I am following the curriculum below for my machine learning scholar adventure. It’s a work in progress and the syllabus will be updated to reflect progress on the the path I am taking. The title of each resource links to the place where you can find it if you’d like to explore it as part of your machine learning journey.
Superb course which introduces you to metalearning i.e. how to learn more effectively.
Excellent course which helps you unlearn unhelpful beliefs/mindsets so that you can unleash the potential of your learning capability.
Excellent primer providing actionable intel from cutting-edge neuroscience and useful tools to help you maximise learning.
A comprehensive guide to design an intensive learning project from start to finish.
- Pandas – Explore the basics of using pandas to work with data. ✅
- Intro to machine learning – Build your first machine learning model. ✅
- Intermediate machine learning – Improve your models by learning how to clean and transform your data. ✅
- Intro to game AI and reinforcement learning – Learn the basics of making intelligent agents. ✅
- Data viz – Create visualisations to aid data investigation and showcase insights.
- Neural networks – Intuitive series describing the mechanics of how neural networks work. ✅
A basic tutorial which gives a high level overview of machine learning.
A gentle introduction which teaches you how to make your own neural network from the ground up using python and numpy.
Fantastic introduction to how generative adversarial networks work. You get to learn Pytorch basics simultaneously when making your GANs. For more info, see my review.
Understand deep learning using a top-down approach which encourages holisitic understanding of concepts and rapid experimentation. You can access the accompanying online course for free.
Build an intuitive understanding of probabilistic thinking.
Gain a foundation in linear algebra through interactive exercises.
Fantastic course covering the fundamentals of programming in python.
An introduction to using python for scientific programming.
Progress beyond basic python to intermediate level concepts.
Helpful tutorials to help you create simple applications using python.
A great primer on using the command line to make development much smoother.
Teaches how to use git to track changes in your projects effectively and how to use github to collaborate with others.
A great intro to cloud technology which explores the basics of various infrastructure configurations from private to hybrid to public clouds. Also touches upon docker use for stable environments and kubernetes for deployment sclaing and automation.