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 foundations we can discover specific topics and expand our knowledge.

Hereunder my reading list for ML field.

  • “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 also important to complement this with online courses that may help you dig deeper in certain fields (NLP, RL, Vision, Decision Making…) depending on your interests.

💻 Code

Reading references is an important step. But in the field of Machine Learning, experimenting with real data and coding is necessary for a better understanding. Here is a list to start with, they contain a lot of real world examples.

🔖 Blogs

Some blogs that worth looking at from time to time.

🔭 Research papers


🛠 Tools

👨‍💻 Development

  • ⚛️ PyCharm is my go to editor.

  • 🐙 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 specialized 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

Short list of a few of my favorite books.

  • Sapiens: A Brief History of Humankind by Yuval Harari
  • The Black Swan: The Impact of the Highly Improbable by Nassim Nicholas Taleb
  • Homo Deus: A Brief History of Tomorrow by 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