Every machine learning project follows the same arc: frame the problem, get and clean the data, split it, train a model, evaluate it, and then use it to make predictions. This lesson gives you that map before you meet any specific algorithm, so each later lesson has a place to slot into.
You'll start with the big divide between supervised learning (learning from labeled examples, as in regression and classification) and unsupervised learning (finding structure in unlabeled data). Then you'll get hands-on with scikit-learn, where nearly every model follows the same fit / predict pattern, and see the whole pipeline run from data to a trained and evaluated model.
Watch the videos in order, then follow along in your own notebook. When you're done, continue on to Linear Regression.
What machine learning is and the main types of learning: how supervised learning (regression and classification) differs from unsupervised learning, and when to use each.
The basic concepts of machine learning and a hands-on introduction to scikit-learn, with practical examples of applying supervised learning to a variety of data. No prior ML experience needed.
The full pipeline from data ingestion with pandas, to visualization with seaborn, to machine learning with scikit-learn: training a linear regression model, comparing evaluation metrics, and using a train/test split to choose features.