------------------------------------- 95 | 96 | ## **Step 3: Machine Learning** 97 | 98 | ### **Why Learn Machine Learning?** 99 | - Key for building predictive models and solving real-world problems. 100 | - Forms the foundation of advanced AI and data science applications. 101 | 102 | ### **What to Learn?** 103 | - Supervised Learning: 104 | - Linear Regression, Logistic Regression, Polynomial Regression, Lasso, Ridge. 105 | - KNN, Decision Trees, Naive Bayes, Support Vector Machines (SVM), Random Forest, LDA, Extra Trees Classifier, LightGBM. 106 | - Norm, Loss & Cost function, R Squared, Confusion Matrix. 107 | - Unsupervised Learning: 108 | - Clustering (K-means, DBSCAN). 109 | - Dimensionality Reduction (PCA, t-SNE). 110 | - Time Series Analysis 111 | - Forecasting trends and seasonality. 112 | - ARIMA models and other time series techniques. 113 | - Advanced Concepts: 114 | - Ensemble methods (Boosting, Bagging). 115 | - Regularizations 116 | - Data Sampling Methods 117 | - Gradient Descents 118 | - Hyperparameter tuning 119 | - Cross-validation. 120 | 121 | ### **Resources** 122 | - [Scikit-learn (`sklearn`):](https://scikit-learn.org/stable/) For statistical machine learning models. 123 | - [Statsmodels:](https://www.statsmodels.org/stable/index.html) For statistical analysis. 124 | - [SciPy:](https://docs.scipy.org/doc/scipy/reference/index.html) For statistical analysis. 125 | - [Machine Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFfWY36nDJDlI26jXwInSm8f) 126 | - [Machine Learning Module](https://aiquest.org/courses/data-science-machine-learning/) 127 | - Practice using Python's `sklearn` and Kaggle competitions. 128 | 129 | --------------------------------------------- 130 | 131 | ## **Step 4: Deep Learning (Intermediate)** 132 | 133 | ### **Why Learn Deep Learning?** 134 | - For advanced AI applications such as image recognition, NLP, and generative models. 135 | - Used in tasks that require neural networks for high-dimensional data. 136 | 137 | ### **What to Learn?** 138 | - Basics: Neural Networks (Perceptron, Feedforward, Backpropagation). 139 | - Architectures: ANN (for normal tasks), CNNs (for images), RNNs (for sequences), Transformers (for NLP tasks). 140 | - Transfer Learning, Fine-tuning 141 | - GD Optimizers, Regularization and Generalization 142 | - Frameworks: TensorFlow, Keras, PyTorch. 143 | 144 | ### **Resources** 145 | - [TensorFlow Library:](https://www.tensorflow.org/tutorials) For deep learning & AI. 146 | - [PyTorch Library:](https://pytorch.org/tutorials/beginner/basics/intro.html) For deep learning & AI. 147 | - [Deep Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdFUCFcUp-7VD4bLXr50hgb) 148 | - [Another DL Playlist](https://www.youtube.com/playlist?list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO) 149 | - [Another DL Playlist](https://www.youtube.com/playlist?list=PLZoTAELRMXVPGU70ZGsckrMdr0FteeRUi) 150 | - [Gradient Descent](https://www.youtube.com/playlist?list=PLKdU0fuY4OFe7mmYIb6NYCPRbljkE6is8) 151 | - [Basic to Advanced Deep Learning](https://aiquest.org/courses/deep-learning-and-generative-ai/) 152 | 153 | --------------------------------------------- 154 | 155 | ## **Step 5: Data Visualization & BI Tools** 156 | 157 | ### **Why Learn Visualization Tools?** 158 | - For communicating insights effectively to stakeholders. 159 | - A critical skill for storytelling with data. 160 | 161 | ### **What to Learn?** 162 | - Tools: Power BI, Tableau, Matplotlib, Seaborn. 163 | - Skills: 164 | - Creating dashboards. 165 | - Customizing reports and interactive visuals. 166 | 167 | ### **Resources** 168 | - [Power BI Tutorials](https://www.youtube.com/playlist?list=PLUaB-1hjhk8HqnmK0gQhfmIdCbxwoAoys) 169 | - [PowerBi, Tableau/Looker Studio Module](https://aiquest.org/courses/data-analysis-specialization/) 170 | 171 | ----------------------------------------------- 172 | ### **Step 6: Learn GitHub** 173 | - GitHub is a crucial platform for version control and collaboration. 174 | - Enables you to showcase your projects and build a portfolio. 175 | - Facilitates teamwork on data science projects. 176 | 177 | ### **What to Learn?** 178 | - **Git Basics:** 179 | - Version control concepts, repositories, branches, commits, pull requests. 180 | - **GitHub Skills:** 181 | - Hosting projects, collaboration workflows, managing issues. 182 | - **Best Practices:** 183 | - Writing READMEs, structuring repositories, using `.gitignore` files. 184 | 185 | ### **Resources** 186 | - [Complete GitHub for Data Scientists](https://www.youtube.com/playlist?list=PLKdU0fuY4OFcK__Q5tjqZY5mSx_u7ghUx) 187 | - Use GitHub to practice hosting Python, SQL, and machine learning projects. 188 | 189 | --- 190 | ## **Step 7: SQL** 191 | 192 | ### **Why Learn SQL?** 193 | - Essential for querying, extracting, and joining data from relational databases. 194 | - Used to preprocess and prepare data before modeling. 195 | 196 | ### **What to Learn?** 197 | - Basics: SELECT, INSERT, UPDATE, DELETE. 198 | - Intermediate: Joins (INNER, LEFT, RIGHT, FULL), subqueries. 199 | - Advanced: Window functions, CTEs (Common Table Expressions), and query optimization. 200 | 201 | ### **Resources** 202 | - [SQL Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFduhpa23Wy5fRv6SGxp2ho0) 203 | - [Programming with Mosh - SQL Playlist](https://youtu.be/7S_tz1z_5bA) 204 | - [SQL Module of Data Analysis Specialization](https://aiquest.org/courses/data-analysis-specialization/) 205 | - Tools like MySQL Workbench, SQLite, or PostgreSQL. 206 | 207 | --------------------------------------------- 208 | 209 | ## **Step 8: Projects** 210 | 211 | ### **Why Work on Projects?** 212 | - Hands-on experience to apply all the skills learned. 213 | - Builds a strong portfolio showcasing `data science` expertise. 214 | 215 | ### **Ideas for Projects** 216 | 1. **Python & Machine Learning:** 217 | - Build a predictive model for house prices. 218 | - Build a predictive model for stock prices 219 | - Create a recommendation system. 220 | 2. **Deep Learning:** 221 | - Train a CNN for image classification. 222 | - Build an NLP model for sentiment analysis. 223 | - Design a Chatbot using Generative AI 224 | 3. **SQL:** 225 | - Analyze and clean a retail dataset. 226 | 4. **Visualization:** 227 | - Design an interactive dashboard using `PowerBi` for e-commerce KPIs. 228 | 229 | ### **Where to Find Data?** 230 | - [Kaggle](https://www.kaggle.com/datasets) 231 | - [UCI Machine Learning Repository](https://archive.ics.uci.edu/datasets) 232 | - [Data.gov](https://catalog.data.gov/dataset/) 233 | - [World Bank](https://data.worldbank.org/) 234 | 235 | ------------------------------------------------------------- 236 | 237 | ## **Final Note: Workflow Integration** 238 | 1. Extract data using **SQL**. 239 | 2. Preprocess and clean data using **Python** and **Pandas**. 240 | 3. Analyze the data using **Statistics** and **Machine Learning**. 241 | 4. Visualize insights with **Power BI**, **Tableau**, or **Matplotlib**. 242 | 5. Deploy `ML models` using [Flask](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdPhZ4k_hu7Rv3sjyi3IPtu) / FastAPI or `PowerBi` dashboards for business use. 243 | 244 | Following this roadmap step-by-step will give you the skills needed to succeed as a `Data Scientist`. Let me know if you’d like additional resources or specific examples! Just write an `email` to me. 245 | 246 | This repository is continually updated based on the top `job postings` on **LinkedIn** and **Indeed**. Please pray for me and my family—your prayers are all I ask as a token of appreciation. 