/nlp_roadmap_in_2024

A complete roadmap to learn and explore nlp in just few time

Natural Language Processing roadmap in 2024

NLP or Natural Language Processing, is a field of artificial intelligence (AI) that deals with computers understanding and using human language. It's basically about giving machines the ability to read, understand, and respond to the way we communicate.

Learning Path to NLP Engineer

Overview

This guide provides a structured approach to becoming an NLP (Natural Language Processing) Engineer, focusing on Python, Machine Learning, and Deep Learning.

Why Learn These Skills?

Python

  • Simplicity: Easy to read and write.
  • Versatility: Applicable across various fields beyond NLP.

Machine Learning

  • Practicality: Powers recommendation systems, predictive text, and more.
  • Widespread Use: Integral to numerous real-world applications.

Deep Learning

  • Complex Problem Solving: Handles advanced tasks like understanding sentence context.
  • Innovation: Drives technologies like self-driving cars and voice assistants.

Hands-on with TensorFlow and PyTorch

  • Participate in coding projects and competitions to get hands-on experience with these frameworks.

How to Acquire These Prerequisites?

Python

  1. Online Tutorials and Courses: Platforms like Codecademy, Khan Academy, and W3Schools.
  2. Practice Coding: Solve problems on HackerRank to build familiarity.

Machine Learning

  1. Introductory Courses: Learn through Coursera and edX.
  2. Hands-On Projects: Work on projects like predicting house prices or image classification.

Deep Learning

  1. Deep Learning Courses: Fast.ai and Coursera's Deep Learning Specialization.
  2. Experiment with Frameworks: Implement models using TensorFlow and PyTorch.

Challenges and Tips

  1. Start Small: Begin with simple projects to build confidence.
  2. Consistent Practice: Regular practice is essential, similar to learning a new language.
  3. Join Communities: Engage with platforms like Stack Overflow and Reddit for support.
  4. Explore Real-World Examples: Apply knowledge to real-world scenarios for better understanding.

Practical Steps to NLP Engineering

Step 1: Text Cleaning

Before computers can understand our words, we need to clean up the messy text. This involves:

  • Mapping and Replacement: Changing certain words to make them easier to understand.
  • Correction of Typos: Fixing typographical errors.

Step 2: Text Preprocessing Level-1

Prepare the text for analysis, similar to preparing ingredients before cooking.

Step 3: Text Preprocessing Level-2

  • Bag of Words (BOW): Understanding word presence without considering order.
  • Term Frequency-Inverse Document Frequency (TF-IDF): Highlighting important words by balancing frequency and uniqueness.
  • Unigram, Bigram, and Ngrams: Breaking down sentences into smaller chunks (one word, two words, or more).

Step 4: Text Preprocessing Level-3

  • Word2Vec: Teaching the computer the meaning of words by context.
  • Average Word2Vec: Capturing the overall meaning of a sentence by averaging word vectors.

Step 5: Hands-on Experience on a Use Case

Choose a simple project, like creating a program that understands if a sentence is positive or negative.

Step 6: Exploring Deep Learning Models

  • Recurrent Neural Networks (RNN): Remembering past information while processing new words.
  • Long Short-Term Memory (LSTM): Grasping long-term dependencies in language.
  • Gated Recurrent Unit (GRU): Efficiently understanding language context with less complexity.

Step 7: Advanced Text Preprocessing

Handle more complex language nuances, such as idioms, subtle meanings, or sarcasm, to make the computer savvy enough to handle intricate language.

Step 8: Exploring Advanced NLP Architectures

  • Bidirectional LSTM RNN: Reading a sentence from both ends for better context understanding.
  • Encoders and Decoders: Tools for translating sentences from one language to another.
  • Self-Attention Models: Focusing on important parts of a sentence.

Step 9: Mastering Transformers

Transformers are supercomputers for NLP, handling complex language tasks efficiently.

Step 10: Mastering Advanced Transformer Models

  • BERT (Bidirectional Encoder Representations from Transformers): Understanding words by their context.
  • GPT (Generative Pre-trained Transformer): Generating new content, like writing essays.

Exploring NLP Frameworks and Libraries

LangChain, RAG, Transformers

  • LangChain: Streamlines the development of NLP applications by integrating various tools and models.
  • RAG (Retrieval-Augmented Generation): Combines retrieval-based and generation-based methods for better performance in NLP tasks.
  • Transformers: Provides state-of-the-art pre-trained models and tools for various NLP tasks.

Challenges and Tips

  1. Start Small: Begin with simple projects to build confidence.
  2. Consistent Practice: Regular practice is essential, similar to learning a new language.
  3. Join Communities: Engage with platforms like Stack Overflow and Reddit for support.
  4. Explore Real-World Examples: Apply knowledge to real-world scenarios for better understanding.

Conclusion

Embarking on this learning journey requires patience and persistence. By following this guide and continuously practicing, you'll be well on your way to becoming an NLP Engineer.

This README.md file provides a comprehensive roadmap and practical steps for aspiring NLP engineers, including foundational knowledge, hands-on projects, and advanced frameworks and tools.