Top 5 Free Machine Learning Courses to Level Up Your Skills

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If you’ve landed on this article, you might still not feel confident about applying your ML knowledge. And it is totally understandable.

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

Whether you’re a non-techie or have a technical background, gaining a deeper understanding of AI and ML will be highly beneficial.

The main problem?

There are so many ML resources that it can be difficult to find high-quality, relevant ones. That’s why, in this article, I’ll be 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 on the field.

The course aims to be clear and to smooth the process of learning GenAI, and will guide you through how generative AI works and what it can (and can’t) do.

It includes hands-on tasks where you’ll learn to use generative AI to help in daily work and receive tips to improve your prompts and get the most value out of LLMs. Furthermore, you’ll delve into real-world applications and learn common use cases.

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

You’ll also learn how to apply generative AI in everyday tasks, making it practical and useful right away. The course is available for free on Deeplearning.ai.

2. CS229: Machine Learning by Stanford

As a second option, I am recommending a classic – yet 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 ones led by Andre Ng, widely considered as one of the best machine learning instructors.

It offers an easy-to-follow introduction to ML and statistical pattern recognition, covering a range of topics such as supervised learning, unsupervised learning, learning theory, reinforcement learning, and control. It starts from the basics and ends up with advanced concepts. This course is perfect for anyone looking to get a solid foundation in machine learning and to end up with a deep understanding of the domain.

You can find all the material in the following link and its corresponding YouTube videos in the following one.

3. Machine Learning with Python by MIT

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

If you’re 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 part of it. Hence, you get the chance to find out what excited you the most. This course is perfect for beginners looking to explore the whole diverse world of machine learning.

You can find the course in the following link

4. Mathematics for Machine Learning by Imperial College London

If you are scared of maths, it is time to face them. Imperial Colege of London designated a course that aims to teach a basic skill for anyone aiming to build a career in machine learning.

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

• Linear Algebra
• Multivariate Calculus
• Principal Component Analysis

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

You can find the courses videos for free on YouTube

5. Practical Deep Learning by fast.ai

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

Its 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.
• Deploying 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 in the following website.

Wrapping Up

To summarize, there are a lot of resources to get started with ML and upskill your current knowledge. Whether you’re a beginner or someone with some coding experience, these courses offer a complete introduction to the field, starting from basic topics and ending up with complex ones.
 
 

Josep Ferrer is an analytics engineer from Barcelona. He graduated in physics engineering and is currently working in the data science field applied to human mobility. He is a part-time content creator focused on data science and technology. Josep writes on all things AI, covering the application of the ongoing explosion in the field.