└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # 🍅 Hazrat Ali 2 | 3 | # 🍏 Porgrammer || Software Engineering 4 | 5 | # 🍑 NLP Engineer 6 | 7 | - Job Type: `Domain Specific`, `Linguists` 8 | - Opportunity: `Less Job Circular` 9 | 10 | --- 11 | 12 | **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. 13 | 14 | --- 15 | ## **Understand the Role of NLP Engineer** 16 | 17 | ### **What does an NLP Engineer do?** 18 | - Develop, fine-tune, and deploy NLP models for language understanding and generation. 19 | - Work on translation, sentiment analysis, chatbots, and summarization tasks. 20 | - Collaborate with data scientists and software engineers to integrate NLP systems into products. 21 | 22 | ### **Responsibilities** 23 | - Preprocessing text data (tokenization, stemming, lemmatization). 24 | - Build and optimize NLP models for specific tasks. 25 | - Deploying NLP solutions and integrating them into applications. 26 | - Researching and applying cutting-edge advancements in NLP. 27 | 28 | --- 29 | 30 | ## **Step 01: Programming and Python Libraries** 31 | 32 | ### **Why Learn Python for NLP?** 33 | - Python has robust libraries for text processing, NLP, and machine learning. 34 | 35 | ### **What to Learn?** 36 | - **Python Basics:** 37 | - Variables, data types, loops, conditionals, functions, and OOPs. 38 | - **Libraries:** 39 | - **Pandas/Polars:** DataFrame library. 40 | - **NLTK & SpaCy:** For text preprocessing. 41 | 42 | ### **Resources** 43 | - [Official Docs of Python](https://docs.python.org/3/tutorial/index.html) 44 | - [Python Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFf7qj4eoBtvALAB_Ml2rN0V) 45 | - [Basic to Advanced Python](https://aiquest.org/courses/become-a-python-developer/) 46 | - [Hugging Face Tutorials](https://huggingface.co/transformers/) 47 | 48 | --- 49 | 50 | ## **Step 02: Foundations of Natural Language Processing (NLP)** 51 | 52 | ### **Why Learn NLP Basics?** 53 | - Understanding foundational concepts is critical for building advanced models. 54 | 55 | ### **What to Learn?** 56 | - Tokenization, Stemming, Lemmatization. 57 | - Stopwords removal, Part-of-Speech tagging, Named Entity Recognition (NER). 58 | - Bag of Words, TF-IDF. 59 | - Word Embeddings (Word2Vec, GloVe, FastText). 60 | 61 | ### **Resources** 62 | - [Natural Language Toolkit Docs](https://www.nltk.org/) 63 | - [SpaCy Tutorials](https://spacy.io/) 64 | - [NLP Videos - Machine Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFfWY36nDJDlI26jXwInSm8f) 65 | 66 | --- 67 | 68 | ## **Step 03: Machine Learning for NLP** 69 | 70 | ### **Why Learn ML for NLP?** 71 | - Classical ML techniques are the basis for many NLP tasks. 72 | 73 | ### **What to Learn?** 74 | - Text Classification (Naive Bayes, SVM). 75 | - Sentiment Analysis, Topic Modeling (Latent Dirichlet Allocation). 76 | - Feature Engineering for Text Data. 77 | 78 | ### **Resources** 79 | - [NLP Videos - Machine Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFfWY36nDJDlI26jXwInSm8f) 80 | - [NLP Module](https://aiquest.org/courses/data-science-machine-learning/) 81 | - **Libraries:** 82 | - **NLTK & SpaCy:** For text preprocessing. 83 | - **Scikit-learn:** For classical machine learning tasks. 84 | --- 85 | 86 | ## **Step 04: Deep Learning for NLP** 87 | 88 | ### **Why Learn Deep Learning for NLP?** 89 | - Powers advanced NLP models for understanding and generating text. 90 | 91 | ### **What to Learn?** 92 | - Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), GRU. 93 | - Transformer Architectures (BERT, GPT, T5). 94 | - Sequence-to-Sequence Models (Seq2Seq, Attention Mechanisms). 95 | - Fine-tuning Pre-trained Models for Custom Tasks. 96 | 97 | ### **Resources** 98 | - [Deep Learning Playlist (ANN, RNN, LSTM, GRU, Transformers)](https://www.youtube.com/playlist?list=PLKdU0fuY4OFdFUCFcUp-7VD4bLXr50hgb) 99 | - [Hugging Face Tutorials](https://huggingface.co/transformers/) 100 | - [Basic to Advanced DL & GenAI](https://aiquest.org/courses/deep-learning-and-generative-ai/) 101 | - **Libraries:** 102 | - **NLTK & SpaCy:** For text preprocessing. 103 | - **Hugging Face Transformers:** For state-of-the-art NLP models. 104 | - **TensorFlow/PyTorch:** For custom deep learning-based NLP solutions. 