247 | 248 | [**Search Data Scientist Jobs**](https://www.google.com/search?q=remote+data+scientist+jobs+near+me) 249 | 250 | --- 251 | # Recomended Courses at aiQuest Intelligence 252 | 1. [Basic to Advanced Python](https://aiquest.org/courses/become-a-python-developer/) 253 | 2. [A-Z Linear Algebra & Calculus for AI & Data Science](https://www.youtube.com/playlist?list=PLKdU0fuY4OFct6HdBIszzy-jZlicLlkIw) 254 | 3. [Machine Learning & Deep Learning Core Concepts](https://aiquest.org/courses/data-science-machine-learning/) 255 | 4. [Advanced Deep Learning & Generative AI](https://aiquest.org/courses/deep-learning-and-generative-ai/) 256 | 5. [SQL & Data Analysis Tools](https://aiquest.org/courses/data-analysis-specialization/) 257 | 258 | *`Note:`* We suggest these premium courses because they are well-organized for absolute beginners and will guide you step by step, from basic to advanced levels. Always remember that `T-shaped skills` are better than `i-shaped skill`. However, for those who cannot afford these courses, don't worry! Search on YouTube using the topic names mentioned in the roadmap. You will find plenty of `free tutorials` that are also great for learning. Best of luck! 259 | 260 | # About the Author 261 | **Rashedul Alam Shakil** 262 | - 🌐 [LinkedIn Profile](https://www.linkedin.com/in/kmrashedulalam/) 263 | - 🎓 Industry Expert | Educator 264 | 265 | # Other Roadmaps 266 | - [Read Now](https://github.com/rashakil-ds/Roadmap-Docs) 267 | 268 | -------------------------------------------------------------------------------- /Generative AI Engineer/README.md: -------------------------------------------------------------------------------- 1 | 2 | # Generative AI (Gen AI) Engineer Roadmap 2025/26 3 | 4 | - Job Category: `Very High` 5 | - Best for `Software Engineers` and `Machine Learning Experts`. 6 | 7 | --- 8 | 9 | ## **Understand the Role of a Generative AI Engineer** 10 | - `Gen AI Engineer` ≈ `NLP and Computer Vision Skills` + `Generative Models Expertise` + `Deployment and Optimization` + `GenAI Applications` 😊 11 | 12 | ### **What does a Generative AI Engineer do?** 13 | - Develop, fine-tune, and deploy `generative` models (GPT, DALL-E, Stable Diffusion). 14 | - Work on innovative AI systems for content creation, personalization, and automation. 15 | - Collaborate with data scientists, ML engineers, and domain experts. 16 | - Ensure generative AI systems' performance, reliability, and ethical compliance. 17 | 18 | ### **Responsibilities** 19 | - Designing and deploying state-of-the-art generative models. 20 | - Fine-tuning pre-trained models for specific applications. 21 | - Creating scalable pipelines for generative AI workflows. 22 | - Exploring ethical implications and bias mitigation for AI systems. 23 | 24 | ----------------------------------------------- 25 | 26 | ## **Step 01: Mathematics for Generative AI** 27 | 28 | ### **Why Learn Mathematics?** 29 | - Provides the theoretical foundation for generative models and algorithms. 30 | 31 | ### **What to Learn?** 32 | - **Linear Algebra:** 33 | - Matrices, vectors, eigenvalues, eigenvectors. 34 | - **Probability and Statistics:** 35 | - Probability distributions, Bayesian inference, hypothesis testing. 36 | - **Optimization:** 37 | - Partial derivatives, Gradient descent, convex optimization, and backpropagation. 38 | 39 | ### **Resources** 40 | - [A-Z Linear Algebra & Calculus for AI & Data Science](https://www.youtube.com/playlist?list=PLKdU0fuY4OFct6HdBIszzy-jZlicLlkIw) 41 | - [Gradient Descent](https://www.youtube.com/playlist?list=PLKdU0fuY4OFe7mmYIb6NYCPRbljkE6is8) 42 | 43 | --- 44 | 45 | ## **Step 02: Programming Fundamentals** 46 | 47 | ### **Why Learn Programming?** 48 | - Gen AI Engineers require strong programming skills to build and deploy advanced models. 49 | 50 | ### **What to Learn?** 51 | - **Python Basics:** 52 | - Variables, data types, loops, conditionals, functions, error handling, debugging, and OOPs. 53 | - **Libraries:** 54 | - **NumPy:** Numerical computations. 55 | - **Pandas:** Data manipulation and cleaning. 56 | - **Matplotlib/Seaborn:** Data visualization. 57 | 58 | --- 59 | ### **Resources** 60 | - [Official Python Docs](https://docs.python.org/3/tutorial/index.html) 61 | - [Python Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFf7qj4eoBtvALAB_Ml2rN0V) 62 | - [Learn Pandas](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdsmcM817qp1L3ngU5amkak) 63 | - [Python for AI Engineers](https://aiquest.org/courses/become-a-python-developer/) 64 | 65 | --- 66 | 67 | ## **Step 03: Foundations of Machine Learning** 68 | 69 | ### **Why Learn Machine Learning?** 70 | - Generative AI builds on the principles of ML for training and optimizing models. 71 | 72 | ### **What to Learn?** 73 | - **Supervised Learning:** 74 | - Linear, Logistic Regression, Boosting. 75 | - **Unsupervised Learning:** 76 | - Clustering (K-means), Dimensionality Reduction (PCA). 77 | - **Model Optimization:** 78 | - Regularization, Cross-validation, Gradient Descent Variants. 79 | 80 | ### **Resources** 81 | - [Sklearn Docs](https://scikit-learn.org/stable/) 82 | - [Machine Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFfWY36nDJDlI26jXwInSm8f) 83 | - [Machine Learning Module](https://aiquest.org/courses/data-science-machine-learning/) 84 | 85 | --- 86 | 87 | ## **Step 04: Core Deep Learning & Neural Networks** 88 | 89 | ### **Why Learn Deep Learning?** 90 | - Deep learning underpins the majority of generative AI models. 91 | 92 | ### **What to Learn?** 93 | - **Basics:** 94 | - Neural Networks, Backpropagation, Activation Functions. 95 | - **Architectures:** 96 | - CNNs (images), RNNs (sequences), Transformers (NLP). 97 | - **Advanced Topics:** 98 | - Attention Mechanisms, Transfer Learning. 99 | - **Frameworks:** TensorFlow, PyTorch. 100 | 101 | ### **Resources** 102 | - [TensorFlow Library:](https://www.tensorflow.org/tutorials) For deep learning & AI. 103 | - [PyTorch Library:](https://pytorch.org/tutorials/beginner/basics/intro.html) For deep learning & AI. 104 | - [Deep Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdFUCFcUp-7VD4bLXr50hgb) 105 | - [Another DL Playlist](https://www.youtube.com/playlist?list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO) 106 | - [Another DL Playlist](https://www.youtube.com/playlist?list=PLZoTAELRMXVPGU70ZGsckrMdr0FteeRUi) 107 | - [Gradient Descent](https://www.youtube.com/playlist?list=PLKdU0fuY4OFe7mmYIb6NYCPRbljkE6is8) 108 | - [Basic to Advanced Deep Learning](https://aiquest.org/courses/deep-learning-and-generative-ai/) 109 | 110 | --- 111 | 112 | ## **Step 05: Natural Language Processing (NLP)** 113 | 114 | ### **Why Learn NLP?** 115 | - Generative models like GPT excel in language-related tasks. 116 | 117 | ### **What to Learn?** 118 | - Tokenization, Stemming, Lemmatization. 119 | - Word Embeddings (Word2Vec, GloVe, FastText). 120 | - Advanced Models (BERT, GPT, T5). 121 | - Fine-tuning and Prompt Engineering for LLMs. 122 | 123 | ### **Resources** 124 | - [Hugging Face Tutorials](https://huggingface.co/transformers/) 125 | - [NLP with Python](https://www.nltk.org/) 126 | 127 | --- 128 | 129 | ## **Step 06: Generative AI with AI Agent** 130 | ### Why Learn Generative AI with Agents? 131 | - Generative AI powers state-of-the-art applications like chatbots, image generation, summarization, and code generation. 132 | - Agentic AI systems (e.g., AutoGPT, BabyAGI) take GenAI to the next level by **autonomously planning, reasoning, and executing tasks**. 