├── desi.md ├── new.md └── videshi.md /desi.md: -------------------------------------------------------------------------------- 1 | # 🤖 Complete AI & GenAI Engineering Roadmap 2025 (India Edition) 2 | 3 | ![AI Engineering Banner](https://media.giphy.com/media/ZVik7pBtu9dNS/giphy.gif) 4 | 5 | > "India is becoming a global hub for AI innovation" 6 | 7 | ## 🎯 Career Paths in AI (2025) - Indian Market 8 | | Role | Average Salary (INR/Year) | Companies | Growth | 9 | |------|-------------------------|-----------|---------| 10 | | AI Engineer | 8-20 LPA | TCS, Infosys, Amazon | High | 11 | | GenAI Specialist | 12-30 LPA | Microsoft, Google, Startups | Very High | 12 | | AI Research Scientist | 15-35 LPA | IIIT, IIT, Research Labs | High | 13 | | MLOps Engineer | 10-25 LPA | Flipkart, Swiggy, Ola | High | 14 | 15 | ## 🎓 Top Indian AI Education Hubs 16 | 1. IIT Madras - [Online BSc in Programming & Data Science](https://study.iitm.ac.in/ds/) 17 | 2. IIIT Hyderabad - [Machine Learning Laboratory](https://mllab.iiit.ac.in/) 18 | 3. IISc Bangalore - [AI & ML Programs](https://iisc.ac.in/) 19 | 4. IIT Delhi - [AI Research](http://ai.iitd.ac.in/) 20 | 21 | ## 📚 Prerequisites 22 | - Python programming (NPTEL/Coding Ninjas) 23 | - Mathematics (12th grade level) 24 | - English communication 25 | - Problem-solving skills 26 | 27 | ## 📺 Top Indian YouTube Channels for AI 28 | 29 | ### 🎯 Programming & AI Fundamentals 30 | | Channel | Focus | Language | 31 | |---------|-------|----------| 32 | | [Krish Naik](https://www.youtube.com/user/krishnaik06) | ML/DL/MLOps | English/Hindi | 33 | | [codebasics](https://www.youtube.com/c/codebasics) | Python/DS/ML | Hindi/English | 34 | | [Telusko](https://www.youtube.com/user/javaboynavin) | Python/Programming | English | 35 | | [CodeWithHarry](https://www.youtube.com/c/CodeWithHarry) | Programming Basics | Hindi | 36 | 37 | ### 🎯 Advanced AI & Interview Prep 38 | | Channel | Focus | Language | 39 | |---------|-------|----------| 40 | | [AIEngineering](https://www.youtube.com/c/AIEngineeringLife) | ML/DL Projects | English | 41 | | [Dhaval Patel](https://www.youtube.com/c/DhavalPatel) | ML/Interview Prep | English/Hindi | 42 | | [iNeuron Intelligence](https://www.youtube.com/c/iNeuroniNtelligence) | AI/ML Courses | English/Hindi | 43 | 44 | ## 📅 Learning Schedule (Indian Context) 45 | 46 | ### 🌟 TRACK 1: FOUNDATION (8 WEEKS) 47 | 48 | #### Week 1-2: Python & Mathematics 49 | | Day | Topics | Indian Resources | Projects | 50 | |-----|---------|-----------------|-----------| 51 | | 1-3 | • Python Basics
• Data Types
• Functions | 🎥 [CodeWithHarry Python (Hindi)](https://youtu.be/gfDE2a7MKjA)
📘 [NPTEL Python Course](https://nptel.ac.in/courses/106/106/106106182/)
🎥 [Telusko Python](https://youtube.com/playlist?list=PLsyeobzWxl7poL9JTVyndKe62ieoN-MZ3) | Simple calculator | 52 | | 4-7 | • NumPy
• Pandas
• Matplotlib | 🎥 [codebasics NumPy (Hindi)](https://youtu.be/Lb6ju7jXj4U)
🎥 [Krish Naik Pandas](https://youtu.be/RhEjmHeDNoA) | Data analysis | 53 | | 8-10 | • Linear Algebra
• Matrices | 🎥 [3Blue1Brown (with Hindi subtitles)](https://youtu.be/fNk_zzaMoSs)
📘 [NCERT Class 12 Maths](http://ncert.nic.in/) | Matrix calculator | 54 | | 11-14 | • Statistics
• Probability | 🎥 [Khan Academy India](https://www.khanacademy.org/in)
📘 [Great Learning Stats](https://www.mygreatlearning.com/academy) | Statistical tool | 55 | 56 | ### 🌟 TRACK 2: MACHINE LEARNING (10 WEEKS) 57 | 58 | #### Week 3-4: ML Fundamentals 59 | | Day | Topics | Indian Resources | Projects | 60 | |-----|---------|-----------------|-----------| 61 | | 15-17 | • ML Introduction
• Supervised Learning | 🎥 [Krish Naik ML Playlist](https://youtube.com/playlist?list=PLZoTAELRMXVPBTrWtJkn3wWQxZkmTXGwe)
📘 [Analytics Vidhya ML Guide](https://www.analyticsvidhya.com/machine-learning/) | House Price Predictor | 62 | | 18-21 | • Linear Regression
• Logistic Regression | 🎥 [codebasics ML (Hindi)](https://youtube.com/playlist?list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw)
📘 [Great Learning Blog](https://www.mygreatlearning.com/blog/) | Bangalore House Price Prediction | 63 | | 22-28 | • Decision Trees
• Random Forests | 🎥 [iNeuron ML (Hindi/English)](https://www.youtube.com/c/iNeuroniNtelligence)
📘 [Scaler Topics](https://www.scaler.com/topics/) | IPL Score Predictor | 64 | 65 | ### 🌟 TRACK 3: DEEP LEARNING (12 WEEKS) 66 | 67 | #### Week 5-8: Neural Networks & Computer Vision 68 | | Day | Topics | Indian Resources | Projects | 69 | |-----|---------|-----------------|-----------| 70 | | 29-35 | • Neural Networks
• PyTorch/TensorFlow | 🎥 [AIEngineering PyTorch](https://youtube.com/playlist?list=PLZoTAELRMXVNxYFq_9MuiUdn2YnlFqmMK)
📘 [IIIT-H Deep Learning](https://www.iiit.ac.in/) | Indian Currency Detector | 71 | | 36-42 | • CNN
