Machine Learning

Supervised and unsupervised algorithms for building, tuning and evaluating predictive models.

Machine Learning Deep Dive: Business Applications and Coding Walkthroughs

An advanced, three-hour course where Netflix data scientist Jeff Li and Ken Jee link ML algorithms to business cases and code models from scratch.

Python

Machine Learning Specialization (Stanford / DeepLearning.AI)

Machine Learning

A three-course beginner program from DeepLearning.AI and Stanford Online covering supervised, unsupervised and neural network methods in Python.

Python

Applied Data Science with Python Specialization (Michigan)

Machine Learning

Five University of Michigan courses teaching Python for data analysis, visualization, machine learning, text mining and network analysis.

Python

Machine Learning A-Z: AI, Python & R

Machine Learning

One of Udemy's best-known ML courses, covering regression, classification and clustering in both Python and R with template-based coding.

Python R

Supervised Learning with scikit-learn

Machine Learning

Hands-on 4-hour DataCamp course teaching classification, regression, tuning, and pipelines in scikit-learn with real datasets in Python.

Python

Unsupervised Learning in Python

Machine Learning

Hands-on Python course on k-means, hierarchical clustering, t-SNE, PCA and NMF with scikit-learn, ending in a music artist recommender.

Python

Machine Learning for Business

Machine Learning

Discover how machine learning can transform business strategies and enhance decision-making without needing a programming background.

Machine learning courses in this section focus on classical algorithms: linear and logistic regression, decision trees, random forests, gradient boosting, support vector machines, k-means and other clustering methods. You will learn the full modelling workflow, including feature engineering, train-test splits, cross-validation, handling class imbalance and choosing evaluation metrics that reflect the actual business cost of an error. Most courses build on scikit-learn and XGBoost in Python, with some R alternatives. Neural network and transformer material lives under Deep Learning; this is the ground you should cover first, because most production problems are still solved with these methods.