133 | - Enables developers to build intelligent systems that interact with users, APIs, and environments **independently**. 134 | 135 | --- 136 | 137 | ### What to Learn? 138 | #### 1. **LLMs (Large Language Models) Fundamentals** 139 | - Understanding architecture: Transformer, Attention Mechanism 140 | - Pretraining vs Fine-tuning vs Prompt Engineering 141 | - Tokens, Embeddings, and Context Window 142 | - Popular Models: GPT-4, LLaMA, Mistral, Claude, Gemini 143 | 144 | #### 2. **Prompt Engineering** 145 | - Zero-shot, Few-shot, and Chain-of-Thought prompting 146 | - System vs User prompts 147 | - Prompt tuning and injection techniques 148 | 149 | #### 3. **LangChain Framework** 150 | - Chains: Sequential, Conditional, and Custom Chains 151 | - Tools and Agents 152 | - Memory and Retrieval-Augmented Generation (RAG) 153 | - Integration with APIs, Databases, and Filesystems 154 | 155 | #### 4. **Vector Databases** 156 | - FAISS, ChromaDB, Pinecone, Weaviate 157 | - Embedding models (OpenAI, Hugging Face, Sentence Transformers) 158 | - Indexing, similarity search, and metadata filtering 159 | 160 | #### 5. **Agentic AI Concepts** 161 | - Planning, Tool-Usage, Task Decomposition 162 | - Tools: AutoGPT, BabyAGI, LangGraph 163 | - Building multi-step autonomous agents with LangChain Agents or OpenAI Function Calling 164 | 165 | --- 166 | 167 | ### Tools & Libraries 168 | 169 | - [`LangChain`](https://github.com/langchain-ai/langchain) – Framework for building LLM-powered apps 170 | - [`Transformers`](https://github.com/huggingface/transformers) – State-of-the-art models from Hugging Face 171 | - [`FAISS`](https://github.com/facebookresearch/faiss) – Vector similarity search 172 | - [`Chroma`](https://www.trychroma.com/) – Lightweight local vector DB 173 | - [`OpenAI API`](https://platform.openai.com/) – GPT-4/3.5 access 174 | 175 | --- 176 | 177 | ### Resources 178 | 179 | - [Agentic AI with LLMs](https://aiquest.org/agentic-ai-with-llms/) 180 | - [LangChain Docs](https://python.langchain.com/docs/introduction/) 181 | - [Hugging Face Transformers Course](https://huggingface.co/learn) 182 | - [OpenAI Cookbook](https://github.com/openai/openai-cookbook) 183 | - [LangChain YouTube Tutorials](https://www.youtube.com/@LangChainAI) 184 | - [Awesome-LLM GitHub Repo](https://github.com/Hannibal046/Awesome-LLM) 185 | 186 | ### YouTube Channel 187 | - [Andrej Karpathy](https://www.youtube.com/@AndrejKarpathy) 188 | - [Sebastian Raschka](https://youtube.com/@sebastianraschka) 189 | - [Study Mart](https://www.youtube.com/playlist?list=PLKdU0fuY4OFeORaFJlz7D4XHzMupso9a9) 190 | --- 191 | 192 | ### Project Ideas 193 | 194 | 1. **Conversational Chatbot Agent** 195 | - Use LangChain + OpenAI + Vector DB 196 | - Enable document querying via conversational interface 197 | 198 | 2. **Autonomous Research Agent** 199 | - AutoGPT-style system that takes a goal and performs research via web/API 200 | 201 | 3. **AI Email Assistant** 202 | - Summarize, respond, or schedule meetings automatically using LLM + calendar/email 203 | 204 | --- 205 | 206 | ## **Step 07: Reinforcement Learning (Optional)** 207 | 208 | ### **Why Learn Reinforcement Learning?** 209 | - RL can enhance generative models by optimizing sequential decision-making. 210 | 211 | ### **What to Learn?** 212 | 1. Markov Decision Processes (MDPs). 213 | 2. Deep Reinforcement Learning (DQN, PPO, A3C). 214 | 215 | ### **Resources** 216 | - [Spinning Up in Deep RL](https://spinningup.openai.com/) 217 | - [Reinforcement Learning Playlist](https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u) 218 | 219 | --- 220 | ### **Step 08: Learn GitHub** 221 | - GitHub is a crucial platform for version control and collaboration. 222 | - Enables you to showcase your projects and build a portfolio. 223 | - Facilitates teamwork on data science projects. 224 | 225 | ### **What to Learn?** 226 | - **Git Basics:** 227 | - Version control concepts, repositories, branches, commits, pull requests. 228 | - **GitHub Skills:** 229 | - Hosting projects, collaboration workflows, managing issues. 230 | - **Best Practices:** 231 | - Writing READMEs, structuring repositories, using `.gitignore` files. 232 | 233 | ### **Resources** 234 | - [Complete GitHub for GenAI Engineers](https://www.youtube.com/playlist?list=PLKdU0fuY4OFcK__Q5tjqZY5mSx_u7ghUx) 235 | - Use GitHub to practice hosting Python, SQL, and machine learning projects. 236 | 237 | --- 238 | 239 | ## **Step 09: SQL** 240 | 241 | ### **Why Learn SQL?** 242 | - Essential for querying, extracting, and joining data from relational databases. 243 | - Used to preprocess and prepare data before modeling. 244 | 245 | ### **What to Learn?** 246 | - Basics: SELECT, INSERT, UPDATE, DELETE. 247 | - Intermediate: Joins (INNER, LEFT, RIGHT, FULL), subqueries. 248 | - Advanced: Window functions, CTEs (Common Table Expressions), and query optimization. 249 | 250 | ### **Resources** 251 | - [SQL Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFduhpa23Wy5fRv6SGxp2ho0) 252 | - [Programming with Mosh - SQL Playlist](https://youtu.be/7S_tz1z_5bA) 253 | - Tools like MySQL Workbench, SQLite, or PostgreSQL. 254 | 255 | --- 256 | 257 | ## **Step 10: MLOps and Model Deployment** 258 | 259 | ### **Why Learn MLOps?** 260 | - To ensure generative AI models are production-ready and maintainable. 261 | 262 | ### **What to Learn?** 263 | - **Tools and Frameworks:** 264 | - MLflow, DVC, Kubeflow. 265 | - **Deployment:** 266 | - Flask, FastAPI, TensorFlow Serving, TorchServe. 267 | - **Cloud Platforms:** 268 | - AWS SageMaker, Google Vertex AI, Azure ML. 269 | - **Monitoring and Retraining:** 270 | - Drift detection, feedback loops, CI/CD pipelines. 271 | 272 | ### **Resources** 273 | - [Deploying AI Systems](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdPhZ4k_hu7Rv3sjyi3IPtu) 274 | - [MLOps on AWS](https://www.youtube.com/playlist?list=PLmPJQXJiMoUWFW2JxRSAfhcsQ0Cr9qbv-) 275 | 276 | --- 277 | 278 | ## **Step 11: Projects** 279 | 280 | ### **Why Work on Projects?** 281 | - Demonstrates your ability to apply generative AI to real-world problems. 282 | 283 | ### **Ideas for Projects** 284 | 1. **Text Generation:** Fine-tune GPT for domain-specific content creation. 285 | 2. **Image Generation:** Build a text-to-image system using Stable Diffusion. 286 | 3. **Audio Generation:** Create voice clones using generative models. 287 | 4. **AI Art:** Design AI tools for generative art. 288 | 5. **Custom Chatbots:** Build personalized conversational agents. 289 | 290 | ### **Where to Find Data?** 291 | - [Kaggle](https://www.kaggle.com/datasets) 292 | - [Hugging Face Datasets](https://huggingface.co/datasets) 293 | 294 | --- 295 | 296 | ## **Final Note: Workflow Integration** 297 | 1. Preprocess and analyze data using Python and libraries like Pandas. 298 | 2. Fine-tune generative models with domain-specific datasets. 299 | 3. Deploy generative AI models using scalable MLOps frameworks. 300 | 4. Monitor performance and iterate for continuous improvement. 301 | 302 | Following this roadmap step-by-step will give you the skills needed to succeed as a **Generative AI Engineer**. Let me know if you’d like additional resources or specific examples! Feel free to reach out. 303 | 304 | [**Search Generative AI Jobs**](https://www.google.com/search?q=remote+generative+ai+jobs+near+me) 305 | 306 | ----------------------------------------------- 307 | ### Addition Language | Java | C++ (Optional): 308 | ### **Why Learn Java?** 309 | - Widely used in enterprise-level AI systems and backend development. 310 | - Excellent for scalable and production-ready AI applications. 311 | - Integrates seamlessly with big data frameworks and distributed systems. 312 | - Supports libraries like **Deeplearning4j** for deep learning and **Weka** for machine learning. 313 | - Ideal for building AI-powered APIs and enterprise solutions. 314 | - Plays a critical role in big data processing with frameworks like **Apache Spark**. 315 | 316 | ### **Why Learn C++?** 317 | - Known for its speed and efficiency, making it ideal for real-time AI applications. 318 | - Widely used in robotics, computer vision, and gaming AI. 319 | - Provides granular control over memory and system resources. 320 | - Supports libraries like **OpenCV** for computer vision and **TensorFlow** C++ API for backend optimizations. 321 | - Essential for robotics and autonomous systems development using **ROS**. 322 | - Excels in creating performance-critical, large-scale AI systems. 323 | 324 | #### Resources (Optional) 325 | - [Complete Java and DSA](https://www.youtube.com/playlist?list=PLfqMhTWNBTe3LtFWcvwpqTkUSlB32kJop) 326 | - [Complete c++ and DSA](https://www.youtube.com/playlist?list=PLfqMhTWNBTe137I_EPQd34TsgV6IO55pt) 327 | 328 | --- 329 | 330 | # Recommended Courses at aiQuest Intelligence 331 | 1. [Basic to Advanced Python](https://aiquest.org/courses/become-a-python-developer/) 332 | 2. [Agentic AI with LLMs](https://aiquest.org/agentic-ai-with-llms/) 333 | 334 | *`Note:`* We suggest these premium courses because they are well-organized for absolute beginners and will guide you step by step, from basic to advanced levels. Always remember that `T-shaped skills` are better than `i-shaped skill`. However, for those who cannot afford these courses, don't worry! Search on YouTube using the topic names mentioned in the roadmap. You will find plenty of `free tutorials` that are also great for learning. Best of luck! 335 | 336 | ## About the Author 337 | **Rashedul Alam Shakil** 338 | - 🌐 [LinkedIn Profile](https://www.linkedin.com/in/kmrashedulalam/) 339 | - 🎓 Industry Expert | Educator 340 | 341 | --- 342 | 343 | ## Other Roadmaps 344 | - [Read Roadmaps](https://github.com/rashakil-ds/Roadmap-Docs) 345 | -------------------------------------------------------------------------------- /Machine Learning Engineer/README.md: -------------------------------------------------------------------------------- 1 | # Machine Learning Engineer Roadmap 2025/26 2 | - Job Category: `High` 3 | - Best for `Software Engineers`. 4 | ## **Understand the Role of Machine Learning Engineer** 5 | - `Machine Learning Engineer` ≈ `Software Engineering Expertise` + `Scalability and Deployment Skills` + `Advanced Machine Learning` 6 | 7 | - [Must Watch This Video!](https://www.youtube.com/watch?v=0I0eRhg9ReE) 8 | 9 | ### **What does a Machine Learning Engineer do?** 10 | - Design, build, and deploy machine learning systems. 11 | - Work on scalable solutions for data preprocessing, model training, and inference. 12 | - Collaborate with data scientists, software engineers, and DevOps teams. 13 | - Optimize machine learning models for performance and scalability. 14 | 15 | ### **Responsibilities** 16 | - Developing and fine-tuning machine learning models. 17 | - Building pipelines for data processing and feature engineering. 18 | - Deploying, monitoring, and maintaining models in production. 19 | - Ensuring the scalability, performance, and reliability of ML systems. 20 | 21 | --- 22 | 23 | ## **Step 01: Mathematics for Machine Learning** 24 | 25 | ### **Why Learn Mathematics for Machine Learning?** 26 | - Provides the theoretical foundation for machine learning algorithms. 27 | - Helps in understanding how models learn and make predictions. 28 | - Optimization of ML models 29 | 30 | ### **What to Learn?** 31 | - **Linear Algebra** 32 | - Matrices, vectors, eigenvalues, eigenvectors. 33 | - **Calculus** 34 | - Differentiation and integration for optimization. 35 | - **Probability and Statistics** 36 | - Probability distributions, Bayes' theorem, hypothesis testing. 37 | - **Optimization** 38 | - Gradient descent, convex and non-convex optimization. 39 | 40 | ### **Resources** 41 | - [A-Z Linear Algebra & Calculus for AI & Data Science](https://www.youtube.com/playlist?list=PLKdU0fuY4OFct6HdBIszzy-jZlicLlkIw) 42 | - [Gradient Descent](https://www.youtube.com/playlist?list=PLKdU0fuY4OFe7mmYIb6NYCPRbljkE6is8) 43 | 44 | --- 45 | 46 | ## **Step 02: Python & Python Libraries** 47 | 48 | ### **Why Learn Python?** 49 | - Python is the primary programming language for machine learning and AI. 50 | - Supports libraries and frameworks for ML, deep learning, and data processing. 51 | 52 | ### **What to Learn?** 53 | - **Python Basics** 54 | - Variables, data types, loops, conditionals, functions, error handling, debugging, and OOPs. 55 | - **Libraries** 56 | - **NumPy:** For numerical computations. 57 | - **Pandas & Polars:** For data manipulation (DataFrames, cleaning data, handling datasets). 58 | - **Matplotlib/Seaborn:** For creating visualizations. 59 | 60 | ### **Resources** 61 | - [Official Python Docs](https://docs.python.org/3/tutorial/index.html) 62 | - [Python Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFf7qj4eoBtvALAB_Ml2rN0V) 63 | - [For Basic to Advanced Python](https://aiquest.org/courses/become-a-python-developer/) 64 | - [Learn Pandas](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdsmcM817qp1L3ngU5amkak) 65 | - Practice with Python datasets on Kaggle or public repositories. 66 | 67 | --- 68 | 69 | ## **Step 03: Statistical Machine Learning** 70 | 71 | ### **Why Learn Machine Learning?** 72 | The core responsibility of an ML engineer is building, optimizing, and scaling ML models. 73 | 74 | ### **What to Learn?** 75 | - **Statistics** 76 | - Correlation, Data Distributions, Hypothesis Testing 77 | - **Supervised Learning:** 78 | - Linear, Polynomial, Lasso, Ridge, Logistic Regression, Decision Trees, Random Forest. 79 | - Gradient Boosting (XGBoost, LightGBM, CatBoost). 80 | - **Unsupervised Learning:** 81 | - Clustering (K-means, DBSCAN). 82 | - Dimensionality Reduction (PCA, t-SNE). 83 | - **Advanced Techniques:** 84 | - Ensemble Methods, Regularization, Hyperparameter Tuning. 85 | - **Model Optimization:** 86 | - Gradient Descent Variants. 87 | - Cross-validation and Bias-Variance Tradeoff. 88 | 89 | ### **Resources** 90 | - [Machine Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFfWY36nDJDlI26jXwInSm8f) 91 | - [Machine Learning Module](https://aiquest.org/courses/data-science-machine-learning/) 92 | - [Scikit-learn (`sklearn`):](https://scikit-learn.org/stable/) For statistical machine learning models. 93 | - [Statsmodels:](https://www.statsmodels.org/stable/index.html) For statistical analysis. 94 | - [SciPy:](https://docs.scipy.org/doc/scipy/reference/index.html) For statistical analysis. 95 | - Practice using Python's `sklearn` and Kaggle competitions. 96 | 97 | --- 98 | 99 | ## **Step 04: Deep Learning** 100 | 101 | ### **Why Learn Deep Learning?** 102 | - Powers advanced AI applications such as image recognition, NLP, and generative models. 103 | 104 | ### **What to Learn?** 105 | - **Basics:** Neural Networks (Perceptron, Activation functions, Feedforward, Backpropagation). 106 | - **Architectures:** CNNs (images), RNNs (sequences), Transformers (NLP tasks). 107 | - **Advanced Topics:** 108 | - YOLO, VAEs, GANs, GPTs, T5. 109 | - Transfer Learning, Fine-tuning. 110 | - Optimizers, Regularization. 111 | - Attention Mechanisms. 112 | - **Frameworks:** TensorFlow, Keras, PyTorch. 