• Transfer Learning | 🎥 [Campus X CNN Series](https://www.youtube.com/c/CampusX-official)
📘 [IIT-M DL Course](https://www.cse.iitm.ac.in/) | Indian Food Classifier | 72 | | 43-49 | • Object Detection
• Face Recognition | 🎥 [The AI Guy](https://www.youtube.com/c/TheAIGuy)
📘 [LearnOpenCV Blog](https://learnopencv.com/) | Vehicle Number Plate Detection | 73 | 74 | ### 🌟 TRACK 4: GENAI & LLMs (10 WEEKS) 75 | 76 | #### Indian Companies Working on GenAI 77 | | Company | Location | Focus Area | Tech Stack | 78 | |---------|----------|------------|------------| 79 | | Sarvam AI | Bangalore | Indic LLMs | PyTorch, Transformers | 80 | | Bhashini | Delhi | Language Translation | Custom Models | 81 | | Vernacular.ai | Bangalore | Voice AI | Speech Recognition | 82 | | Krutrim AI | Mumbai | Multilingual AI | Custom Architecture | 83 | 84 | #### Week 9-12: LLM Development 85 | | Day | Topics | Indian Resources | Projects | 86 | |-----|---------|-----------------|-----------| 87 | | 50-56 | • Transformer Architecture
• BERT/GPT | 🎥 [Vaibhav Balloli](https://www.youtube.com/c/VaibhavBalloli)
📘 [Hugging Face India](https://discuss.huggingface.co/c/local-communities/india/) | Hindi Text Summarizer | 88 | | 57-63 | • Fine-tuning
• Prompt Engineering | 🎥 [Machine Learning Street Talk India](https://www.youtube.com/c/MachineLearningStreetTalk)
📘 [OpenInApp Docs](https://docs.openinapp.com/) | Indic Language Chatbot | 89 | | 64-70 | • RAG
• Vector Databases | 🎥 [Coding Ninjas GenAI](https://www.codingninjas.com/)
📘 [LangChain India Guide](https://python.langchain.com/) | Legal Document Assistant | 90 | 91 | ## 🎯 Indian Industry-Specific Projects 92 | 93 | ### Banking & Finance 94 | 1. UPI Fraud Detection System 95 | 2. Credit Score Predictor 96 | 3. Stock Market Analysis for NSE/BSE 97 | 4. Insurance Claim Processor 98 | 99 | ### E-commerce & Retail 100 | 1. Flipkart Review Analyzer 101 | 2. Product Recommendation System 102 | 3. Inventory Demand Predictor 103 | 4. Visual Search for Indian Products 104 | 105 | ### Healthcare 106 | 1. Disease Prediction from Symptoms 107 | 2. Medical Report Analyzer 108 | 3. AYUSH Medicine Classifier 109 | 4. Healthcare Chatbot (Multi-lingual) 110 | 111 | ### Government & Public Sector 112 | 1. Crop Yield Prediction 113 | 2. Air Quality Analysis 114 | 3. Traffic Management System 115 | 4. Document Digitization (Multiple Indian Languages) 116 | 117 | ## 📝 Interview Preparation (Indian Companies) 118 | 119 | ### Service-Based Companies (TCS, Infosys, Wipro) 120 | | Round | Focus | Preparation Resources | 121 | |-------|-------|----------------------| 122 | | Aptitude | Quantitative, Logical | 🎯 [IndiaBix](https://www.indiabix.com/)
📘 [PrepInsta](https://prepinsta.com/) | 123 | | Technical | DS, Algo, ML Basics | 🎯 [InterviewBit India](https://www.interviewbit.com/)
📘 [GeeksforGeeks](https://www.geeksforgeeks.org/) | 124 | | ML/AI | Basic ML Concepts | 🎯 [Analytics Vidhya Interview Series](https://www.analyticsvidhya.com/blog/category/interview-questions/)
📘 [ML Interview Guide](https://github.com/youssefHosni/Data-Science-Interview-Questions) | 125 | | HR | Communication, Culture | 🎯 [Glassdoor India Reviews](https://www.glassdoor.co.in/) | 126 | 127 | ### Product Companies (Amazon, Microsoft, Google India) 128 | | Round | Focus | Preparation Resources | 129 | |-------|-------|----------------------| 130 | | Online Assessment | DSA, ML Problems | 🎯 [LeetCode India](https://leetcode.com/discuss/interview-question?currentPage=1&orderBy=hot&query=india) | 131 | | Technical (1-2) | ML Theory, System Design | 🎯 [Striver's SDE Sheet](https://takeuforward.org/interviews/strivers-sde-sheet-top-coding-interview-problems/) | 132 | | ML/AI Design | End-to-End ML System | 🎯 [Grokking ML Interview](https://www.educative.io/courses/grokking-the-machine-learning-interview) | 133 | | Behavioral | Leadership Principles | 🎯 [InterviewBit HR Questions](https://www.interviewbit.com/hr-interview-questions/) | 134 | 135 | ### Indian Startups 136 | | Round | Focus | Preparation Resources | 137 | |-------|-------|----------------------| 138 | | Take-home Assignment | Practical Implementation | 🎯 [Kaggle India](https://www.kaggle.com/india) | 139 | | Technical Discussion | Problem Solving | 🎯 [MLOps Community India](https://mlops.community/) | 140 | | Culture Fit | Startup Mindset | 🎯 [YourStory Startup Jobs](https://yourstory.com/jobs) | 141 | 142 | ## 🎓 Indian AI Certifications & Their Value 143 | 144 | ### Government & Academic Certifications 145 | | Certification | Institution | Cost (INR) | Value | 146 | |--------------|-------------|-------------|--------| 147 | | PG Diploma in AI | IIT Madras | ₹2.3 Lakhs | Very High | 148 | | AI/ML Certification | IIIT Hyderabad | ₹2.5 Lakhs | Very High | 149 | | Advanced ML Program | IISc Bangalore | ₹3 Lakhs | Excellent | 150 | | NPTEL