Useful resources

A collection of tools, guides, and courses that I found useful or wish I had found earlier! The topics include Computer Science, Machine Learning, Machine Learning applied to Medicine, Deep Learning and Reinforcement Learning.

General

• HackerNews Search [Search Engine] Incredibly useful for finding new resources
• Awesome list [List of Resources] Curated list of awesome lists

New to programming?

• Harvard CS50 [MOOC] If you want to learn how to build stuff (think apps, websites, startups. etc.) then use this to start your journey.
• Learn Python for DataScience [MOOC] If you want to learn Data Science (think research projects, extra job skills, etc.) then use this to start your journey.

New to machine learning?

If you don’t yet know how to program I’d definitely start with the courses mentioned above. Then choose one of the following:

• Fast.ai Deep Learning [MOOC] “Learn how to build state of the art models without needing graduate-level math — but also without dumbing anything down”. I would highly recommend this course, although they suggest having at least one year of python beforehand.
• Andrew Ng’s Machine Learning [MOOC] Arguably the most popular machine learning course online. It with the fundamentals, as opposed to the top-down approach of Fast.ai.

Programming

• Learn X in Y minutes [Guide] Summaries of programming languages.

Python

• Codecademy [Interactive tutorials] Start here
• Automate the boring stuff [Textbook] Useful for reference
• Beginner’s cheat sheets [Cheat sheet]

Computer Science

• Teach Yourself Computer Science [List of Resources] “All the resources you need to give yourself a world-class computer science education.”

Machine Learning

• A Visual Introduction to Machine Learning [Guide]
• Dive into machine learning [List of Resources]
• Siraj [Videos] Applied introduction to the different types of machine learning
• Scikit-learn choosing the right algorithm [Cheat sheet]
• Andrew Ng’s Machine Learning [MOOC] Arguably the most popular machine learning course online. It starts with the fundamentals, as opposed to the top-down approach of Fast.ai
• Fast.ai machine learning [MOOC]
• Machine learning mastery [Guides]
• Superintelligence: Paths, Dangers, Strategies by Nick Bostrom [Book]
• State of the art results for machine learning problems
• 100 Pandas puzzles [Guides]
• 100 NumPy exercises [Guides]
• Stanford machine learning cheatsheets [Cheat sheets] More theoretical than applied but very high quality

Maths for Machine Learning

• Review of Probability Theory [Cheat sheet]
• Linear Algebra review and reference [Cheat sheet]
• Mathematics for Machine Learning [Textbook]

Machine Learning in Medicine

• Review paper on deep learning, genomics, and precision medicine [Paper]
• Medical Data for Machine Learning [Datasets]
• Awesome Bioinformatics libraries and software [List] Tools for data pre-processing, handling and visualisation.

Deep Learning

• 3Blue1Brown [Videos] Visualisation of how neural networks work, gradient descent, backpropagation and some of the maths involved.
• Fast.ai Deep Learning [MOOC] “Learn how to build state of the art models without needing graduate-level math — but also without dumbing anything down”. I would highly recommend this course, although they suggest having at least one year of python beforehand.
• Andrew Ng’s Deep learning [MOOC]
• The Deep Learning Book by Ian Goodfellow [Textbook]
• Cheat sheets

Deep Reinforcement Learning

• Spinning Up as a Deep RL Researcher [Guide] “… educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning”
• Deep Reinforcement Learning: Pong from Pixels [Blog Post] Fantastic post by Karpathy guiding you from supervised learning to policy gradient methods.
• UCL Course of Reinforcement Learning by David Silver [Course] Videos and lecture notes on reinforcement learning from theory to practice. Works well with the textbook below.
• Reinforcement Learning: An introduction by Sutton and Barto [Textbook] The textbook on reinforcement learning.