113 | 114 | ### **Resources** 115 | - [TensorFlow Library:](https://www.tensorflow.org/tutorials) For deep learning & AI. 116 | - [PyTorch Library:](https://pytorch.org/tutorials/beginner/basics/intro.html) For deep learning & AI. 117 | - [Deep Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdFUCFcUp-7VD4bLXr50hgb) 118 | - [Another DL Playlist](https://www.youtube.com/playlist?list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO) 119 | - [Another DL Playlist](https://www.youtube.com/playlist?list=PLZoTAELRMXVPGU70ZGsckrMdr0FteeRUi) 120 | - [Basic to Advanced Deep Learning](https://aiquest.org/courses/deep-learning-and-generative-ai/) 121 | 122 | --- 123 | ## **Step 05: Generative AI with AI Agent** 124 | ### Why Learn Generative AI with Agents? 125 | - Generative AI powers state-of-the-art applications like chatbots, image generation, summarization, and code generation. 126 | - Agentic AI systems (e.g., AutoGPT, BabyAGI) take GenAI to the next level by **autonomously planning, reasoning, and executing tasks**. 127 | - Enables developers to build intelligent systems that interact with users, APIs, and environments **independently**. 128 | 129 | --- 130 | 131 | ### What to Learn? 132 | #### 1. **LLMs (Large Language Models) Fundamentals** 133 | - Understanding architecture: Transformer, Attention Mechanism 134 | - Pretraining vs Fine-tuning vs Prompt Engineering 135 | - Tokens, Embeddings, and Context Window 136 | - Popular Models: GPT-4, LLaMA, Mistral, Claude, Gemini 137 | 138 | #### 2. **Prompt Engineering** 139 | - Zero-shot, Few-shot, and Chain-of-Thought prompting 140 | - System vs User prompts 141 | - Prompt tuning and injection techniques 142 | 143 | #### 3. **LangChain Framework** 144 | - Chains: Sequential, Conditional, and Custom Chains 145 | - Tools and Agents 146 | - Memory and Retrieval-Augmented Generation (RAG) 147 | - Integration with APIs, Databases, and Filesystems 148 | 149 | #### 4. **Vector Databases** 150 | - FAISS, ChromaDB, Pinecone, Weaviate 151 | - Embedding models (OpenAI, Hugging Face, Sentence Transformers) 152 | - Indexing, similarity search, and metadata filtering 153 | 154 | #### 5. **Agentic AI Concepts** 155 | - Planning, Tool-Usage, Task Decomposition 156 | - Tools: AutoGPT, BabyAGI, LangGraph 157 | - Building multi-step autonomous agents with LangChain Agents or OpenAI Function Calling 158 | 159 | --- 160 | 161 | ### Tools & Libraries 162 | 163 | - [`LangChain`](https://github.com/langchain-ai/langchain) – Framework for building LLM-powered apps 164 | - [`Transformers`](https://github.com/huggingface/transformers) – State-of-the-art models from Hugging Face 165 | - [`FAISS`](https://github.com/facebookresearch/faiss) – Vector similarity search 166 | - [`Chroma`](https://www.trychroma.com/) – Lightweight local vector DB 167 | - [`OpenAI API`](https://platform.openai.com/) – GPT-4/3.5 access 168 | 169 | --- 170 | 171 | ### Resources 172 | 173 | - [Deep Learning & Generative AI](https://aiquest.org/courses/deep-learning-and-generative-ai/) 174 | - [LangChain Docs](https://python.langchain.com/docs/introduction/) 175 | - [Hugging Face Transformers Course](https://huggingface.co/learn) 176 | - [OpenAI Cookbook](https://github.com/openai/openai-cookbook) 177 | - [LangChain YouTube Tutorials](https://www.youtube.com/playlist?list=PLKnIA16_RmvaTbihpo4MtzVm4XOQa0ER0) 178 | - [Awesome-LLM GitHub Repo](https://github.com/Hannibal046/Awesome-LLM) 179 | 180 | ### YouTube Channel 181 | - [Andrej Karpathy](https://www.youtube.com/@AndrejKarpathy) 182 | - [Sebastian Raschka](https://youtube.com/@sebastianraschka) 183 | - [Study Mart](https://www.youtube.com/playlist?list=PLKdU0fuY4OFeORaFJlz7D4XHzMupso9a9) 184 | --- 185 | 186 | ### Project Ideas 187 | 188 | 1. **Conversational Chatbot Agent** 189 | - Use LangChain + OpenAI + Vector DB 190 | - Enable document querying via conversational interface 191 | 192 | 2. **Autonomous Research Agent** 193 | - AutoGPT-style system that takes a goal and performs research via web/API 194 | 195 | 3. **AI Email Assistant** 196 | - Summarize, respond, or schedule meetings automatically using LLM + calendar/email 197 | 198 | 199 | ## **Step 06: Learn GitHub** 200 | - GitHub is a crucial platform for version control and collaboration. 201 | - Enables you to showcase your projects and build a portfolio. 202 | - Facilitates teamwork on data science projects. 203 | 204 | ### **What to Learn?** 205 | - **Git Basics:** 206 | - Version control concepts, repositories, branches, commits, pull requests. 207 | - **GitHub Skills:** 208 | - Hosting projects, collaboration workflows, managing issues. 209 | - **Best Practices:** 210 | - Writing READMEs, structuring repositories, using `.gitignore` files. 211 | 212 | ### **Resources** 213 | - [Complete GitHub for Machine Learning Engineers](https://www.youtube.com/playlist?list=PLKdU0fuY4OFcK__Q5tjqZY5mSx_u7ghUx) 214 | - Use GitHub to practice hosting Python, SQL, and machine learning projects. 215 | 216 | --- 217 | 218 | ## **Step 07: SQL** 219 | 220 | ### **Why Learn SQL?** 221 | - Essential for querying, extracting, and joining data from relational databases. 222 | - Used to preprocess and prepare data before modeling. 223 | 224 | ### **What to Learn?** 225 | - Basics: SELECT, INSERT, UPDATE, DELETE. 226 | - Intermediate: Joins (INNER, LEFT, RIGHT, FULL), subqueries. 227 | - Advanced: Window functions, CTEs (Common Table Expressions), and query optimization. 228 | 229 | ### **Resources** 230 | - [SQL Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFduhpa23Wy5fRv6SGxp2ho0) 231 | - [Programming with Mosh - SQL Playlist](https://youtu.be/7S_tz1z_5bA) 232 | - Tools like MySQL Workbench, SQLite, or PostgreSQL. 233 | 234 | --- 235 | 236 | ## **Step 08: MLOps and Model Deployment** 237 | 238 | ### **Why Learn MLOps?** 239 | - Ensures models are production-ready, scalable, and maintainable. 240 | - Covers CI/CD pipelines, version control, and monitoring. 241 | 242 | ### **What to Learn?** 243 | - **Tools and Frameworks:** 244 | - MLflow, DVC, Kubeflow. 245 | - **Deployment:** 246 | - Flask, FastAPI, TensorFlow Serving, TorchServe. 247 | - **Cloud Platforms:** 248 | - AWS SageMaker, Google Vertex AI, Azure ML. 249 | - **Monitoring and Retraining:** 250 | - Drift detection, feedback loops, CI/CD pipelines. 251 | 252 | ### **Resources** 253 | - [Deploying ML Models Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdPhZ4k_hu7Rv3sjyi3IPtu) 254 | - [AWS SageMaker](https://www.youtube.com/playlist?list=PLmPJQXJiMoUWFW2JxRSAfhcsQ0Cr9qbv-) 255 | 256 | --- 257 | 258 | ## **Step 09: Scalable Data Processing** 259 | 260 | ### **Why Learn Scalable Data Processing?** 261 | - Essential for handling large datasets in production environments. 262 | 263 | ### **What to Learn?** 264 | - **Git & GitHub** 265 | - **Big Data Tools:** 266 | - Apache Spark, Hadoop. 267 | - **Distributed Computing:** 268 | - PySpark, Dask. 269 | - **Data Engineering Basics:** 270 | - ETL Pipelines, Data Lakes. 271 | 272 | ### **Resources** 273 | - [PySpark](https://youtu.be/XGrKYz_aapA?list=PLKdU0fuY4OFeaY8dMKkxmhNDyijI-0H5L) 274 | - [Everything that you need](https://aiquest.org/courses/become-a-big-data-engineer/) 275 | 276 | --- 277 | 278 | ## **Step 10: Projects** 279 | 280 | ### **Why Work on Projects?** 281 | - Demonstrates your ability to build and deploy end-to-end ML solutions. 282 | - Showcases your expertise in real-world scenarios. 283 | 284 | ### **Ideas for Projects** 285 | 1. **Machine Learning:** 286 | - Predict stock prices using regression models. 287 | - Fraud detection in financial transactions. 