AI/ML | IITs | ₹1000 | Good | 151 | 152 | ### Industry Certifications (Indian Pricing) 153 | | Certification | Provider | Cost (INR) | Value | 154 | |--------------|----------|-------------|--------| 155 | | AI Engineering | Simplilearn | ₹80,000 | Good | 156 | | ML Specialization | upGrad | ₹1.5 Lakhs | Good | 157 | | Deep Learning | Great Learning | ₹70,000 | Good | 158 | | AI/ML Bootcamp | Scaler | ₹2.5 Lakhs | Very Good | 159 | 160 | ### Free Certifications (with Indian Recognition) 161 | 1. Google Cloud AI (Free with ₹1000 exam fee) 162 | 2. Microsoft AI Fundamentals (Free learning, exam cost: ₹4000) 163 | 3. IBM AI Engineering (Free on Coursera) 164 | 4. Intel AI Academy Certification (Free) 165 | 166 | ### Indian Learning Platforms & Costs 167 | 168 | #### Free Resources 169 | 1. NPTEL Courses (Free with certification at ₹1000) 170 | 2. Swayam Portal 171 | 3. Government AI Portal ([ai.gov.in](https://ai.gov.in)) 172 | 4. IIT-M Online Degree (₹15,000-25,000 per semester) 173 | 174 | #### Paid Resources (Indian Pricing) 175 | | Platform | Course | Cost (INR) | Duration | 176 | |----------|--------|------------|-----------| 177 | | Coding Ninjas | AI & ML | ₹15,000-20,000 | 6 months | 178 | | Scaler Academy | AI/ML Program | ₹2-3 Lakhs | 12 months | 179 | | upGrad | ML & AI | ₹2.5-4 Lakhs | 12-18 months | 180 | | Great Learning | AI/ML PG | ₹2-3 Lakhs | 12 months | 181 | 182 | ### Indian AI Communities 183 | 1. [TFUG India](https://www.tensorflow.org/community/groups) 184 | 2. [PyTorch India](https://www.facebook.com/groups/pytorch.india/) 185 | 3. [AI India Discord](https://discord.gg/india-ai) 186 | 4. [DataHack by Analytics Vidhya](https://datahack.analyticsvidhya.com/) 187 | 188 | ## 🚀 Indian AI Startup Ecosystem 189 | 190 | ### Major AI Hubs in India 191 | | City | Notable Startups | Focus Areas | 192 | |------|-----------------|-------------| 193 | | Bangalore | Artivatic, Mad Street Den | FinTech, Retail AI | 194 | | Mumbai | Tata Neu, Haptik | Conversational AI | 195 | | Delhi NCR | Staqu, Parallel Dots | Computer Vision | 196 | | Hyderabad | TechEagle, Darwinbox | HR Tech, Drones | 197 | | Pune | Mindtree AI, Zensar | Enterprise AI | 198 | 199 | ### AI Startup Incubators 200 | 1. T-Hub (Hyderabad) 201 | 2. NASSCOM CoE (Multiple Cities) 202 | 3. IIT Madras Research Park 203 | 4. Venture Studio (Bangalore) 204 | 205 | ### Funding Resources 206 | 1. [100X.VC](https://www.100x.vc/) 207 | 2. Sequoia Surge 208 | 3. Axilor Ventures 209 | 4. Indian Angel Network 210 | 211 | ## 💼 Job Search Strategies (Indian Market) 212 | 213 | ### Online Job Portals 214 | | Portal | Best For | Tips | 215 | |--------|----------|------| 216 | | Naukri.com | IT/ML Jobs | Create AI-specific resume | 217 | | LinkedIn India | Network Building | Join AI India groups | 218 | | AngelList India | Startup Jobs | Follow AI startups | 219 | | Indeed India | MNC Jobs | Set job alerts | 220 | 221 | ### Networking Platforms 222 | 1. [LinkedIn AI Groups India](https://www.linkedin.com/groups/) 223 | 2. [Discord AI India](https://discord.gg/aiindia) 224 | 3. [Reddit India AI](https://www.reddit.com/r/IndianAI/) 225 | 4. [Kaggle India Community](https://www.kaggle.com/india) 226 | 227 | ### AI Conferences & Events (India) 228 | | Event | Location | Focus | Networking Value | 229 | |-------|----------|-------|------------------| 230 | | MLDS | Multiple Cities | ML/DS | Very High | 231 | | Cypher | Bangalore | Analytics/AI | High | 232 | | RAISE | New Delhi | AI Summit | Very High | 233 | | PyConf India | Multiple Cities | Python/AI | High | 234 | 235 | ## 💰 Salary Negotiations (Indian Context) 236 | 237 | ### Salary Components 238 | 1. Base Pay 239 | 2. Stocks (ESOPs) 240 | 3. Variable Pay 241 | 4. Benefits 242 | 243 | ### Salary Ranges by Experience (2025) 244 | | Experience | Base Pay (LPA) | Total CTC (LPA) | 245 | |------------|---------------|------------------| 246 | | Fresher | 5-8 | 6-10 | 247 | | 1-3 years | 8-15 | 10-20 | 248 | | 3-5 years | 15-25 | 20-35 | 249 | | 5+ years | 25-40 | 35-60+ | 250 | 251 | ### Negotiation Tips 252 | 1. Research on Glassdoor India 253 | 2. Use competing offers 254 | 3. Consider ESOPs in startups 255 | 4. Factor in city cost of living 256 | 257 | ## 🌐 Remote Work Opportunities 258 | 259 | ### Indian Companies Offering Remote AI Roles 260 | 1. Fractal Analytics 261 | 2. Tiger Analytics 262 | 3. Sigmoid 263 | 4. ThoughtWorks India 264 | 265 | ### International Remote Opportunities 266 | | Type | Platforms | Salary Range (Monthly) | 267 | |------|-----------|----------------------| 268 | | Freelance | Upwork, Toptal | $1000-5000 | 269 | | Full-time Remote | AngelList, WeWorkRemotely | $2000-8000 | 270 | | Contract | Turing, Andela | $1500-7000 | 271 | 272 | ## 📚 Higher Education Pathways 273 | 274 | ### Masters Programs in India 