Roadmap for Natural Language Processing (NLP) A-Z.

Rahul Tiwari
2 min readJun 8, 2024

--

Phase 1: Foundations

  1. Mathematics and Statistics:
  • Linear Algebra: Vectors, Matrices, Eigenvalues/Eigenvectors
  • Calculus: Derivatives, Integrals, Gradient Descent
  • Probability and Statistics: Distributions, Bayesian Inference, Hypothesis Testing

2. Programming:

  • Python: Basic syntax, Data Structures, OOP
  • Libraries: NumPy, Pandas, Matplotlib/Seaborn for data manipulation and visualization

Phase 2: Introduction to NLP

  1. Basic Concepts
  • Text Processing: Tokenization, Lemmatization, Stemming
  • Regular Expressions: Pattern matching in text

2. Core NLP Tasks

  • Sentiment Analysis
  • Named Entity Recognition (NER)
  • Part-of-Speech (POS) Tagging
  • Text Classification

3. Key Libraries

  • NLTK: Basics of text processing.
  • SpaCy: Advanced text processing, NER, POS tagging.
  • TextBlob: Simplified text processing.

4. Data Preprocessing

  • Handling Text Data: Cleaning, Normalization, Removing Stopwords.
  • Feature Extraction: Bag of Words (BoW), TF-IDF, Word Embeddings.

Phase 3: Intermediate NLP

  1. Machine Learning Basics
  • Supervised Learning: Regression, Classification.
  • Unsupervised Learning: Clustering, Dimensionality Reduction.

2. NLP Models

  • Traditional Models: Naive Bayes, SVM, Decision Trees.
  • Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC.

3. Deep Learning Basics

  • Neural Networks: Basics, Activation Functions, Backpropagation.
  • Frameworks: TensorFlow, PyTorch basics.

4. Advanced Feature Extraction

  • Word Embeddings: Word2Vec, GloVe, FastText.
  • Document Embeddings: Doc2Vec

Phase 4: Advanced NLP Techniques

  1. Sequence Models
  • Recurrent Neural Networks (RNNs): LSTM, GRU
  • Sequence-to-Sequence Models: Encoder-Decoder architectures

2. Attention Mechanisms

  • Attention Mechanisms: Introduction, Self-Attention
  • Transformers: Understanding the architecture, BERT, GPT

3. Advanced Applications

  • Machine Translation
  • Text Summarization
  • Question Answering
  • Dialogue Systems

4. Transfer Learning in NLP

  • Pre-trained Models: BERT, GPT-2/3, RoBERTa.
  • Fine-Tuning Pre-trained Models for specific tasks.

Phase 5: Expert Level

  1. Research and Development
  • Reading NLP Research Papers
  • Implementing State-of-the-Art Models
  • Experimentation and Hyperparameter Tuning

2. Advanced Deep Learning Techniques

  • Reinforcement Learning for NLP
  • Generative Models: GANs, VAEs

3. Scalability and Optimization

  • Deploying NLP Models: Serving models, API integration
  • Optimizing Performance: Quantization, Pruning, Distillation

4. Ethics and Fairness in NLP

  • Bias in NLP models
  • Ethical considerations and responsible AI.

Phase 6: Practical Experience and Projects

  1. Capstone Projects
  • Sentiment Analysis on social media data
  • Chatbot Development
  • Real-time Language Translation System
  • Summarization of News Articles

2. Contributions and Networking

  • Contributing to open-source NLP projects
  • Participating in NLP competitions (e.g., Kaggle, Hackathons)
  • Engaging with the NLP community (Conferences, Meetups).

Recommended Resources

Books:

  • Speech and Language Processing” by Jurafsky and Martin
  • Natural Language Processing with Python” by Bird, Klein, and Loper
  • Deep Learning for Natural Language Processing” by Palash Goyal et al.

Online Courses:

  • Coursera: Natural Language Processing Specialization by Deeplearning.ai
  • edX: Natural Language Processing with Python
  • Udacity: Natural Language Processing Nanodegree

Websites:

  • Towards Data Science
  • Medium (NLP Topic)
  • ArXiv.org for latest research papers

Communities:

  • Stack Overflow
  • NLP Slack groups and forums.

Conclusion:

This roadmap provides a structured approach from foundational knowledge to advanced expertise in NLP. Each phase builds on the previous one, ensuring a comprehensive understanding of both the theoretical and practical aspects of NLP.

--

--