288 | - Recommendation systems 289 | 2. **Deep Learning:** 290 | - Build an image classification model using CNNs. 291 | - Create a sentiment analysis model with Transformers. 292 | - Object detection using YOLO/Faster R-CNN 293 | - Design a Chatbot using Generative AI 294 | 3. **MLOps:** 295 | - Deploy a machine learning model using Flask/FastAPI. 296 | - Build a CI/CD pipeline for model retraining. 297 | 298 | ### **Where to Find Data?** 299 | - [Kaggle](https://www.kaggle.com/datasets) 300 | - [UCI Machine Learning Repository](https://archive.ics.uci.edu/datasets) 301 | - [Data.gov](https://catalog.data.gov/dataset/) 302 | 303 | ------------------------------------------------------------- 304 | ## Additional Skills (Mandatory for GenAI-LLMs Domain) 305 | - [Hugging Face](https://huggingface.co/models) 306 | - [LangChain](https://python.langchain.com/docs/introduction/) 307 | - At least one `Vector Database` 308 | 309 | ------------------------------------------------------------- 310 | ## Additional Skills (Mandatory for Computer Vision Domain) 311 | - [Learn OpenCV](https://opencv.org/) 312 | 313 | ------------------------------------------------------------- 314 | ## **Final Note: Workflow Integration** 315 | 1. Extract data using **SQL** or data engineering tools. 316 | 2. Preprocess and clean data using **Python**. 317 | 3. Train and optimize ML models. 318 | 4. Deploy models using MLOps frameworks. 319 | 5. Monitor and maintain models in production. 320 | 321 | Following this roadmap, step-by-step will give you the skills needed to succeed as a `Machine Learning Engineer`. Let me know if you’d like additional resources or specific examples! 322 | 323 | [**Search Machine Learning Engineer Jobs**](https://www.google.com/search?q=remote+machine+learning+engineer+jobs+near+me) 324 | 325 | ----------------------------------------------- 326 | ### Addition Language | Java & C++ (Optional): 327 | ### **Why Learn Java?** 328 | - Widely used in enterprise-level AI systems and backend development. 329 | - Excellent for scalable and production-ready AI applications. 330 | - Integrates seamlessly with big data frameworks and distributed systems. 331 | - Supports libraries like **Deeplearning4j** for deep learning and **Weka** for machine learning. 332 | - Ideal for building AI-powered APIs and enterprise solutions. 333 | - Plays a critical role in big data processing with frameworks like **Apache Spark**. 334 | 335 | ### **Why Learn C++?** 336 | - Known for its speed and efficiency, making it ideal for real-time AI applications. 337 | - Widely used in robotics, computer vision, and gaming AI. 338 | - Provides granular control over memory and system resources. 339 | - Supports libraries like **OpenCV** for computer vision and **TensorFlow** C++ API for backend optimizations. 340 | - Essential for robotics and autonomous systems development using **ROS**. 341 | - Excels in creating performance-critical, large-scale AI systems. 342 | 343 | #### Resources (Optional) 344 | - [Complete Java and DSA](https://www.youtube.com/playlist?list=PLfqMhTWNBTe3LtFWcvwpqTkUSlB32kJop) 345 | - [Complete c++ and DSA](https://www.youtube.com/playlist?list=PLfqMhTWNBTe137I_EPQd34TsgV6IO55pt) 346 | 347 | --- 348 | # Recommended Courses at aiQuest Intelligence 349 | 1. [Basic to Advanced Python](https://aiquest.org/courses/become-a-python-developer/) 350 | 3. [Machine Learning & Deep Learning Core Concepts](https://aiquest.org/courses/data-science-machine-learning/) 351 | 4. [Advanced Deep Learning & Generative AI](https://aiquest.org/courses/deep-learning-and-generative-ai/) 352 | 5. [Cloud, Big Data Technologies & Tools](https://aiquest.org/courses/become-a-big-data-engineer/) 353 | 354 | *`Note:`* We suggest these premium courses because they are well-organized for absolute beginners and will guide you step by step, from basic to advanced levels. Always remember that `T-shaped skills` are better than `i-shaped skill`. However, for those who cannot afford these courses, don't worry! Search on YouTube using the topic names mentioned in the roadmap. You will find plenty of `free tutorials` that are also great for learning. Best of luck! 355 | 356 | ## About the Author 357 | **Rashedul Alam Shakil** 358 | - 🌐 [LinkedIn Profile](https://www.linkedin.com/in/kmrashedulalam/) 359 | - 🎓 Industry Expert | Educator 360 | 361 | # Other Roadmaps 362 | - [Read Roadmaps](https://github.com/rashakil-ds/Roadmap-Docs) 363 | -------------------------------------------------------------------------------- /AI Engineer/README.md: -------------------------------------------------------------------------------- 1 | # AI Engineer Roadmap 2025/26 2 | 3 | - Job Category: `High` 4 | - Best for `Software Engineers`, `Data Scientists`, and `Robotics Enthusiasts`. 5 | 6 | --- 7 | 8 | ## **Understand the Role of AI Engineer** 9 | - `AI Engineer` ≈ `Machine Learning Expertise` + `Broad AI Knowledge` + `Deployment and Scalability Skills` + `Interdisciplinary Collaboration` 😊 10 | - [Must Watch This Video!](https://www.youtube.com/watch?v=0I0eRhg9ReE) 11 | 12 | ### **What does an AI Engineer do?** 13 | - Develop end-to-end AI solutions combining ML, NLP, Computer Vision, and other technologies. 14 | - Work on designing, training, and deploying AI models at scale. 15 | - Collaborate with cross-functional teams (data engineers, data scientists, software engineers, robotics experts). 16 | - Optimize AI systems for performance, reliability, and ethical considerations. 17 | 18 | ### **Responsibilities** 19 | - Building and deploying advanced AI systems. 20 | - Integrating AI solutions into existing business workflows. 21 | - Researching and applying cutting-edge AI technologies. 22 | - Ensuring the scalability, reliability, and ethical compliance of AI systems. 23 | 24 | --- 25 | 26 | ## **Step 1: Programming Fundamentals (Python)** 27 | 28 | ### **Why Learn Python?** 29 | - AI Engineers need a strong programming foundation for implementing AI models and systems. 30 | 31 | ### **What to Learn?** 32 | - **Python Basics:** 33 | - Variables, data types, loops, conditionals, functions, error handling, debugging, and OOPs. 34 | - **Libraries:** 35 | - **NumPy:** Numerical computations. 36 | - **Pandas & Polars:** Data manipulation and cleaning. 37 | - **Matplotlib/Seaborn/Plotly:** Data visualization. 38 | 39 | ### **Resources** 40 | - [Official Python Docs](https://docs.python.org/3/tutorial/index.html) 41 | - [Python Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFf7qj4eoBtvALAB_Ml2rN0V) 42 | - [Learn Pandas](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdsmcM817qp1L3ngU5amkak) 43 | - [Basic to Advanced Python](https://aiquest.org/courses/become-a-python-developer/) 44 | 45 | ----------------------------------------------- 46 | 47 | ## **Step 2: Mathematics for AI** 48 | 49 | ### **Why Learn Mathematics for AI?** 50 | - Provides the foundation for understanding and implementing AI algorithms. 51 | 52 | ### **What to Learn?** 53 | - **Linear Algebra:** 54 | - Matrices, vectors, eigenvalues, eigenvectors. 55 | - **Calculus:** 56 | - Differentiation and integration for optimization. 57 | - **Probability and Statistics:** 58 | - Probability distributions, Bayes' theorem, hypothesis testing. 59 | - **Optimization:** 60 | - Gradient descent, convex and non-convex optimization. 61 | 62 | ### **Resources** 63 | - [A-Z Linear Algebra & Calculus for AI & Data Science](https://www.youtube.com/playlist?list=PLKdU0fuY4OFct6HdBIszzy-jZlicLlkIw) 64 | - [Gradient Descent](https://www.youtube.com/playlist?list=PLKdU0fuY4OFe7mmYIb6NYCPRbljkE6is8) 65 | --- 66 | 67 | ## **Step 3: Statistical Machine Learning** 68 | 69 | ### **Why Learn Machine Learning?