275 | | University | Program | Duration | Fee (INR) | 276 | |------------|---------|----------|------------| 277 | | IIT Bombay | M.Tech (AI) | 2 years | 2-3 Lakhs | 278 | | IIIT Bangalore | M.Tech (ML) | 2 years | 3-4 Lakhs | 279 | | IISc | M.Tech (AI) | 2 years | 2-3 Lakhs | 280 | | IIT Delhi | M.Tech (AI) | 2 years | 2-3 Lakhs | 281 | 282 | ### Online Degree Options 283 | 1. IIT Madras BS in Data Science 284 | 2. BITS Pilani Work Integrated Learning Programs 285 | 3. IIIT Bangalore Online M.Tech 286 | 287 | ### Research Opportunities 288 | 1. Research Internship at IITs/IISc 289 | 2. Microsoft Research India 290 | 3. Google AI Research India 291 | 4. IBM Research India 292 | 293 | ## 📱 Essential Apps & Tools for Indian AI Engineers 294 | 295 | ### Learning Apps 296 | 1. GeeksforGeeks 297 | 2. InterviewBit 298 | 3. Coursera 299 | 4. Unacademy 300 | 301 | ### Development Tools 302 | 1. Google Colab (Free GPU) 303 | 2. Kaggle Notebooks 304 | 3. GitHub Student Pack 305 | 4. Azure for Students 306 | 307 | ### Interview Prep Apps 308 | 1. LeetCode 309 | 2. HackerRank 310 | 3. CodeChef 311 | 4. PrepInsta 312 | 313 | ## 🎯 First 90 Days Plan in AI Job 314 | 315 | ### Month 1 316 | 1. Understanding company tech stack 317 | 2. Learning internal tools 318 | 3. Basic project contributions 319 | 4. Team collaboration 320 | 321 | ### Month 2 322 | 1. Independent feature development 323 | 2. Code review participation 324 | 3. Documentation 325 | 4. Small project ownership 326 | 327 | ### Month 3 328 | 1. End-to-end project handling 329 | 2. Team presentations 330 | 3. Mentoring juniors 331 | 4. Process improvements 332 | 333 | ## 💡 Tips for Indian AI Engineers 334 | 335 | 1. Build strong mathematics foundation 336 | 2. Focus on practical implementations 337 | 3. Contribute to open source 338 | 4. Network actively 339 | 5. Keep updating skills 340 | 6. Document your projects 341 | 7. Build online presence 342 | 8. Learn cloud platforms 343 | 9. Practice communication 344 | 10. Stay updated with AI news 345 | -------------------------------------------------------------------------------- /new.md: -------------------------------------------------------------------------------- 1 | # AI Development Roadmap 2025 2 | ## For Web Developers Transitioning to AI 3 | 4 | ### Prerequisites 5 | - Strong JavaScript/TypeScript knowledge 6 | - Experience with React and Next.js 7 | - Understanding of web development principles 8 | - Git version control 9 | - Basic command line proficiency 10 | 11 | ### Phase 1: Python Foundations (4-6 weeks) 12 | #### Week 1-2: Python Basics 13 | - **Core Python Concepts** 14 | - Variables, data types, and operators 15 | - Control structures and functions 16 | - Object-oriented programming 17 | - List comprehensions and generators 18 | 19 | **Resources:** 20 | - [Python Official Tutorial](https://docs.python.org/3/tutorial/) 21 | - [Real Python - Python Basics](https://realpython.com/learning-paths/python3-introduction/) 22 | - Course: [Python for JavaScript Developers](https://www.udemy.com/course/python-for-javascript-developers/) 23 | 24 | **Practice Projects:** 25 | 1. Convert a JavaScript utility library to Python 26 | 2. Build a CLI tool for file management 27 | 3. Create a simple API with FastAPI 28 | 29 | #### Week 3-4: Data Science Libraries 30 | - **NumPy Fundamentals** 31 | - Arrays and array operations 32 | - Broadcasting 33 | - Linear algebra operations 34 | 35 | - **Pandas Basics** 36 | - DataFrames and Series 37 | - Data cleaning and manipulation 38 | - Data analysis and visualization 39 | 40 | **Resources:** 41 | - [NumPy User Guide](https://numpy.org/doc/stable/user/index.html) 42 | - [Pandas Documentation](https://pandas.pydata.org/docs/) 43 | - Course: [Data Analysis with Python](https://www.coursera.org/learn/data-analysis-with-python) 44 | 45 | **Practice Projects:** 46 | 1. Data cleaning and analysis pipeline 47 | 2. CSV to JSON converter with data validation 48 | 3. Simple dashboard with Plotly 49 | 50 | ### Phase 2: AI/ML Foundations (8-10 weeks) 51 | #### Week 1-3: Machine Learning Basics 52 | - **Core ML Concepts** 53 | - Supervised vs unsupervised learning 54 | - Classification and regression 55 | - Model evaluation metrics 56 | - Feature engineering 57 | 58 | **Resources:** 59 | - [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course) 60 | - [Fast.ai Practical Deep Learning](https://course.fast.ai/) 61 | - Book: "Hands-On Machine Learning with Scikit-Learn and TensorFlow" 62 | 63 | #### Week 4-6: Deep Learning Fundamentals 64 | - **Neural Networks** 65 | - Architecture and components 66 | - Forward and backward propagation 67 | - Activation functions 68 | - Loss functions and optimization 69 | 70 | **Resources:** 71 | - [Deep Learning Specialization](https://www.deeplearning.ai/) 72 | - [PyTorch Tutorials](https://pytorch.org/tutorials/) 73 | - [TensorFlow Guide](https://www.tensorflow.org/guide) 74 | 75 | #### Week 7-10: Natural Language Processing 76 | - **NLP Concepts** 77 | - Text preprocessing 78 | - Tokenization 79 | - Embeddings 80 | - Transformer architecture 81 | 82 | **Resources:** 83 | - [HuggingFace Course](https://huggingface.co/course) 84 | - [Spacy Course](https://course.spacy.io/) 85 | - [Stanford NLP Course](https://web.stanford.edu/class/cs224n/) 86 | 87 | ### Phase 3: LLM Integration (8-10 weeks) 88 | #### Week 1-3: LLM Fundamentals 89 | - **Core Concepts** 90 | - Prompt engineering 91 | - Context windows 92 | - Temperature and sampling 93 | - Token optimization 94 | 95 | **Resources:** 96 | - [OpenAI Documentation](https://platform.openai.com/docs) 97 | - [Anthropic Claude Documentation](https://docs.anthropic.com/) 98 | - [Prompt Engineering Guide](https://www.promptingguide.ai/) 99 | 100 | #### Week 4-6: Vector Databases 101 | - **Concepts and Implementation** 102 | - Embeddings generation 103 | - Vector similarity search 104 | - Database options and tradeoffs 105 | 106 | **Resources:** 107 | - [Pinecone Learning Center](https://www.pinecone.io/learn/) 108 | - [Weaviate Documentation](https://weaviate.io/developers/weaviate) 109 | - [Milvus Bootcamp](https://milvus.io/bootcamp) 110 | 111 | #### Week 7-10: RAG Applications 112 | - **Building RAG Systems** 113 | - Document processing 114 | - Chunking strategies 115 | - Query processing 116 | - Response generation 117 | 118 | **Resources:** 119 | - [LangChain Documentation](https://python.langchain.com/docs/get_started) 120 | - [LlamaIndex Guide](https://docs.llamaindex.ai/) 121 | - [Chroma Documentation](https://docs.trychroma.com/) 122 | 123 | ### Phase 4: Full-Stack AI Development (12 weeks) 124 | #### Week 1-4: Frontend Integration 125 | - **AI-Powered Components** 126 | - Streaming responses 127 | - Progressive loading 128 | - Error handling 129 | - Rate limiting 130 | 131 | **Resources:** 132 | - [Vercel AI SDK](https://sdk.vercel.ai/docs) 133 | - [React Server Components](https://react.dev/blog/2023/03/22/react-labs-what-we-have-been-working-on-march-2023#react-server-components) 134 | - [Next.js AI Documentation](https://nextjs.org/docs/app/building-your-application/ai) 135 | 136 | #### Week 5-8: Backend Integration 137 | - **AI Service Architecture** 138 | - API design 139 | - Caching strategies 140 | - Load balancing 141 | - Cost optimization 142 | 143 | **Resources:** 144 | - [FastAPI Documentation](https://fastapi.tiangolo.com/) 145 | - [Redis AI Documentation](https://redis.io/docs/stack/ai/) 146 | - [MongoDB Vector Search](https://www.mongodb.com/docs/atlas/vector-search/) 147 | 148 | #### Week 9-12: Production Deployment 149 | - **MLOps Basics** 150 | - Model versioning 151 | - Monitoring 152 | - A/B testing 153 | - Performance optimization 154 | 155 | **Resources:** 156 | - [AWS SageMaker Documentation](https://docs.aws.amazon.com/sagemaker/) 157 | - [Google Vertex AI Guide](https://cloud.google.com/vertex-ai/docs) 158 | - [Azure ML Documentation](https://learn.microsoft.com/en-us/azure/machine-learning/) 159 | 160 | ### Capstone Projects 161 | 1. **AI-Powered Content Management System** 162 | - Content generation 163 | - Auto-tagging 164 | - SEO optimization 165 | - Image generation 166 | 167 | 2. **Intelligent Document Processing System** 168 | - PDF parsing 169 | - Information extraction 170 | - Summary generation 171 | - Question answering 172 | 173 | 3. **Code Analysis Assistant** 174 | - Code review automation 175 | - Bug detection 176 | - Refactoring suggestions 177 | - Documentation generation 178 | 179 | ### Continuing Education 180 | - Join AI research paper reading groups 181 | - Participate in Kaggle competitions 182 | - Contribute to open-source AI projects 183 | - Attend AI conferences and meetups 184 | 185 | ### Career Development 186 | - Build a portfolio of AI projects 187 | - Write technical blogs about your learning journey 188 | - Network with AI developers and researchers 189 | - Join AI-focused Discord and Slack communities 190 | 191 | ### Recommended Books 192 | 1. "Deep Learning with Python" by François Chollet 193 | 2. "Building Machine Learning Powered Applications" by Emmanuel Ameisen 194 | 3. "Designing Machine Learning Systems" by Chip Huyen 195 | 4. "Natural Language Processing with Transformers" by Lewis Tunstall et al. 196 | 5. "Production Machine Learning Systems" by Emmanuel Ameisen 197 | 198 | ### Online Communities 199 | - [Hugging Face Forums](https://discuss.huggingface.co/) 200 | - [Fast.ai Forums](https://forums.fast.ai/) 201 | - [AI Stack Exchange](https://ai.stackexchange.com/) 202 | - [r/MachineLearning](https://www.reddit.com/r/MachineLearning/) 203 | - [PyTorch Discussion