6 free Machine Learning courses you can take in 2025

Machine Learning (ML) is no longer just a futuristic concept, it’s shaping industries and transforming our everyday lives. From fraud detection in banking systems to self-driving cars, ML is at the core of modern technology. As the reliance on data-driven decision-making increases, the demand for skilled ML engineers continues to grow. This makes machine learning, at the centre of technological evolution, quite a rewarding field.

If you’re hesitant to get started in Machine Learning (ML) due to the misconception that ML requires an advanced degree or expensive courses, perhaps this article will change your mind. While formal education is an added advantage, practical skills and experience are just as valuable. 

There are a ton of quality free courses available online that can help you build a solid foundation in Machine Learning, so financial constraints should not be a problem anymore. We’ve carefully curated six free machine learning courses among the best to help you kick-start your journey without breaking the bank.

| 1 | Machine Learning for Everybody

This course is a free YouTube resource provided by the reputable Freecodecamp, and taught by a renowned Physicist and Engineer, Kyliw Ying. It's a brief 3-hour course that introduces you to the concept and basics of Machine learning. It covers, training Models, how to prepare data, KNN Implementation, naive Bayes implementation, logistic Regression, Log Regression implementation, Neural Networks, TensorFlow, and so much more. This is a good place to get started as a beginner, so you understand the fundamentals of Machine Learning.

| 2 | Machine Learning Crash Course

Google also provides a free Machine Learning crash course. The course requires familiarity with Python, NumPy, algebra, linear algebra, and statistics. These are necessary for fully understanding Machine Learning concepts. In this course, you'll learn Machine Learning concepts, engineering, applying Machine Learning systems to the real world, and how to build Machine Learning models using the TensorFlow framework.

| 3 | CS229: Machine Learning

The CS229 Machine Learning course is an accessible Stanford course for enthusiasts. This course teaches the workings of Machine Learning algorithms in greater detail. It goes beyond the fundamentals of Machine Learning, touching more technical and advanced aspects. If you cannot access the course material from the source link, try the playlist uploaded to YouTube.

| 4 | Practical Deep Learning

This is an interactive course taught by Jeremy Howard. The course first introduces you to using state-of-the-art deep learning networks to solve real-world problems, and progress deeper into Machine Learning. The course covers; Neural Networks, Affine functions and nonlinearities, Parameters and activations, Transfer learning, Stochastic gradient descent (SGD), Data augmentation, Weight decay, Image classification, Entity and word embeddings, and so much more. By taking this course, you'd learn how to train models, how to turn your models into web applications and deploy them, the latest deep learning techniques, and so much more.

| 5 | Data Science: Machine Learning

This is a free project-based course by Harvard University available on Edx. By building a movie recommendation system, you'll learn popular machine learning algorithms, principal component analysis, and regularization. You will learn about training data, and how to use a set of data to discover potentially predictive relationships.

| 6 | UvA Deep Learning Tutorials

If you prefer learning with texts and some visual representation, then this is for you. This is a series of Machine Learning topics by different professionals in the Machine Learning field. It is split into several parts which cover different topics in the program. One session covers training models, another covers Deep Learning using PyTorch, Deep Learning using JAX+Flax, and so on. If you get lost in the tutorial process, you can get explanations from the YouTube adaptation of the series.

Conclusion

Breaking into machine learning doesn’t have to be expensive. The courses listed above provide you foundational knowledge needed to start building ML models. Whether you’re a complete beginner or have some experience in data science, these resources will help you expand your knowledge and develop the skills needed to advance in this rapidly growing field.

The key to mastering ML is consistency and practice. Take advantage of these free courses, work on projects, and engage with the ML community to stay motivated and informed. As technology continues to evolve, machine learning expertise will only become more valuable, opening doors to exciting career opportunities.