Free Machine Learning Courses
Machine learning courses vary more in difficulty than any other category here, so pick based on your actual background, not ambition. Google's Machine Learning Crash Course and Kaggle's Intro to Machine Learning are the right starting points if you're new: both are short, practical, and don't require much math beyond algebra. Once you're comfortable with Python, freeCodeCamp's Machine Learning with Python course and MIT's Introduction to Deep Learning build real technical depth. Andrew Ng's Deep Learning Specialization on Coursera and Andrej Karpathy's Neural Networks: Zero to Hero are both advanced and both worth the time, but expect to need linear algebra and a working knowledge of Python before you start either one.
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6 free Machine Learning courses include a certificate
See all free coding courses with certificates →Google's fast-paced introduction to machine learning. Covers ML concepts, TensorFlow APIs, and real-world case studies. Written and maintained by Google engineers. Completely free.
Machine Learning with Python
freeCodeCamp's machine learning curriculum. Covers TensorFlow, neural networks, natural language processing, and reinforcement learning. Build and train models through five certification projects. Free certificate.
Harvard's 9-course data science certificate on edX. Covers R programming, data visualisation, probability, inference, regression, machine learning, and capstone.
Andrew Ng's landmark Deep Learning Specialization on Coursera. Five courses covering neural networks, CNNs, RNNs, optimisation, and ML strategy. Free to audit; certificate costs money.
MIT's graduate-level machine learning course. Covers supervised and unsupervised learning, neural networks, SVMs, Bayesian methods, EM algorithm, and reinforcement learning.
Andrej Karpathy's free video series building neural networks from scratch — from backpropagation all the way to GPT. Widely considered the single best free deep-learning course available. Taught by an OpenAI founding member and ex-Tesla AI director.
Kaggle Learn's micro-course on machine learning fundamentals using scikit-learn. Covers decision trees, model validation, underfitting and overfitting, and random forests. Three hours, all in browser-based notebooks.
Kaggle Learn's follow-up to Intro to ML. Covers missing values, categorical variables, pipelines, cross-validation, XGBoost, and data leakage. Four hours of focused, applied content.
Intro to Deep Learning (Kaggle)
Kaggle Learn's introduction to deep learning with TensorFlow and Keras. Covers neural networks, dropout, batch normalization, and binary classification. Hands-on with real datasets.
MIT's annual deep learning course. Covers deep learning fundamentals, CNNs, RNNs, generative models, and responsible AI. Lecture videos updated annually and completely free.