Forums](https://discuss.pytorch.org/) 204 | 205 | ### Time Commitment 206 | - Full-time: 6-8 months 207 | - Part-time: 12-15 months 208 | - Casual learning: 18-24 months 209 | 210 | Remember to: 211 | - Build projects continuously 212 | - Document your learning 213 | - Network with others 214 | - Stay updated with AI news 215 | - Practice regularly 216 | - Focus on practical applications 217 | -------------------------------------------------------------------------------- /videshi.md: -------------------------------------------------------------------------------- 1 | # 🤖 Complete AI & GenAI Engineering Roadmap 2025 2 | 3 | ![AI Engineering Banner](https://media.giphy.com/media/ZVik7pBtu9dNS/giphy.gif) 4 | 5 | > "The best way to predict the future is to create it with AI" 6 | 7 | ## 🎯 Career Paths in AI (2025) 8 | | Role | Average Salary | Focus Areas | Growth Potential | 9 | |------|----------------|-------------|------------------| 10 | | AI Engineer | $120k-200k | ML/DL, MLOps | High | 11 | | GenAI Specialist | $150k-250k | LLMs, Transformers | Very High | 12 | | AI Research Scientist | $130k-220k | Advanced AI, Papers | High | 13 | | MLOps Engineer | $115k-180k | Deployment, Scaling | High | 14 | 15 | ## 📚 Prerequisites 16 | - Basic Python programming 17 | - High school mathematics 18 | - Analytical thinking 19 | - Problem-solving skills 20 | 21 | ## 🎓 Learning Tracks 22 | 23 | ### Track 1️⃣: Foundation (8 weeks) 24 | ### Track 2️⃣: Machine Learning (10 weeks) 25 | ### Track 3️⃣: Deep Learning (12 weeks) 26 | ### Track 4️⃣: GenAI & LLMs (10 weeks) 27 | ### Track 5️⃣: MLOps & Deployment (8 weeks) 28 | 29 | ## 📅 Detailed Learning Schedule 30 | 31 | ### 🌟 TRACK 1: FOUNDATION (8 WEEKS) 32 | 33 | #### Week 1-2: Python & Mathematics 34 | | Day | Topics | Resources | Projects | 35 | |-----|---------|-----------|-----------| 36 | | 1-3 | • Python Basics
• Data Types
• Functions | 🎥 [Python for AI - freeCodeCamp](https://youtu.be/WGJJIrtnfpk)
📘 [Python Documentation](https://docs.python.org/3/)
🎥 [Tech With Tim - Python](https://www.youtube.com/c/TechWithTim) | Build a data processing utility | 37 | | 4-7 | • NumPy
• Pandas
• Matplotlib | 🎥 [Keith Galli - Pandas](https://youtu.be/vmEHCJofslg)
🎥 [Corey Schafer - Python](https://www.youtube.com/c/Coreyms) | Data analysis dashboard | 38 | | 8-10 | • Linear Algebra
• Matrices
• Vectors | 🎥 [3Blue1Brown - Linear Algebra](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)
📘 [Khan Academy - Linear Algebra](https://www.khanacademy.org/math/linear-algebra) | Matrix operations library | 39 | | 11-14 | • Statistics
• Probability
• Calculus | 🎥 [StatQuest with Josh Starmer](https://www.youtube.com/c/joshstarmer)
📘 [Mathematics for ML](https://mml-book.github.io/) | Statistical analysis tool | 40 | 41 | #### Week 3-4: Data Science Fundamentals 42 | | Day | Topics | Resources | Projects | 43 | |-----|---------|-----------|-----------| 44 | | 15-17 | • Data Cleaning
• EDA
• Visualization | 🎥 [Ken Jee - Data Science](https://www.youtube.com/c/KenJee1)
📘 [Kaggle Learn](https://www.kaggle.com/learn) | EDA on real dataset | 45 | | 18-21 | • Feature Engineering
• Data Preprocessing | 🎥 [Krish Naik](https://www.youtube.com/user/krishnaik06)
📘 [Feature Engineering Book](https://www.oreilly.com/library/view/feature-engineering-for/9781491953235/) | Feature engineering pipeline | 46 | | 22-28 | • SQL
• Database Basics
• Data Integration | 🎥 [SQL Tutorial - freeCodeCamp](https://youtu.be/HXV3zeQKqGY)
📘 [Mode SQL Tutorial](https://mode.com/sql-tutorial/) | Database integration project | 47 | 48 | ### 🌟 TRACK 2: MACHINE LEARNING (10 WEEKS) 49 | 50 | #### Week 5-6: ML Fundamentals 51 | | Day | Topics | Resources | Projects | 52 | |-----|---------|-----------|-----------| 53 | | 29-31 | • ML Introduction
• Supervised Learning | 🎥 [Andrew Ng's ML Course](https://www.coursera.org/learn/machine-learning)
🎥 [Sentdex ML](https://www.youtube.com/c/sentdex) | Basic ML model | 54 | | 32-35 | • Linear Regression
• Logistic Regression | 🎥 [StatQuest ML](https://www.youtube.com/playlist?list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF)
📘 [Scikit-learn Docs](https://scikit-learn.org/) | Prediction models | 55 | | 36-42 | • Decision Trees
• Random Forests | 🎥 [Abhishek Thakur](https://www.youtube.com/c/AbhishekThakurAbhi)
📘 [ML Mastery](https://machinelearningmastery.com/) | Ensemble models | 56 | 57 | #### Week 7-8: Advanced ML 58 | | Day | Topics | Resources | Projects | 59 | |-----|---------|-----------|-----------| 60 | | 43-46 | • SVM
• KNN
• Naive Bayes | 🎥 [ritvikmath](https://www.youtube.com/c/ritvikmath)
📘 [ML From Scratch](https://github.com/eriklindernoren/ML-From-Scratch) | Classification system | 61 | | 47-49 | • Clustering
• Dimensionality Reduction | 🎥 [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai)
