1. About Natural Language Processing (NLP)
NLP is a subfield of Artificial Intelligence (AI) that focuses on enabling machines to understand, interpret, and generate human language. It powers applications like chatbots, sentiment analysis, machine translation, speech recognition, and text summarization.
Key Applications:
- Text Classification : Spam detection, sentiment analysis.
- Language Translation : Google Translate, DeepL.
- Chatbots & Virtual Assistants : Siri, Alexa, GPT-based models.
- Speech Recognition : Transcribing audio into text.
- Text Generation : Writing articles, stories, or code using AI.
2. Why Learn NLP?
- High Demand : NLP engineers are in demand across industries like tech, healthcare, and finance.
- Versatility : Used in applications like customer support, content generation, and data analysis.
- Automation : Automate tasks like document summarization, translation, and sentiment analysis.
- Research Opportunities : Contribute to cutting-edge research in AI and linguistics.
- Impactful Applications : Build tools that improve accessibility, communication, and decision-making.
3. Full Syllabus
Phase 1: Basics (Weeks 1–4)
- Introduction to NLP
- What is NLP?
- Key Terminology: Tokenization, Lemmatization, Stopwords, POS Tagging.
- Challenges in NLP: Ambiguity, Context Understanding, Language Variations.
- Programming Basics
- Learn Python (the most popular language for NLP).
- Libraries: NLTK, SpaCy, TextBlob.
- Text Preprocessing
- Tokenization: Splitting text into words or sentences.
- Normalization: Lowercasing, Removing Punctuation.
- Stopword Removal: Filtering out common words like “the” and “is.”
- Stemming & Lemmatization: Reducing words to their root forms.
- Exploratory Text Analysis
- Analyze word frequencies, n-grams, and word clouds.
- Visualize text data using libraries like Matplotlib and Seaborn.
Phase 2: Intermediate (Weeks 5–8)
- Feature Extraction
- Bag of Words (BoW): Representing text as word counts.
- TF-IDF (Term Frequency-Inverse Document Frequency): Weighting words based on importance.
- Word Embeddings: Word2Vec, GloVe, FastText.
- Text Classification
- Algorithms: Naive Bayes, Logistic Regression, Support Vector Machines (SVM).
- Applications: Spam Detection, Sentiment Analysis.
- Named Entity Recognition (NER)
- Identify entities like names, locations, dates, and organizations in text.
- Tools: SpaCy, NLTK.
- Part-of-Speech (POS) Tagging
- Assign grammatical tags to words (e.g., noun, verb, adjective).
- Tools: NLTK, SpaCy.
Phase 3: Advanced (Weeks 9–12)
- Transformer-Based Models
- Attention Mechanism: How models focus on relevant parts of text.
- Transformer Architecture: Encoder-Decoder Structure.
- Popular Models: BERT, GPT, T5.
- Text Generation
- Generate coherent text using models like GPT or T5.
- Applications: Chatbots, Content Creation.
- Machine Translation
- Translate text from one language to another.
- Tools: Google Translate API, Hugging Face Transformers.
- Sentiment Analysis
- Analyze emotions in text (positive, negative, neutral).
- Tools: VADER, TextBlob, Hugging Face.
Phase 4: Real-World Applications (Weeks 13–16)
- Deploying NLP Models
- Save and load models using libraries like Pickle or Joblib.
- Deploy models using Flask/Django (for APIs) or cloud platforms like AWS, GCP, or Azure.
- Speech-to-Text & Text-to-Speech
- Convert audio to text and vice versa.
- Tools: Google Speech-to-Text API, TTS libraries like gTTS.
- Summarization
- Extractive Summarization: Select important sentences from text.
- Abstractive Summarization: Generate concise summaries using models like BART.
- Ethics in NLP
- Bias in Language Models: Addressing gender, racial, and cultural biases.
- Privacy Concerns: Handling sensitive text data.
4. Projects to Do
Beginner Projects
- Spam Email Classifier :
- Classify emails as spam or not spam using text classification techniques.
- Dataset: Enron Email Dataset.
- Framework: Scikit-learn.
- Sentiment Analysis :
- Analyze the sentiment of movie reviews using NLP techniques.
- Dataset: IMDb Movie Reviews.
- Framework: NLTK, TextBlob.
- Word Cloud Generator :
- Create word clouds to visualize the most frequent words in a document.
- Tools: Matplotlib, WordCloud library.
Intermediate Projects
- Chatbot Development :
- Build a rule-based or ML-based chatbot using libraries like NLTK or Rasa.
- Dataset: Cornell Movie Dialog Corpus.
- Named Entity Recognition (NER) :
- Identify entities like names, locations, and organizations in news articles.
