Welcome to the Machine Learning Road Map-Turbo. This is the fastest, high-quality road map to get up to speed on machine learning fundamentals. It teaches you the prerequisites and machine learning fundamentals necessary to understand how machine learning works and build with it. The goal is to quickly get to a point where you can comfortably explore machine learning topics on your own. Many other road maps are more comprehensive, this one is purposefully streamlined.
These resources are aggregated from the best ML educators. I've linked to the authors as much as possible. Please support them. Feedback/suggestions/corrections are always welcome and appreciated.
If you're less interested in the technical details of machine learning and want to know more about how machine learning will affect you as a consumer, I've written an article just for that. You can also check out the Google AI Essentials Course learn how to use generative AI to boost your productivity.
This road map will be updated as new learning resources are created and new ML topics emerge. Let's get started!
If you're looking for the previous road map, you can find it here.
- Machine Learning will impact everyone's life. It's a new paradigm of computing that will completely change the way consumers expect their devices to work.
- Machine learning is a rapidly developing field. There are many complex fields within machine learning. Take it slow and don't expect to become an expert in it all.
- The best way to understand machine learning is to learn from others who understand the topics you want to know more about. I've created a list of accounts to follow on X. I've also aggregated a list of newsletters, blogs, and channels I find helpful to stay updated.
These prerequisites contain a mixture of math and programming concepts. Feel free to skip things you already understand.
| Topic | Source | Author |
|---|---|---|
| Programming | ||
| General Programming | CS50 | Harvard |
| Python | Intro to Python (For Beginners) | Harvard |
| Google's Python Class (Refresher) | ||
| NumPy | NumPy Tutorial | |
| Pandas | Pandas Course | Kaggle |
| Math | ||
| Algebra | Algebra Curriculum | Khan Academy |
| Linear Algebra | Linear Algebra Curriculum | Khan Academy |
| Probability | Uncertainty Section of CS50 | Harvard |
| Calculus | Derivatives/Partial Derivatives | Khan Academy |
| Gradients | Khan Academy | |
| Backpropagation Visualization | ||
| Tools | ||
| Version Control | Learn How to Use Git | Open Source Git Community |
| Github Tutorial | GitHub | |
| Terminal | Learn Shell | learnshell.org |
This is the main material. Complete these to understand machine learning fundamentals:
| Topic | Source | Author |
|---|---|---|
| Intro | 20 Min Introduction to Machine Learning | |
| Fundamentals | Machine Learning Crash Course |
High-quality resources to explore more advanced topics that are helpful for machine learning:
All of these are must-subscribes:
For a list of almost all the available ML YouTube Courses check out this repo by Dair AI.
I've aggregated a list of cloud providers that offer a free tier for training machine learning models. Anyone can get started with ML- you don't need a powerful local machine. If anything is incorrect, reach out to me on X so I can make the fix. If there is a cloud computing platform that I've missed, also let me know.
| Resource | Details |
|---|---|
| Top Choices | |
| Google Colab | Offers free access to GPUs (usually NVIDIA T4 or P100) and TPUs with limited usage time and resources. Excellent for small projects and experimentation. |
| Kaggle Notebooks | Provides 30 hours/week of GPU usage (NVIDIA Tesla P100 or T4) for free. It's a good option with access to Kaggle's datasets and community. |
| Other Options | |
| Lightning AI | Offers one free studio with 22 GPU hours and is pay-as-you-go after that. |
| Google Cloud Platform | Offers $300 in free credits to new users. |
| Amazon SageMaker | Provides a free tier with limited access to various machine learning resources. |
| Paperspace Gradient | Offers a free community tier with access to limited GPU resources for experimentation and learning. |
Don't forget to star this repo and follow me on X to support this guide. Please support the authors of these resources by following them at the links I included. You can also find them in my ML on X list.
If any information is missing, you are the author of a resource and you'd like it removed, or any other general feedback send me a message to let me know.