Natural Language Processing Roadmap for Freshers
A comprehensive 10-week learning plan to master NLP, text processing, and language models from scratch
This roadmap assumes 3-4 hours of daily study (2 hours learning + 1-2 hours practice)
Week 1-2: Python & NLP Fundamentals
| Day | Topics | Learn (hrs) | Practice (hrs) | Important Topics |
|---|---|---|---|---|
| Week 1: Python Basics for NLP | ||||
| Day 1 |
Python Introduction - Installation & Setup - Jupyter Notebooks - Basic Syntax |
2 | 1 | Python Environments, Variables |
| Day 2 |
Data Structures - Lists, Tuples - Dictionaries, Sets - String Operations |
2 | 1.5 | String Manipulation |
| Day 3 |
File Handling & APIs - Reading/Writing Files - REST APIs - JSON Handling |
2 | 2 | Text File Processing |
| Day 4 |
NumPy & Pandas - Arrays & DataFrames - Data Manipulation - Text Data Handling |
2.5 | 2 | Text Data Cleaning |
| Day 5 |
NLP Introduction - What is NLP? - Applications & Use Cases - NLP Pipeline Overview |
2.5 | 1.5 | NLP Applications |
| Day 6 |
Practice Day - Text Processing Project - API Integration |
1 | 3 | Regex Basics |
| Day 7 |
Review Day - Week 1 Concepts - Q&A Session |
1 | 2 | Common Text Processing Issues |
| Week 2: Essential NLP Concepts | ||||
| Day 8 |
Text Preprocessing - Tokenization - Lowercasing - Stopword Removal |
2.5 | 1.5 | Tokenization Techniques |
| Day 9 |
Advanced Text Cleaning - Stemming - Lemmatization - Spell Correction |
2.5 | 1.5 | Stemming vs Lemmatization |
| Day 10 |
Text Representation - Bag of Words - TF-IDF - N-grams |
2.5 | 1.5 | TF-IDF Calculation |
| Day 11 |
Math for NLP - Probability Basics - Linear Algebra Intro - Statistics for Text |
2.5 | 1.5 | Probability in NLP |
| Day 12 |
Practice Day - Text Preprocessing Project - TF-IDF Implementation |
1 | 3 | Scikit-learn Basics |
| Day 13 |
Review Day - Week 2 Concepts - Q&A Session |
1 | 2 | Concept Integration |
Week 3-6: Core NLP Techniques & Libraries
| Day | Topics | Learn (hrs) | Practice (hrs) | Important Topics |
|---|---|---|---|---|
| Week 3-4: NLP Libraries & Techniques | ||||
| Day 15 |
NLTK Library - Installation & Setup - Basic Functions - Corpus Access |
2.5 | 2 | NLTK Corpora |
| Day 16 |
spaCy Library - Installation & Setup - Pipeline Concepts - Comparison with NLTK |
3 | 2 | spaCy Pipelines |
| Day 17 |
Part-of-Speech Tagging - POS Concepts - Implementation in NLTK/spaCy - Applications |
3 | 2 | POS Tag Sets |
| Day 18 |
Named Entity Recognition - NER Concepts - Implementation - Evaluation Metrics |
2.5 | 2 | NER Tagging |
| Day 19 |
Dependency Parsing - Syntax Trees - Dependency Graphs - Applications |
2.5 | 2 | Tree Representations |
| Day 20 |
Practice Day - Build an NLP Pipeline - Text Analysis Project |
1 | 3 | Pipeline Optimization |
| Day 21 |
Review Day - Concepts Review - Q&A Session |
1 | 2 | Library Comparison |
| Week 5-6: Advanced NLP Techniques | ||||
| Day 22 |
Word Embeddings - Word2Vec - GloVe - FastText |
3 | 2 | Vector Semantics |
| Day 23 |
Text Classification - Naive Bayes - SVM for Text - Evaluation Metrics |
3 | 2 | Classification Metrics |
| Day 24 |
Sentiment Analysis - Techniques - Lexicon-based Approaches - Machine Learning Approaches |
2.5 | 2 | Sentiment Lexicons |
| Day 25 |
Text Similarity - Cosine Similarity - Jaccard Similarity - Semantic Similarity |
2.5 | 2 | Similarity Metrics |
| Day 26 |
Practice Day - Sentiment Analysis Project - Text Classification Project |
1 | 3 | Model Evaluation |
| Day 27-28 |
Review & Projects - NLP Concepts - Mini Projects |
1 | 4 | Project Deployment |
Week 7-10: Advanced NLP & Transformers
| Day | Topics | Learn (hrs) | Practice (hrs) | Important Topics |
|---|---|---|---|---|
| Week 7-8: Deep Learning for NLP | ||||
| Day 29 |
Neural Networks Basics - Perceptrons - Activation Functions - Backpropagation |
3 | 2 | Gradient Descent |
| Day 30 |
RNNs & LSTMs - RNN Architecture - LSTM Cells - Applications in NLP |
3 | 2 | Sequence Modeling |
| Day 31 |
Keras/TensorFlow/PyTorch - Basic Syntax - Building NLP Models - Training Process |
3 | 2 | Model Architecture |
| Day 32 |
Seq2Seq Models - Encoder-Decoder Architecture - Attention Mechanism - Applications |
3 | 2 | Attention Weights |
| Day 33 |
Practice Day - Build an RNN Model - Text Generation Project |
1 | 3 | Hyperparameter Tuning |
| Day 34 |
Review Day - Deep Learning Concepts - Q&A Session |
1 | 2 | Model Comparison |
| Week 9-10: Transformers & Deployment | ||||
| Day 35-37 |
Transformer Architecture - Self-Attention Mechanism - Transformer Blocks - Positional Encoding |
3 | 3 | Attention Calculations |
| Day 38-40 |
BERT & GPT Models - BERT Architecture - GPT Family - Fine-tuning Techniques |
3 | 3 | Transfer Learning |
| Day 41-44 |
Hugging Face Ecosystem - Transformers Library - Datasets Hub - Model Hub |
2 | 4 | Pipeline API |
| Day 45-50 |
Final Project & Deployment - End-to-End NLP System - Model Deployment - Performance Optimization |
2 | 3 | Production Considerations |
Key Recommendations
- Daily Practice: Work with text data and NLP libraries daily
- Projects: Build at least 5 complete NLP projects by the end
- Community: Join NLP communities like Hugging Face, spaCy, NLTK
- Stay Updated: Follow latest research papers and model releases
- Ethics First: Always consider ethical implications of your NLP applications