Machine learning is the point where your data starts making predictions. Instead of writing rules by hand, you let an algorithm find the patterns, and your job becomes choosing the right model, feeding it good data, and judging honestly how well it works.
You'll start with the big picture of supervised and unsupervised learning, then build your first model with linear regression. From there the module covers the ideas that keep models honest: the bias-variance trade-off, overfitting, and cross-validation. You'll also get hands-on with scikit-learn, the standard Python library for training and evaluating classical models.
With these fundamentals in place, you'll be ready for the next module, Deep Learning, where models grow into neural networks that can handle images, sound, and more.
A beginner-friendly course covering supervised and unsupervised learning, with code for classification, regression, and clustering.
How a line is fitted to data, what least squares means, and how to read R-squared and p-values for a simple model.
Why simple models underfit and complex models overfit, and how to think about balancing the two.
How to split your data to get an honest estimate of performance and choose between models without fooling yourself.
Training, tuning, and evaluating models with scikit-learn, including pipelines and common classifiers and regressors.