Table of Contents

Last update: Sep 28, 2019

👨‍💻 Self-educational approach

📘 Basics

An efficient way to learn is to get few solid references and go through chapter by chapter to get a good understanding of the subject. After building the foundations, we can discover specific topics and expand our knowledge.

Hereunder my reading list for Machine Learning foundations.

  • “Pattern Recognition and Machine Learning” (PRML) by Christopher Bishop - Download here
  • The “Deep Learning Book” by Ian Goodfellow, Yoshua Benign and Aaron Courville is a great detailed introduction to Deep Learning - Available online

    For longer reads:

  • “Machine Learning A Probabilistic Perspective” by Kevin Murphy

  • “The Elements of Statistical Learning” by Hastie, Tibshirani and Friedman


It is important to complement this list with online courses to get a deeper understanding of certain fields (NLP, RL, Vision, Decision Making…) depending on your interests.

💻 Code

In the field of Machine Learning, experimenting with real data and coding is necessary for a better understanding of different concepts. Here is a list of links to different code examples and repositories.

🔖 Blogs

🔭 Research papers


🛠 Tools

👨‍💻 Development

  • ⚛️ PyCharm favorite IDE.

  • 🐙 Gitkraken an amazing Git GUI client.

  • 📝 Notion for notes and to-do lists.

💫 Machine Learning workspace

  • 🖥 The ML workspace is an all-in-one web-based IDE for machine learning and data science. It is very simple and convenient for ML experiments and development.

  • 🐳 Docker to create customized containers for specific projects.

📚 Non-technical Books

List of some of my favorite books.

  • Sapiens: A Brief History of Humankind, Yuval Harari
  • The Black Swan: The Impact of the Highly Improbable, Nassim Nicholas Taleb
  • Homo Deus: A Brief History of Tomorrow, Yuval Harari
  • Les jeux sont faits, Jean-Paul Sartre
  • Voyage au bout de la nuit, Céline
  • Les particules élémentaires, Michel Houellebecq
  • The Grapes of Wrath, John Steinbeck