For the past few months, I have been learning machine learning and data science. Below are some of the resources which have helped me the most as I’ve studied. This list is particular to me and my studies, and your mileage may vary. But I hope you find something of use.

I placed a star by the item in each category which I most recommend.

YouTube:

3Blue1Brown: Beautiful and simple math explanations. Good for big-picture ideas. Check out his Essence of Linear Algebra playlist. As of writing this, he has also started a series on neural networks.

Alexander Ihler: Detailed presentations on various machine learning concepts. Not an active channel, but the archive is excellent.

*Two Minute Papers: Simple and easy-to-understand distillations of research in ML, simulations, etc.

Siraj Raval: Various ML concepts covered. Energetic presenter for those days when you need it.

Sentdex: Extensive Python tutorials (including on machine learning).

Jake Vanderplas: Small channel. Contains simple data analysis walkthrough in Jupyter Notebooks

Podcasts:

*Linear Digressions: Short episodes on a new paper or idea. Excellent and fun.

Talking Machines: Long episodes covering a variety of topics. (Can be less beginner friendly but very interesting)

Super Data Science Podcast: Beginner friendly topics and conversations. High energy and motivating. Much of the content is designed to help new people enter the field.

This Week in Machine Learning & AI: Recent addition to the list. Various news and interviews.

Partially Derivative: Beginner friendly topics and conversations (No longer running, but archive is good)

Data Skeptic: Both long form and short episodes – content rich and very enjoyable to listen to.

Books:

*Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Geron (Amazon): Excellent overview of ML with scikit-learn. Best to be read once you know the basics of Python and some machine learning concepts.

Python for Data Analysis by Wes McKinney (Amazon): Clear and simple explanation of basic python data analysis with NumPy, Pandas, etc.

Introduction to Statistical Learning (Amazon, pdf).

Online courses:

*Jose Portilla’s Python for Data Science Bootcamp: Excellent for beginners (with some familiarity with Python). Walks students through the basics of using NumPy, seaborn, scikit-learn, etc.

Kirill Eremenko’s courses on Udemy.

Coursera also offers some courses that I think are good, but I prefer Udemy‘s interface and structure. A lot of people recommend Andrew Ng’s original ML course and his later Deep Learning Course on the Coursera platform. I found his courses to contain a lot of excellent content, but I don’t think I would use them as a starting point for the first introduction to machine learning.

Sites:

*Kaggle: Many machine learning competitions and datasets. Friendly community with lots of example code.

ChrisAlbon.com: Simple code-snippets for data science.

/r/MachineLearning: Machine Learning community – news, discussions, tutorials, etc.

/r/DataScience: Data science community – news, discussions, tutorials, etc.

/r/DataIsBeautiful: Beautiful visualizations.

Learning path outlines:

*Learning Machine Learning (30 min podcast episode): A helpful overview from Chris Albon of how to approach learning machine learning.

Analytics Vihya 2017 DS Learning Path: A very long and exhaustive article. Is useful for some timing and structural elements.

Jose Portilla’s How to Become a Data Scientist: A helpful big-picture take on how to become a data scientist with some excellent resources and book recommendations.

GitHub Repositories:

Aurélien Geron: Machine Learning with scikit-learn explanation and examples

*Jose Portilla: Plentiful resources on Python, Machine Learning, etc.

Other resources, tools, and advice:

Blogs:

I don’t follow any particular blogs on a regular basis. But if you love blogs, rushter on GitHub made a massive curated list of data science blogs.

Tools:

Misc. advice to beginners: