https://github.com/tal7aouy/nlp-roadmap

Master NLP Roadmap 🛣️
https://github.com/tal7aouy/nlp-roadmap

deep-learning deep-neural-networks machine-learning nlp nlp-parsing

Last synced: about 3 hours ago
JSON representation

Master NLP Roadmap 🛣️

• Host: GitHub
• URL: https://github.com/tal7aouy/nlp-roadmap
• Owner: tal7aouy
• License: mit
• Created: 2024-03-23T21:29:49.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-03-23T21:30:14.000Z (8 months ago)
• Last Synced: 2024-03-23T22:28:15.127Z (8 months ago)
• Topics: deep-learning, deep-neural-networks, machine-learning, nlp, nlp-parsing
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# 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](https://www.nltk.org/book/) by Steven Bird, Ewan Klein, and Edward Loper 🐍.

  - [Coursera: Natural Language Processing Specialization](https://www.coursera.org/specializations/natural-language-processing) by DeepLearning.AI 🧠.

  - [Introduction to Natural Language Processing](https://www.coursera.org/learn/natural-language-processing) on Coursera 📘.

  - [Introduction to NLP](https://developers.google.com/machine-learning/guides/text-classification/) by Google 🖥️.

## Mathematics for NLP 🧮

- **Goals**: Understand linear algebra, probability, statistics, and their application in NLP algorithms.

- **Resources**:

  - [Linear Algebra on Khan Academy](https://www.khanacademy.org/math/linear-algebra) ➕.

  - [Probability and Statistics on MIT OpenCourseWare](https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/) 📊.

  - [Essence of linear algebra](https://www.3blue1brown.com/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](https://automatetheboringstuff.com/) 📖.

  - [Scikit-learn Documentation](https://scikit-learn.org/stable/documentation.html) 🛠️.

  - [Spacy 101: Everything you need to know](https://spacy.io/usage/spacy-101) 🗂️.

## Text Processing and Regular Expressions 📝

- **Goals**: Understand how to preprocess text data.

- **Resources**:

  - [Text Preprocessing in Python: Steps, Tools, and Examples](https://towardsdatascience.com/) 🔧.

  - [Regular Expressions for Natural Language Processing](https://www.regexone.com/) ⚙️.

## Machine Learning for NLP 🤖

- **Goals**: Master the use of machine learning models in NLP.

- **Resources**:

  - [Speech and Language Processing](https://web.stanford.edu/~jurafsky/slp3/) by Dan Jurafsky and James H. Martin 💬.

  - [Machine Learning on Coursera](https://www.coursera.org/learn/machine-learning) by Andrew Ng 🧑‍🏫.

## Deep Learning for NLP 🧠

- **Goals**: Dive into deep learning techniques used in NLP.

- **Resources**:

  - [CS224n: Natural Language Processing with Deep Learning](http://web.stanford.edu/class/cs224n/) 🏫.

  - [Deep Learning Specialization on Coursera](https://www.coursera.org/specializations/deep-learning) by DeepLearning.AI 👨‍🔬.

## Advanced Topics 🌐

- **Goals**: Explore advanced NLP topics such as transformers, BERT, GPT.

- **Resources**:

  - [The Illustrated Transformer](http://jalammar.github.io/illustrated-transformer/) 🖼️.

  - [Hugging Face Transformers Documentation](https://huggingface.co/docs/transformers/index) 🤗.

## Projects and Hands-On Experience 👨‍💻

- **Goals**: Apply your knowledge by working on real-world NLP projects.

- **Resources**:

  - [Kaggle Competitions](https://www.kaggle.com/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](https://arxiv.org/) 📜.

  - [ACL Anthology](https://www.aclweb.org/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](https://www.aclweb.org/portal/content/ethics-nlp-research) by ACL 📖.

  - [Data Ethics on Coursera](https://www.coursera.org/learn/data-ethics) by the University of Michigan 🏛️.

  - [Fairness and Abstraction in Sociotechnical Systems](https://dl.acm.org/doi/10.1145/3287560.3287598) by Selbst et al. 📄.

  - [Language Technology Ethics](https://ethical.institute/mlexplain.html) by the Ethical AI Institute 🏥.