📘 [Hands-on ML Book](https://github.com/ageron/handson-ml2) | Clustering project | 62 | | 50-56 | • Model Evaluation
• Cross-validation | 🎥 [Python Engineer](https://www.youtube.com/c/PythonEngineer)
📘 [Evaluation Metrics](https://neptune.ai/blog/evaluation-metrics-binary-classification) | Model evaluation suite | 63 | 64 | ### 🌟 TRACK 3: DEEP LEARNING (12 WEEKS) 65 | 66 | #### Week 9-10: Neural Networks Fundamentals 67 | | Day | Topics | Resources | Projects | 68 | |-----|---------|-----------|-----------| 69 | | 57-60 | • Neural Network Basics
• Perceptrons
• Activation Functions | 🎥 [3Blue1Brown - Neural Networks](https://youtu.be/aircAruvnKk)
📘 [Deep Learning Book](https://www.deeplearningbook.org/) | Build NN from scratch | 70 | | 61-63 | • Backpropagation
• Gradient Descent
• Optimizers | 🎥 [Stanford CS231n](https://youtube.com/playlist?list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk)
📘 [PyTorch Tutorials](https://pytorch.org/tutorials/) | Optimizer implementation | 71 | | 64-70 | • Loss Functions
• Regularization
• Dropout | 🎥 [Weights & Biases](https://www.youtube.com/c/WeightsBiases)
📘 [TensorFlow Guide](https://www.tensorflow.org/guide) | Training pipeline | 72 | 73 | #### Week 11-14: Advanced Deep Learning 74 | | Day | Topics | Resources | Projects | 75 | |-----|---------|-----------|-----------| 76 | | 71-77 | • CNN Architecture
• Image Processing
• Computer Vision | 🎥 [Computerphile](https://www.youtube.com/user/computerphile)
📘 [Fast.ai Course](https://course.fast.ai/) | Image classification | 77 | | 78-84 | • RNN & LSTM
• Sequential Data
• Time Series | 🎥 [DeepLearning.AI](https://www.youtube.com/c/Deeplearningai)
📘 [Sequence Models](https://www.coursera.org/learn/nlp-sequence-models) | Time series predictor | 78 | | 85-91 | • GANs
• Autoencoders
• Transfer Learning | 🎥 [Lex Fridman](https://www.youtube.com/c/lexfridman)
📘 [GAN Lab](https://poloclub.github.io/ganlab/) | Image generation | 79 | 80 | ### 🌟 TRACK 4: GENAI & LLMs (10 WEEKS) 81 | 82 | #### Week 15-17: Transformer Architecture 83 | | Day | Topics | Resources | Projects | 84 | |-----|---------|-----------|-----------| 85 | | 92-98 | • Attention Mechanism
• Self-Attention
• Multi-Head Attention | 🎥 [Jay Alammar](https://jalammar.github.io/)
📘 [Attention Paper](https://arxiv.org/abs/1706.03762) | Attention visualizer | 86 | | 99-105 | • BERT
• GPT Architecture
• T5 Models | 🎥 [Andrej Karpathy](https://www.youtube.com/c/AndrejKarpathy)
📘 [Hugging Face Course](https://huggingface.co/course) | Fine-tune BERT | 87 | 88 | #### Week 18-20: LLM Development 89 | | Day | Topics | Resources | Projects | 90 | |-----|---------|-----------|-----------| 91 | | 106-112 | • Prompt Engineering
• Few-shot Learning
• Chain-of-Thought | 🎥 [AI Coffee Break](https://www.youtube.com/c/AICoffeeBreak)
📘 [LangChain Docs](https://python.langchain.com/en/latest/) | Custom chatbot | 92 | | 113-119 | • RAG
• Fine-tuning LLMs
• PEFT Methods | 🎥 [Assembly AI](https://www.youtube.com/c/AssemblyAI)
📘 [LlamaIndex Docs](https://gpt-index.readthedocs.io/) | RAG application | 93 | 94 | #### Week 21-24: Advanced GenAI 95 | | Day | Topics | Resources | Projects | 96 | |-----|---------|-----------|-----------| 97 | | 120-126 | • Multi-modal Models
• DALL-E
• Stable Diffusion | 🎥 [Machine Learning Street Talk](https://www.youtube.com/c/MachineLearningStreetTalk)
📘 [Diffusers Library](https://huggingface.co/docs/diffusers/) | Image generator | 98 | | 127-133 | • Vector Databases
• Semantic Search
• Embeddings | 🎥 [DataEngineering](https://www.youtube.com/c/DataEngineeringCourse)
📘 [Pinecone Docs](https://docs.pinecone.io/) | Search engine | 99 | 100 | ### 🌟 TRACK 5: MLOPS & DEPLOYMENT (8 WEEKS) 101 | 102 | #### Week 25-28: MLOps Fundamentals 103 | | Day | Topics | Resources | Projects | 104 | |-----|---------|-----------|-----------| 105 | | 134-140 | • Docker
• Kubernetes
• CI/CD | 🎥 [TechWorld with Nana](https://www.youtube.com/c/TechWorldwithNana)
📘 [MLOps Zoomcamp](https://github.com/DataTalksClub/mlops-zoomcamp) | Containerized ML | 106 | | 141-147 | • Model Versioning
• Experiment Tracking
• ML Pipeline | 🎥 [Weights & Biases](https://www.youtube.com/c/WeightsBiases)
📘 [MLflow Docs](https://mlflow.org/docs/latest/index.html) | ML pipeline | 107 | 108 | #### Week 29-32: Production Deployment 109 | | Day | Topics | Resources | Projects | 110 | |-----|---------|-----------|-----------| 111 | | 148-154 | • AWS SageMaker
• Azure ML
• Google AI Platform | 🎥 [AWS Training](https://www.youtube.com/c/AmazonWebServices)
📘 [Cloud Certifications](https://aws.amazon.com/certification/) | Cloud deployment | 112 | | 155-161 | • Model Monitoring
• A/B Testing
• Model Governance | 🎥 [Full Stack Deep Learning](https://fullstackdeeplearning.com/)