- Tools: SpaCy, NLTK.
- Language Translation :
- Build a simple translator using transformer-based models like Hugging Face.
- Framework: Hugging Face Transformers.
Advanced Projects
- Text Summarization :
- Summarize long documents using extractive or abstractive methods.
- Tools: Hugging Face Transformers (BART, T5).
- Speech-to-Text Application :
- Convert spoken language into written text using APIs like Google Speech-to-Text.
- Tools: Google Speech-to-Text API.
- Fake News Detection :
- Detect fake news using text classification and deep learning models.
- Dataset: Fake News Challenge Dataset.
5. Valid Links for Learning NLP
English Resources
- DeepLearning.AI (Andrew Ng) :
- Hugging Face :
- freeCodeCamp :
- Sentdex :
- StatQuest with Josh Starmer :
Hindi Resources
- CodeWithHarry :
- Thapa Technical :
- Hitesh Choudhary :
6. Final Tips
- Start Small : Begin with simple projects like sentiment analysis to understand the basics of NLP.
- Practice Daily : Spend at least 1 hour coding every day.
- Focus on Libraries : Master libraries like NLTK, SpaCy, and Hugging Face Transformers.
- Stay Updated : Follow blogs like Towards Data Science , Medium , or Analytics Vidhya for the latest updates.
- Join Communities : Engage with forums like Reddit’s r/LanguageTechnology or Discord groups for support.
100-Day Master Plan
| 1 | Introduction to NLP & Setting Up Environment | NLP Basics |
| 2 | Python Basics for NLP (NumPy, Pandas, Matplotlib) | Python Official Docs |
| 3 | Text Preprocessing (Tokenization, Lowercasing, Stopwords Removal) | Text Preprocessing |
| 4 | Stemming & Lemmatization | Stemming & Lemmatization |
| 5 | Regular Expressions for Text Cleaning | Regex Tutorial |
| 6 | Bag of Words (BoW) Model | Bag of Words |
| 7 | Term Frequency-Inverse Document Frequency (TF-IDF) | TF-IDF |
| 8 | Word Embeddings (Word2Vec, GloVe) | Word Embeddings |
| 9 | Contextualized Word Embeddings (ELMo, BERT) | Contextualized Embeddings |
| 10 | Language Models (n-grams, Unigram, Bigram) | Language Models |
| 11 | Part-of-Speech (POS) Tagging | POS Tagging |
| 12 | Named Entity Recognition (NER) | NER Tutorial |
| 13 | Dependency Parsing | Dependency Parsing |
| 14 | Sentiment Analysis (Lexicon-Based Methods) | Sentiment Analysis |
| 15 | Sentiment Analysis (Machine Learning Models) | ML Sentiment Analysis |
| 16 | Topic Modeling (Latent Dirichlet Allocation – LDA) | LDA Tutorial |
| 17 | Text Summarization (Extractive Methods) | Extractive Summarization |
| 18 | Text Summarization (Abstractive Methods) | Abstractive Summarization |
| 19 | Machine Translation (Seq2Seq + Attention) | Machine Translation |
| 20 | Neural Machine Translation (Transformer Architecture) | Transformers |
| 21 | Question Answering Systems | Question Answering |
| 22 | Chatbot Development (Seq2Seq Models) | Chatbot Tutorial |
| 23 | Text Generation (RNNs + LSTMs) | Text Generation |
| 24 | Text Classification (CNNs, RNNs, Transformers) | Text Classification |
| 25 | Language Modeling (GPT, GPT-2, GPT-3) | GPT Models |
| 26 | Transfer Learning for NLP (BERT, RoBERTa, DistilBERT) | Transfer Learning |
| 27 | Fine-Tuning Pretrained Models | Fine-Tuning BERT |
| 28 | Coreference Resolution | Coreference Resolution |
| 29 | Semantic Role Labeling | Semantic Role Labeling |
| 30 | Relation Extraction | Relation Extraction |
| 31 | Text Similarity & Paraphrase Detection | Text Similarity |
| 32 | Spell Checking & Correction | Spell Correction |
| 33 | Speech-to-Text Conversion | Speech-to-Text |
| 34 | Text-to-Speech Conversion | Text-to-Speech |
| 35 | Multilingual NLP | Multilingual Models |
| 36 | Cross-Lingual Transfer Learning | Cross-Lingual Learning |
| 37 | Explainable AI for NLP | Explainable AI |
| 38 | Bias & Fairness in NLP | Bias in NLP |
| 39 | Ethical Considerations in NLP | Ethics in NLP |
| 40 | Deployment of NLP Models (Flask API) | Deploy NLP Models |
| 41 | MLOps for NLP | MLOps Guide |
| 42 | Building Custom Tokenizers | Custom Tokenizers |
