This guide will take you from whatever your current machine learning knowledge is to whatever you want to learn about machine learning using high-quality, free resources.
There are many greate machine learning resources on the internet, but the field is complex and growing so quickly it requires a resource just to navigate those resources. This guide serves this purpose.
This guide will always be free. You can support it by starring this repo and following me on X so I can share it with more people.
Understand the following:
- Everyone needs an understanding of AI. Even consumers will have to know how the technology they use affects their life. I'll include different road maps for the different levels of knowledge. I've written more in-depth about this here.
- Take your time. Machine learning is a complex field that is growing quickly. Don't rush the learning process. It will always be a good time to learn about AI.
- Your greatest learning resource is people. Very few people have a good grasp on all AI topics. The best way to learn about AI is from people who know about it and are sharing their knowledge. I've included a list of people to follow and newsletters to subscribe to for this purpose.
- AI is still being figured out. Even the most well-known AI researchers are trying to understand how AI will affect humanity. Don't automatically assume something someone is saying is truth, learn from many different people, and check sources.
Here are the 5 different paths to learning about machine learning this road map covers. If you think there are other paths of learning that I've missed, let me know.
- I'm a consumer and I want to know how ML will affect me. ML will fundamentally change the way consumers interact with their devices. They need to understand this interaction to know the privacy and safety implications of this change. This road map is simple and will help you understand that.
- I want to learn how to develop ML models and get into AI research. Machine learning math is the primary and most important skill for this along with a coding background. This road map is more intense and will take you through these skills.
- I want to learn the skills necessary for an ML engineer. A strong software development background with a ML math understanding is required to solve the problems ML engineers tackle. This road map is more intense and will take you through these skills.
- I'm a dev who wants to build applications using ML. The emergence of LLMs has led a lot of developers to become interested in how ML can be used in their apps. This road map will help you understand this.
- My company wants to use AI and I don't know how to get started. Every company can benefit from the use of AI but how this happens is determined by the problems that company is trying to solve and the data they have available.
Machine learning is a quickly evolving field. These are the resources I use to stay updated on developments and products.
X is the best place for any sort of tech news, updates, and learning. I've curated a list of people to follow to learn more about ML for you to use as a starting point. You can follow the list and pin it on your feed.
I suggest following all the people within this list as well as this will curate your 'For You' and 'Following' feeds and other features such as 'Top Articles'.
I've included people with differing viewpoints on AI because I think taking many opinions into account is the only way to learn.
Another great resources are newsletters. Here are high-quality newsletters that won't spam your inbox:
Don't forget to star this repo and following me on X to support this guide.
Last Updated January 2024
In order to comply with X Shopping's terms of service, this road map is also available in paperback. It'll cost as I'll have to send a print out of it to you. You can contact me for this on X.