Learning path: Get started with machine learning

Level Topic Type
100 Introduction to machine learning Article
101 Build and test your first machine learning model using Python and scikit-learn Tutorial+Notebook
201 Learn regression algorithms using Python and scikit-learn Tutorial+Notebook
202 Learn classification algorithms using Python and scikit-learn Tutorial+Notebook
203 Learn clustering algorithms using Python and scikit-learn Tutorial+Notebook

This learning path is designed for anyone interested in quickly getting up to speed with machine learning. This learning path consists of step-by-step tutorials with hands-on demonstrations where you will build models and use them in apps.

To get started, click on a card below, or see the previous table for a complete list of topics covered.

Introduction to machine learning


Learn about:

  • What is machine learning?
  • Supervised versus unsupervised learning
  • Machine learning pipelines
  • Terms and concepts

Build and test your first machine learning model using Python and scikit-learn


Learn about:

  • Performing data exploration
  • Performing data preprocessing
  • Splitting data for training and testing
  • Preparing a classification model
  • Training the model
  • Running predictions on the model
  • Evaluating and visualizing model performance

Learn regression algorithms using Python and scikit-learn


Learn about:

  • Linear regression
  • Splitting, training, validation
  • Overfitting and underfitting
  • Model evaluation
  • Logistic regression
  • Naive Bayes
  • Ensemble learning

Learn classification algorithms using Python and scikit-learn


Learn about:

  • Classification algorithms
  • Basics of solving a classification-based machine learning problem

Learn clustering algorithms using Python and scikit-learn


Learn about:

  • K-means clustering
  • Mean shift
  • DBSCAN
  • Hierarchical clustering
  • Agglomerative clustering
  • Use cases


Next: Introduction to machine learning