| 43 | Building Custom Language Models | Custom Models |
| 44 | Self-Supervised Learning for NLP | Self-Supervised Learning |
| 45 | Federated Learning for NLP | Federated Learning |
| 46 | Hyperparameter Tuning for NLP Models | Hyperparameter Tuning |
| 47 | Finalize and Document Your Projects | Documentation Best Practices |
| 48 | Spam Email Classifier (Naive Bayes) | Spam Detection |
| 49 | Sentiment Analysis on Movie Reviews (IMDb Dataset) | IMDb Dataset |
| 50 | Fake News Detection (NLP + ML) | Fake News Dataset |
| 51 | Text Summarization on News Articles | News Articles |
| 52 | Machine Translation (English to French) | Translation Dataset |
| 53 | Chatbot Development (Customer Support Bot) | Chatbot Tutorial |
| 54 | Text Generation (Poetry Generator) | Poetry Dataset |
| 55 | Named Entity Recognition (NER) on Legal Documents | Legal Documents |
| 56 | Topic Modeling on Research Papers | Research Papers |
| 57 | Question Answering System (SQuAD Dataset) | SQuAD Dataset |
| 58 | Text Classification (Spam vs Ham) | Spam Dataset |
| 59 | Sentiment Analysis on Twitter Data | Twitter Sentiment |
| 60 | Text Similarity for Duplicate Question Detection | Quora Dataset |
| 61 | Language Identification (Detecting Language from Text) | Language Detection |
| 62 | Speech Emotion Recognition (Audio Features) | Speech Emotion Dataset |
| 63 | Speech-to-Text Transcription | Speech Dataset |
| 64 | Text-to-Speech Synthesis | TTS Tutorial |
| 65 | Multilingual Sentiment Analysis | Multilingual Dataset |
| 66 | Cross-Lingual Transfer Learning (Translate English to Hindi) | Translation Dataset |
| 67 | Bias Detection in NLP Models | Bias in NLP |
| 68 | Build a Custom Spell Checker | Spell Correction |
| 69 | Build a Text Summarizer for Long Documents | Summarization Dataset |
| 70 | Build a Paraphrase Detection System | Paraphrase Dataset |
| 71 | Build a Hate Speech Detection Model | Hate Speech Dataset |
| 72 | Build a Multilingual Chatbot | Multilingual Chatbot |
| 73 | Build a Question Answering System for PDFs | PDF QA Dataset |
| 74 | Build a Text Classification Model for Legal Documents | Legal Documents |
| 75 | Build a Sentiment Analysis Model for Product Reviews | Product Reviews |
| 76 | Build a Text Generation Model for Story Writing | Story Dataset |
| 77 | Build a Named Entity Recognition System for Medical Texts | Medical Texts |
| 78 | Build a Machine Translation Model for Rare Languages | Rare Language Dataset |
| 79 | Build a Speech Emotion Recognition System | Speech Emotion Dataset |
| 80 | Build a Text-to-Speech System for Low-Resource Languages | Low-Resource TTS |
| 81 | Build a Cross-Lingual Transfer Learning Model | Cross-Lingual Dataset |
| 82 | Build a Bias Mitigation System for NLP Models | Bias Mitigation |
| 83 | Deploy an NLP Model as a REST API (FastAPI) | FastAPI Docs |
| 84 | Optimize NLP Models (Quantization, Pruning) | Optimization Techniques |
| 85 | Build a Custom Transformer for NLP | Custom Transformers |
| 86 | Build a Multimodal Model (Image + Text) | Multimodal Models |
| 87 | Build a Self-Supervised Learning Model for NLP | Self-Supervised Learning |
| 88 | Build a Federated Learning Model for NLP | Federated Learning |
| 89 | Build a Large-Scale Language Model (GPT-like) | GPT Models |
| 90 | Build a Real-Time Speech-to-Text System | Real-Time Speech |
| 91 | Build a Text Classification Pipeline for Social Media | Social Media Dataset |
| 92 | Build a Dialogue State Tracking System for Conversational AI | Dialogue Dataset |
| 93 | Build a Cross-Domain Sentiment Analysis Model | Cross-Domain Dataset |
| 94 | Build a Text Style Transfer Model (Formal to Informal) | Style Transfer |
| 95 | Build a Code-Switching Detection System | Code-Switching Dataset |
| 96 | Build a Multi-Task Learning Model for NLP | Multi-Task Learning |
| 97 | Finalize and Document Your Projects | Documentation Best Practices |
| 98 | Reflect and Plan Next Steps | NLP Career Paths |