** 70 | - AI Engineers build on ML techniques to create intelligent systems. 71 | 72 | ### **What to Learn?** 73 | - **Statistics** 74 | - Correlation, Data Distributions, Hypothesis Testing 75 | - **Supervised Learning:** 76 | - Linear, Polynomial, Logistic Regression. 77 | - Decision Trees, Random Forest, Gradient Boosting (XGBoost, LightGBM, CatBoost). 78 | - **Unsupervised Learning:** 79 | - Clustering (K-means, DBSCAN). 80 | - Dimensionality Reduction (PCA, t-SNE). 81 | - **Model Optimization:** 82 | - Cross-validation, Gradient Descent Variants. 83 | 84 | ### **Resources** 85 | - [Machine Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFfWY36nDJDlI26jXwInSm8f) 86 | - [Machine Learning Module](https://aiquest.org/courses/data-science-machine-learning/) 87 | - [Scikit-learn (`sklearn`):](https://scikit-learn.org/stable/) For statistical machine learning models. 88 | - [Statsmodels:](https://www.statsmodels.org/stable/index.html) For statistical analysis. 89 | - [SciPy:](https://docs.scipy.org/doc/scipy/reference/index.html) For statistical analysis. 90 | - Practice using Python's `sklearn` and Kaggle competitions. 91 | --- 92 | 93 | ## **Step 4: Deep Learning** 94 | 95 | ### **Why Learn Deep Learning?** 96 | - Fundamental for advanced AI applications like NLP, Computer Vision, and Generative AI. 97 | 98 | ### **What to Learn?** 99 | - **Basics:** 100 | - Neural Networks, Activation Functions, Backpropagation, Gradient Descent. 101 | - **Architectures:** 102 | - CNNs (images), RNNs (sequences), Transformers (NLP tasks). 103 | - **Advanced Topics:** 104 | - Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs). 105 | - Attention Mechanisms, Transfer Learning. 106 | - **Frameworks:** TensorFlow, PyTorch. 107 | 108 | ### **Resources** 109 | - [TensorFlow Library:](https://www.tensorflow.org/tutorials) For deep learning & AI. 110 | - [PyTorch Library:](https://pytorch.org/tutorials/beginner/basics/intro.html) For deep learning & AI. 111 | - [Deep Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdFUCFcUp-7VD4bLXr50hgb) 112 | - [Another DL Playlist](https://www.youtube.com/playlist?list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO) 113 | - [Another DL Playlist](https://www.youtube.com/playlist?list=PLZoTAELRMXVPGU70ZGsckrMdr0FteeRUi) 114 | - [Basic to Advanced Deep Learning](https://aiquest.org/courses/deep-learning-and-generative-ai/) 115 | 116 | --- 117 | 118 | ## **Step 5: Natural Language Processing (NLP)** 119 | 120 | ### **Why Learn NLP?** 121 | - Essential for tasks like language translation, sentiment analysis, and conversational AI. 122 | 123 | ### **What to Learn?** 124 | - Tokenization, Stemming, Lemmatization. 125 | - Word Embeddings (Word2Vec, GloVe). 126 | - Advanced Models (BERT, GPT, T5). 127 | - Fine-tuning Pretrained Models. 128 | 129 | ### **Resources** 130 | - [Hugging Face Tutorials](https://huggingface.co/transformers/) 131 | - [NLP with Python](https://www.nltk.org/) 132 | - [Generative AI Course](https://aiquest.org/courses/deep-learning-and-generative-ai/) 133 | 134 | --- 135 | 136 | ## **Step 6: Computer Vision** 137 | 138 | ### **Why Learn Computer Vision?** 139 | - Powers AI applications like object detection, facial recognition, and autonomous vehicles. 140 | 141 | ### **What to Learn?** 142 | - Basics of Image Processing (OpenCV). 143 | - CNNs and Advanced Architectures (YOLO, Faster R-CNN). 144 | - Generative Models for Vision (StyleGAN, BigGAN). 145 | 146 | ### **Resources** 147 | - [Learn OpenCV](https://opencv.org/) 148 | - [Deep Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdFUCFcUp-7VD4bLXr50hgb) 149 | - [Deep Learning and Generative AI](https://aiquest.org/courses/deep-learning-and-generative-ai/) 150 | 151 | --- 152 | 153 | ## **Step 07: Generative AI with AI Agent** 154 | ### Why Learn Generative AI with Agents? 155 | - Generative AI powers state-of-the-art applications like chatbots, image generation, summarization, and code generation. 156 | - Agentic AI systems (e.g., AutoGPT, BabyAGI) take GenAI to the next level by **autonomously planning, reasoning, and executing tasks**. 157 | - Enables developers to build intelligent systems that interact with users, APIs, and environments **independently**. 158 | 159 | --- 160 | 161 | ### What to Learn? 162 | #### 1. **LLMs (Large Language Models) Fundamentals** 163 | - Understanding architecture: Transformer, Attention Mechanism 164 | - Pretraining vs Fine-tuning vs Prompt Engineering 165 | - Tokens, Embeddings, and Context Window 166 | - Popular Models: GPT-4, LLaMA, Mistral, Claude, Gemini 167 | 168 | #### 2. **Prompt Engineering** 169 | - Zero-shot, Few-shot, and Chain-of-Thought prompting 170 | - System vs User prompts 171 | - Prompt tuning and injection techniques 172 | 173 | #### 3. **LangChain Framework** 174 | - Chains: Sequential, Conditional, and Custom Chains 175 | - Tools and Agents 176 | - Memory and Retrieval-Augmented Generation (RAG) 177 | - Integration with APIs, Databases, and Filesystems 178 | 179 | #### 4. **Vector Databases** 180 | - FAISS, ChromaDB, Pinecone, Weaviate 181 | - Embedding models (OpenAI, Hugging Face, Sentence Transformers) 182 | - Indexing, similarity search, and metadata filtering 183 | 184 | #### 5. **Agentic AI Concepts** 185 | - Planning, Tool-Usage, Task Decomposition 186 | - Tools: AutoGPT, BabyAGI, LangGraph 187 | - Building multi-step autonomous agents with LangChain Agents or OpenAI Function Calling 188 | 189 | --- 190 | 191 | ### Tools & Libraries 192 | 193 | - [`LangChain`](https://github.com/langchain-ai/langchain) – Framework for building LLM-powered apps 194 | - [`Transformers`](https://github.com/huggingface/transformers) – State-of-the-art models from Hugging Face 195 | - [`FAISS`](https://github.com/facebookresearch/faiss) – Vector similarity search 196 | - [`Chroma`](https://www.trychroma.com/) – Lightweight local vector DB 197 | - [`OpenAI API`](https://platform.openai.com/) – GPT-4/3.5 access 198 | 199 | --- 200 | 201 | ### Resources 202 | 203 | - [Deep Learning & Generative AI](https://aiquest.org/courses/deep-learning-and-generative-ai/) 204 | - [LangChain Docs](https://python.langchain.com/docs/introduction/) 205 | - [Hugging Face Transformers Course](https://huggingface.co/learn) 206 | - [OpenAI Cookbook](https://github.com/openai/openai-cookbook) 207 | - [LangChain YouTube Tutorials](https://www.youtube.com/playlist?list=PLKnIA16_RmvaTbihpo4MtzVm4XOQa0ER0) 208 | - [Awesome-LLM GitHub Repo](https://github.com/Hannibal046/Awesome-LLM) 209 | 210 | ### YouTube Channel 211 | - [Andrej Karpathy](https://www.youtube.com/@AndrejKarpathy) 212 | - [Sebastian Raschka](https://youtube.com/@sebastianraschka) 213 | - [Study Mart](https://www.youtube.com/playlist?list=PLKdU0fuY4OFeORaFJlz7D4XHzMupso9a9) 214 | --- 215 | 216 | ### Project Ideas 217 | 218 | 1. **Conversational Chatbot Agent** 219 | - Use LangChain + OpenAI + Vector DB 220 | - Enable document querying via conversational interface 221 | 222 | 2. **Autonomous Research Agent** 223 | - AutoGPT-style system that takes a goal and performs research via web/API 224 | 225 | 3. **AI Email Assistant** 226 | - Summarize, respond, or schedule meetings automatically using LLM + calendar/email 227 | 228 | --- 229 | 230 | ## **Step 8: Reinforcement Learning (RL)** 231 | 232 | ### **Why Learn Reinforcement Learning?** 233 | - RL focuses on decision-making in dynamic environments. 