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If you’ve landed on this article, you might still not feel confident about applying your ML knowledge. And that’s completely understandable.

In our modern society, continuous learning is the only constant. That is why, after the rise of AI and ML, more and more people want to improve their skills and increase their confidence in these areas.

Whether you have a technical background or not, gaining a better understanding of AI and ML is very useful.

What is the biggest problem?

There are so many ML resources out there that it can be hard to find high-quality, relevant ones. That’s why in this article I’m sharing my personal favorite machine learning courses from top universities.

1. Generative AI for everyone by DeepLearning.ai

The first course had to be dedicated to the buzzword of the year: AI and LLMs. Designed by DeepLearning.AI and taught by Andrew Ng, “Generative AI for Everyone” is an excellent way to get started with GenAI, even without any prior knowledge in the field.

The course is designed to make the learning process of GenAI clear and smooth. It also explains how generative AI works and what it can (and cannot) do.

It includes practical tasks where you will learn to use generative AI to help with your daily work, get tips to improve your prompts and get the most out of LLMs, and delve into real-world applications and learn common use cases.

By the end, you will understand the concepts of Large Language Models, Deep Learning and Generative AI skills. You will be able to put your knowledge into practice and gain insight into the impact of AI on both business and society based on the three core elements of today’s ML world.

You will also learn how to apply generative AI to everyday tasks, making it immediately practical and useful. The course is available for free on Deeplearning.ai.

2. CS229: Machine Learning by Stanford

As a second option I recommend a classic – but still one of the best free ML courses out there. There are many versions and instructors, but as a personal recommendation I would take the courses of Andre Ng, who is widely regarded as one of the best instructors for machine learning.

It provides an easy-to-follow introduction to ML and statistical pattern recognition, covering a range of topics including supervised learning, unsupervised learning, learning theory, reinforcement learning, and control. It starts with the basics and ends with advanced concepts. This course is perfect for anyone who wants to gain a solid foundation in machine learning and end with a deep understanding of the domain.

All materials can be found via the following link and the corresponding YouTube videos can be found via the following link.

3. Machine Learning with Python by MIT

If your idea is to master ML with Python, a good option is to take the MIT course that is specifically designed with this specific goal in mind. It offers a complete introduction to ML algorithms and models, including deep learning and reinforcement learning, all through hands-on Python projects.

If you are new to the field, choosing a specific subdomain can be overwhelming. A better way to understand the whole and diverse world of ML is to start with a course that covers most of it. This will give you the chance to figure out what appeals to you the most. This course is perfect for beginners who want to explore the whole diverse world of machine learning.

You can find the course via the link below

4. Mathematics for Machine Learning by Imperial College London

If you’re scared of math, it’s time to face it. Imperial College of London has designated a course that aims to teach a foundational skill to anyone looking to build a career in machine learning.

Mathematics is fundamental to machine learning, and understanding mathematical principles is crucial for interpreting the results produced by ML algorithms. This specialization includes three courses:

• Linear Algebra
• Multivariable Calculus
• Principal Component Analysis

Each course lasts 4-6 weeks and covers the fundamental mathematical concepts needed to understand machine learning algorithms.

The course videos can be found for free on YouTube

5. Practical Deep Learning by fast.ai

This free course is designed for people with some programming experience who want to apply deep learning and ML to real-world problems. Developed by fast.ai, this course is designed to help people become industry-ready AI developers. It covers fundamental topics in Computer Vision and Natural Language Processing, among others, through a project-based approach that moves from basic to advanced concepts.

The main scope is based on:

• Building and training deep learning models for computer vision, natural language processing, tabular analysis, and collaborative filtering.
• Creating random forests and regression models.
• Implementing models.
• Using PyTorch, the world’s fastest growing deep learning library, along with popular libraries like fastai and Hugging Face.

You can find the course on the following website.

Complete

In summary, there are many resources to get started with ML and expand your current knowledge. Whether you are a beginner or someone with some programming experience, these courses provide a complete introduction to the field, starting with basic topics and ending with complex topics.

Josep Ferrer is an analytics engineer from Barcelona. He graduated in physics and currently works in the field of data science, applied to human mobility. He is a part-time content creator focused on data science and technology. Josep writes about everything related to AI, and covers the application of the ongoing explosion in the field.