📘 [ML System Design](https://github.com/chiphuyen/machine-learning-systems-design) | Monitoring system | 113 | 114 | ## 📺 Top YouTube Channels for AI Learning 115 | 116 | ### 🎥 General AI & ML 117 | | Channel | Focus Area | Best For | 118 | |---------|------------|----------| 119 | | [3Blue1Brown](https://www.youtube.com/c/3blue1brown) | Math & Concepts | Visual learners | 120 | | [Sentdex](https://www.youtube.com/c/sentdex) | Practical AI | Hands-on coding | 121 | | [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai) | AI Research | Latest developments | 122 | | [Yannic Kilcher](https://www.youtube.com/c/YannicKilcher) | Paper Reviews | Research understanding | 123 | | [Weights & Biases](https://www.youtube.com/c/WeightsBiases) | MLOps & DL | Production AI | 124 | 125 | ### 🎥 GenAI Specific 126 | | Channel | Focus Area | Best For | 127 | |---------|------------|----------| 128 | | [AI Coffee Break](https://www.youtube.com/c/AICoffeeBreak) | LLMs & Transformers | Latest GenAI trends | 129 | | [Assembly AI](https://www.youtube.com/c/AssemblyAI) | NLP & Speech | Practical implementations | 130 | | [Prompt Engineering](https://www.youtube.com/c/PromptEngineering) | Prompt Design | LLM applications | 131 | | [GenAI Crash Course](https://www.youtube.com/watch?v=nJ25yl34Uqw) | Crash Course | freeCodeCamp | 132 | 133 | ## 📚 Certifications Roadmap 134 | 135 | ### Entry Level 136 | 1. [AWS Machine Learning Specialty](https://aws.amazon.com/certification/certified-machine-learning-specialty/) 137 | 2. [TensorFlow Developer Certificate](https://www.tensorflow.org/certificate) 138 | 3. [Azure AI Fundamentals](https://learn.microsoft.com/en-us/certifications/azure-ai-fundamentals/) 139 | 140 | ### Advanced Level 141 | 1. [Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning) 142 | 2. [MLOps Engineering Certificate](https://www.coursera.org/professional-certificates/mlops-machine-learning-engineering) 143 | 3. [Google Cloud Professional ML Engineer](https://cloud.google.com/certification/machine-learning-engineer) 144 | 145 | ## 🎯 Project Portfolio Building 146 | 147 | ### Beginner Projects 148 | 1. Image Classification using CNNs 149 | 2. Sentiment Analysis using BERT 150 | 3. Time Series Prediction 151 | 4. Recommendation System 152 | 153 | ### Intermediate Projects 154 | 1. Custom Object Detection 155 | 2. Language Translation Model 156 | 3. Music Generation using RNNs 157 | 4. Face Recognition System 158 | 159 | ### Advanced Projects 160 | 1. Custom LLM Development 161 | 2. Multi-modal AI System 162 | 3. Autonomous Agent 163 | 4. Enterprise MLOps Pipeline 164 | 165 | ## 📝 Interview Preparation 166 | 167 | ### Technical Topics 168 | 1. Machine Learning 169 | - Algorithm selection 170 | - Model evaluation 171 | - Feature engineering 172 | 173 | 2. Deep Learning 174 | - Architecture design 175 | - Training optimization 176 | - Transfer learning 177 | 178 | 3. GenAI 179 | - Transformer architecture 180 | - Prompt engineering 181 | - RAG implementation 182 | 183 | 4. MLOps 184 | - Deployment strategies 185 | - Monitoring systems 186 | - Scaling solutions 187 | 188 | ### System Design 189 | 1. Large Scale ML Systems 190 | 2. Real-time Inference 191 | 3. Distributed Training 192 | 4. Data Pipeline Design 193 | 194 | ### Behavioral Questions 195 | 1. Project Leadership 196 | 2. Problem-solving 197 | 3. Team Collaboration 198 | 4. Technical Communication 199 | 200 | ## 🌟 Additional Resources 201 | 202 | ### Books 203 | 1. "Deep Learning" by Ian Goodfellow 204 | 2. "Designing Machine Learning Systems" by Chip Huyen 205 | 3. "Building Machine Learning Powered Applications" by Emmanuel Ameisen 206 | 207 | ### Newsletters 208 | 1. [Import AI](https://jack-clark.net/) 209 | 2. [The Batch](https://www.deeplearning.ai/the-batch/) 210 | 3. [ML News](https://www.linkedin.com/newsletters/ml-news-6824927594418544640/) 211 | 212 | ### Communities 213 | 1. [Hugging Face Discord](https://huggingface.co/join/discord) 214 | 2. [PyTorch Forums](https://discuss.pytorch.org/) 215 | 3. [Reddit r/MachineLearning](https://www.reddit.com/r/MachineLearning/) 216 | 217 | ## 🎉 Career Growth Path 218 | 219 | ### Entry Level (0-2 years) 220 | - Junior AI Engineer 221 | - ML Engineer I 222 | - Research Assistant 223 | 224 | ### Mid Level (2-5 years) 225 | - Senior AI Engineer 226 | - ML Platform Engineer 227 | - AI Research Scientist 228 | 229 | ### Senior Level (5+ years) 230 | - Lead AI Engineer 231 | - AI Architect 232 | - Principal Scientist 233 | - AI Research Director 234 | 235 | ## 💡 Final Tips 236 | 1. Build in public 237 | 2. Contribute to open source 238 | 3. Write technical blogs 239 | 4. Network with AI community 240 | 5. Stay updated with research 241 | 6. Focus on practical implementation 242 | 7. Document your learning journey 243 | --------------------------------------------------------------------------------