*“[Overchoice takes place when] the advantages of diversity and individualization are canceled by the complexity of buyer’s decision-making process.”*

— From Alvin Toffler, *Future Shock*, 1971

The amount of information available on the Internet goes beyond measure. That’s great if you’re an expert who masters the big picture and knows how to navigate in deep waters, but it’s an overchoice nightmare if you’re starting your learning journey.

In this page, you’ll find the list of resources that most helped me learn Machine Learning and Data Science. This list is organized by subject. The choice of subjects was based on several job descriptions for Machine Learning Engineer and Data Scientist positions.

**Machine Learning**

- Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow [Book]
- Machine Learning [Online Course]
- An Introduction to Statistical Learning [Free Book]

**Data Science**

- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython [Book]
- Kaggle [Online Competitions]
- Python Data Science Handbook [Free Book]

**Deep Learning**

- Deep Learning with Python [Book]
- Deep Learning [Online Course]
- Practical Deep Learning For Coders [Online Course]

**Reinforcement Learning**

- Reinforcement Learning: An Introduction [Free Book]
- Reinforcement Learning Course [Online Course]
- Denny Britz’s Repository [GitHub]

You can find a more comprehensive list of resources in my posts ‘3 Skills to Become a Successful Data Scientist‘ and ‘45 Python Libraries Every Machine Learning Engineer Should Know‘.