234 | - Powers AI systems like autonomous vehicles, robotics, and game AI (AlphaGo, OpenAI Gym). 235 | 236 | ### **What to Learn?** 237 | 1. **Core Concepts:** 238 | - Markov Decision Processes (MDPs) 239 | - Rewards, States, Actions, and Policies 240 | - Bellman Equations 241 | 2. **Basic Algorithms:** 242 | - Q-Learning, SARSA 243 | - Policy Gradient Methods 244 | 3. **Advanced Techniques:** 245 | - Deep Q-Learning (DQN) 246 | - Proximal Policy Optimization (PPO) 247 | - Actor-Critic Methods (A3C, DDPG) 248 | - Model-Based RL 249 | 4. **Applications:** 250 | - Game AI (chess, video games) 251 | - Robotics Control 252 | - Portfolio Optimization in Finance 253 | 254 | ### **Tools and Frameworks:** 255 | - OpenAI Gym 256 | - Stable-Baselines3 257 | - Ray RLlib 258 | 259 | ### **Resources:** 260 | - [Spinning Up in Deep RL](https://spinningup.openai.com/) 261 | - [Reinforcement Learning Playlist](https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u) 262 | 263 | --- 264 | 265 | ## **Step 9: Learn GitHub** 266 | - GitHub is a crucial platform for version control and collaboration. 267 | - Enables you to showcase your projects and build a portfolio. 268 | - Facilitates teamwork on data science projects. 269 | 270 | ### **What to Learn?** 271 | - **Git Basics:** 272 | - Version control concepts, repositories, branches, commits, pull requests. 273 | - **GitHub Skills:** 274 | - Hosting projects, collaboration workflows, managing issues. 275 | - **Best Practices:** 276 | - Writing READMEs, structuring repositories, using `.gitignore` files. 277 | 278 | ### **Resources** 279 | - [Complete GitHub for AI Engineers](https://www.youtube.com/playlist?list=PLKdU0fuY4OFcK__Q5tjqZY5mSx_u7ghUx) 280 | - Use GitHub to practice hosting Python, SQL, and machine learning projects. 281 | 282 | ----------------------------------------------- 283 | ## **Step 10: SQL** 284 | 285 | ### **Why Learn SQL?** 286 | - Essential for querying, extracting, and joining data from relational databases. 287 | - Used to preprocess and prepare data before modeling. 288 | 289 | ### **What to Learn?** 290 | - Basics: SELECT, INSERT, UPDATE, DELETE. 291 | - Intermediate: Joins (INNER, LEFT, RIGHT, FULL), subqueries. 292 | - Advanced: Window functions, CTEs (Common Table Expressions), and query optimization. 293 | 294 | ### **Resources** 295 | - [SQL Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFduhpa23Wy5fRv6SGxp2ho0) 296 | - [Programming with Mosh - SQL Playlist](https://youtu.be/7S_tz1z_5bA) 297 | - Tools like MySQL Workbench, SQLite, or PostgreSQL. 298 | 299 | --- 300 | 301 | ## **Step 11: MLOps and AI Deployment** 302 | 303 | ### **Why Learn MLOps?** 304 | - To ensure AI systems are scalable, maintainable, and production-ready. 305 | 306 | ### **What to Learn?** 307 | - **Tools and Frameworks:** 308 | - MLflow, DVC, Kubeflow. 309 | - **Deployment:** 310 | - Flask, FastAPI, TensorFlow Serving, TorchServe. 311 | - **Cloud Platforms:** 312 | - AWS SageMaker, Google Vertex AI, Azure ML. 313 | - **Monitoring and Retraining:** 314 | - Drift detection, feedback loops, CI/CD pipelines. 315 | 316 | ### **Resources** 317 | - [Deploying AI Systems](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdPhZ4k_hu7Rv3sjyi3IPtu) 318 | - [MLOps on AWS](https://www.youtube.com/playlist?list=PLmPJQXJiMoUWFW2JxRSAfhcsQ0Cr9qbv-) 319 | 320 | --- 321 | 322 | ## **Step 12: Projects** 323 | 324 | ### **Why Work on Projects?** 325 | - Demonstrates your ability to build and deploy AI systems. 326 | - Showcases expertise in real-world applications. 327 | 328 | ### **Ideas for Projects** 329 | 1. **NLP:** Build a chatbot or sentiment analysis system. 330 | 2. **Computer Vision:** Create an object detection model or face recognition system. 331 | 3. **Generative AI:** Design a text-to-image generator using Stable Diffusion. 332 | 4. **Reinforcement Learning:** Develop an AI agent for a game or robotic simulation. 333 | 5. **MLOps:** Build a CI/CD pipeline for AI model deployment. 334 | 335 | ### **Where to Find Data?** 336 | - [Kaggle](https://www.kaggle.com/datasets) 337 | - [UCI Repository](https://archive.ics.uci.edu/datasets) 338 | 339 | --- 340 | 341 | ## **Final Note: Workflow Integration** 342 | 1. Extract and preprocess data using SQL and Python. 343 | 2. Train ML/DL models and optimize them. 344 | 3. Deploy models using MLOps frameworks. 345 | 4. Monitor and iterate for production scalability. 346 | 347 | Following this roadmap step-by-step will give you the skills needed to succeed as an `AI Engineer`. Let me know if you’d like additional resources or specific examples! Feel free to reach out. 348 | 349 | This repository is continually updated based on the top `job postings` on **LinkedIn** and **Indeed**. Please pray for me and my family—your prayers are all I ask as a token of appreciation. 🙏 350 | 351 | ----------------------------------------------- 352 | ### Addition Language | Java | C++ (Optional): 353 | ### **Why Learn Java?** 354 | - Widely used in enterprise-level AI systems and backend development. 355 | - Excellent for scalable and production-ready AI applications. 356 | - Integrates seamlessly with big data frameworks and distributed systems. 357 | - Supports libraries like **Deeplearning4j** for deep learning and **Weka** for machine learning. 358 | - Ideal for building AI-powered APIs and enterprise solutions. 359 | - Plays a critical role in big data processing with frameworks like **Apache Spark**. 360 | 361 | ### **Why Learn C++?** 362 | - Known for its speed and efficiency, making it ideal for real-time AI applications. 363 | - Widely used in robotics, computer vision, and gaming AI. 364 | - Provides granular control over memory and system resources. 365 | - Supports libraries like **OpenCV** for computer vision and **TensorFlow** C++ API for backend optimizations. 366 | - Essential for robotics and autonomous systems development using **ROS**. 367 | - Excels in creating performance-critical, large-scale AI systems. 368 | 369 | #### Resources (Optional) 370 | - [Complete Java and DSA](https://www.youtube.com/playlist?list=PLfqMhTWNBTe3LtFWcvwpqTkUSlB32kJop) 371 | - [Complete c++ and DSA](https://www.youtube.com/playlist?list=PLfqMhTWNBTe137I_EPQd34TsgV6IO55pt) 372 | 373 | --- 374 | # Recomended Courses at aiQuest Intelligence 375 | 1. [Basic to Advanced Python](https://aiquest.org/courses/become-a-python-developer/) 376 | 3. [Machine Learning & Deep Learning Core Concepts](https://aiquest.org/courses/data-science-machine-learning/) 377 | 4. [Advanced Deep Learning & Generative AI](https://aiquest.org/courses/deep-learning-and-generative-ai/) 378 | 5. [Cloud, Big Data Technologies & Tools](https://aiquest.org/courses/become-a-big-data-engineer/) 379 | 380 | *`Note:`* We suggest these premium courses because they are well-organized for absolute beginners and will guide you step by step, from basic to advanced levels. Always remember that `T-shaped skills` are better than `i-shaped skill`. However, for those who cannot afford these courses, don't worry! Search on YouTube using the topic names mentioned in the roadmap. You will find plenty of `free tutorials` that are also great for learning. Best of luck! 381 | 382 | --- 383 | 384 | ## About the Author 385 | **Rashedul Alam Shakil** 386 | - 🌐 [LinkedIn Profile](https://www.linkedin.com/in/kmrashedulalam/) 387 | - 🎓 Industry Expert | Educator 388 | 389 | --- 390 | 391 | ## Other Roadmaps 392 | - [Read Roadmaps](https://github.com/rashakil-ds/Roadmap-Docs) 393 | --------------------------------------------------------------------------------