105 | --- 106 | 107 | ## **Step 05: Generative Models** 108 | 109 | ### **Why Learn Generative Models?** 110 | - Generative models drive content creation in text, audio, and more. 111 | 112 | ### **What to Learn?** 113 | - **Variational Autoencoders (VAEs):** 114 | - Applications in text generation and compression. 115 | - **Transformers:** 116 | - GPT, DALL-E, T5. 117 | - **Fine-Tuning and Custom Training:** 118 | - Domain-specific adaptations of pre-trained models. 119 | 120 | ### **Resources** 121 | - [Generative AI Guide](https://huggingface.co/models) 122 | - [LangChain](https://python.langchain.com/docs/introduction/) 123 | - [Generative AI Course](https://aiquest.org/courses/deep-learning-and-generative-ai/) 124 | - [Stable Diffusion](https://github.com/CompVis/stable-diffusion) 125 | 126 | ----------------------------------------------- 127 | ## **Step 06: Learn GitHub** 128 | - GitHub is a crucial platform for version control and collaboration. 129 | - Enables you to showcase your projects and build a portfolio. 130 | - Facilitates teamwork on data science projects. 131 | 132 | ### **What to Learn?** 133 | - **Git Basics:** 134 | - Version control concepts, repositories, branches, commits, pull requests. 135 | - **GitHub Skills:** 136 | - Hosting projects, collaboration workflows, managing issues. 137 | - **Best Practices:** 138 | - Writing READMEs, structuring repositories, using `.gitignore` files. 139 | 140 | ### **Resources** 141 | - [Complete GitHub for NLP Engineers](https://www.youtube.com/playlist?list=PLKdU0fuY4OFcK__Q5tjqZY5mSx_u7ghUx) 142 | - Use GitHub to practice hosting Python, SQL, and machine learning projects. 143 | 144 | ----------------------------------------------- 145 | ## **Step 07: SQL** 146 | 147 | ### **Why Learn SQL?** 148 | - Essential for querying, extracting, and joining data from relational databases. 149 | - Used to preprocess and prepare data before modeling. 150 | 151 | ### **What to Learn?** 152 | - Basics: SELECT, INSERT, UPDATE, DELETE. 153 | - Intermediate: Joins (INNER, LEFT, RIGHT, FULL), subqueries. 154 | - Advanced: Window functions, CTEs (Common Table Expressions), and query optimization. 155 | 156 | ### **Resources** 157 | - [SQL Learning Playlist](https://www.youtube.com/playlist?list=PLKdU0fuY4OFduhpa23Wy5fRv6SGxp2ho0) 158 | - [Programming with Mosh - SQL Playlist](https://youtu.be/7S_tz1z_5bA) 159 | - Tools like MySQL Workbench, SQLite, or PostgreSQL. 160 | 161 | --- 162 | ## **Step 08: Projects** 163 | 164 | ### **Why Work on Projects?** 165 | - Projects showcase your ability to apply NLP techniques in real-world scenarios. 166 | 167 | ### **Ideas for Projects** 168 | 1. Build a sentiment analysis tool for customer reviews. 169 | 2. Create a chatbot using Transformer models. 170 | 3. Design an automatic summarizer for news articles. 171 | 4. Fine-tune BERT for a domain-specific NER task. 172 | 173 | ### **Where to Find Data?** 174 | - [Kaggle](https://www.kaggle.com/datasets) 175 | - [Hugging Face Datasets](https://huggingface.co/datasets) 176 | 177 | --- 178 | 179 | ## **Final Note: Workflow Integration** 180 | 1. Preprocess text data using tools like NLTK or SpaCy. 181 | 2. Train models using Scikit-learn, TensorFlow, or PyTorch. 182 | 3. Fine-tune Transformer models for advanced NLP tasks. 183 | 4. Deploy and integrate NLP models into applications. 184 | 185 | By following this roadmap, you’ll develop the skills needed to become a successful `NLP Engineer`. 186 | 187 | --- 188 | # Recomended Courses at aiQuest Intelligence 189 | 1. [Basic to Advanced Python](https://aiquest.org/courses/become-a-python-developer/) 190 | 2. [Machine Learning Concepts](https://aiquest.org/courses/data-science-machine-learning/) 191 | 3. [Advanced Deep Learning for NLP & Generative AI](https://aiquest.org/courses/deep-learning-and-generative-ai/) 192 | 193 | *`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! 194 | 195 | --- 196 | 197 | ## About the Author 198 | **Hazrat Ali** 199 | - 🌐 [LinkedIn Profile]() 200 | - 🎓 Programmer || Software Engineering 201 | 202 | --- 203 | 204 | ## Other Roadmaps 205 | [Read More Roadmaps]() 206 | 207 | --------------------------------------------------------------------------------