The Fastest Way to Get Up to Speed on Machine Learning Fundamentals for Free

Announcing the ML Road Map-Turbo

Everyone needs to understand machine learning. ML is a paradigm shift in computing that will affect everyone through the technology they use. To make understanding ML easier, I’ve been searching for the best machine learning educational resources over the past few months. My goal was to organize the many resources available and piece them together to help anyone gain a high-quality ML education for free.

I was especially keen on streamlining the learning process so anyone can get up-to-speed on ML quickly. Long guides and tutorials tend to be bookmarked and ignored. A shorter, focused guide is more approachable, helps those learning get involved in the ML community and discussions faster, and facilitates learners digging deeper into their topics of interest more quickly.

Essentially, this guide includes only the topics absolutely necessary to understand ML fundamentals in the prerequisites and includes everything else in a separate section for learners to explore as needed.

Introducing the ML Road Map-Turbo

I’m excited to announce the ML Road Map-Turbo. It’s a streamlined rework of my previous road map with a more comprehensive list of educational resources. Here’s a brief walkthrough of the resources included:

• Things to know before you start learning ML.

• The prerequisites necessary to understand ML fundamentals.

• A course to learn machine learning fundamentals.

• Resources to dig into more advanced topics.

• A list of blogs, channels, and newsletters to learn from others.

• A list of free compute to get started training models.

It includes resources from the best machine learning educators, including some that write on Substack:

Sebastian Raschka, PhD

,

Devansh

,

Nathan Lambert

,

Cameron R. Wolfe, Ph.D.

,

Bilawal Sidhu

,

Damien Benveniste

,

Hesam Sheikh

,

elvis

,

Tivadar Danka

, and more. Thanks to all these educators for their excellent resources. Please support them!

Feedback is always appreciated. I’d love to know what you think.

The best way to support the road map is by starring the repo and sharing it with others.