Master NLP Roadmap 🛣️

Introduction to NLP 📚

• Goals: Grasp the foundational principles of NLP, understand its various applications in technology, and comprehend common terms like tokenization, lemmatization, stemming, etc.
• Resources:
  • Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper 🐍.
  • Coursera: Natural Language Processing Specialization by DeepLearning.AI 🧠.
  • Introduction to Natural Language Processing on Coursera 📘.
  • Introduction to NLP by Google 🖥️.

Mathematics for NLP 🧮

• Goals: Understand linear algebra, probability, statistics, and their application in NLP algorithms.
• Resources:
  • Linear Algebra on Khan Academy ➕.
  • Probability and Statistics on MIT OpenCourseWare 📊.
  • Essence of linear algebra video series by 3Blue1Brown 🔢.

Programming and Tools 💻

• Goals: Learn Python and libraries used in NLP.
• Resources:
  • Automate the Boring Stuff with Python 📖.
  • Scikit-learn Documentation 🛠️.
  • Spacy 101: Everything you need to know 🗂️.

Text Processing and Regular Expressions 📝

• Goals: Understand how to preprocess text data.
• Resources:
  • Text Preprocessing in Python: Steps, Tools, and Examples 🔧.
  • Regular Expressions for Natural Language Processing ⚙️.

Machine Learning for NLP 🤖

• Goals: Master the use of machine learning models in NLP.
• Resources:
  • Speech and Language Processing by Dan Jurafsky and James H. Martin 💬.
  • Machine Learning on Coursera by Andrew Ng 🧑‍🏫.

Deep Learning for NLP 🧠

• Goals: Dive into deep learning techniques used in NLP.
• Resources:
  • CS224n: Natural Language Processing with Deep Learning 🏫.
  • Deep Learning Specialization on Coursera by DeepLearning.AI 👨‍🔬.

Advanced Topics 🌐

• Goals: Explore advanced NLP topics such as transformers, BERT, GPT.
• Resources:
  • The Illustrated Transformer 🖼️.
  • Hugging Face Transformers Documentation 🤗.

Projects and Hands-On Experience 👨‍💻

• Goals: Apply your knowledge by working on real-world NLP projects.
• Resources:
  • Kaggle Competitions 🏆.
  • Create your own projects like sentiment analysis, chatbot, language translation 🛠️.

Staying Updated and Continued Learning 🔄

• Goals: Keep up with the latest in NLP and AI research.
• Resources:
  • ArXiv.org for NLP papers 📜.
  • ACL Anthology 📚.
  • Follow relevant researchers and practitioners on Twitter and LinkedIn 📱.

Ethics in NLP 🤝

• Goals: Understand the ethical implications of NLP applications.
• Resources:
  • Ethics in NLP Research by ACL 📖.
  • Data Ethics on Coursera by the University of Michigan 🏛️.
  • Fairness and Abstraction in Sociotechnical Systems by Selbst et al. 📄.
  • Language Technology Ethics by the Ethical AI Institute 🏥.