Machine Learning Fundamentals
Master the core concepts of machine learning — from supervised and unsupervised learning to model evaluation and deployment. Build real-world projects using Python, scikit-learn, and industry datasets.
What you'll learn
Prerequisites
- Basic Python programming (variables, loops, functions)
- High school level mathematics (algebra, probability)
- No prior ML experience required
Welcome & Course Overview
What is Machine Learning?
Setting Up Your Python Environment
Supervised vs Unsupervised Learning
Your First ML Model: Linear Regression
Quiz: Core ML Concepts
Classification with Logistic Regression
Decision Trees & Random Forests
Feature Engineering Deep Dive
Model Evaluation & Cross-Validation
Hyperparameter Tuning with GridSearchCV
Quiz: Model Selection & Evaluation
Intro to Neural Networks
Unsupervised Learning: K-Means Clustering
Dimensionality Reduction with PCA
Capstone Project: End-to-End ML Pipeline
Deploying Your Model as a REST API
Course Wrap-Up & Next Steps
Your Instructor
Dr. Marcus Chen
Senior ML Researcher · Stanford AI Lab
Dr. Chen has spent 12 years at the intersection of academic ML research and industry application. Previously at Google Brain and DeepMind, he now focuses on making machine learning accessible to learners at every level. His teaching style emphasises intuition before mathematics — building deep understanding through real-world examples before introducing formal notation.
Student Reviews
4.8
Course rating
Taiwo Adeyemi
Aug 3, 2026
Absolutely fantastic course. Dr. Chen has a gift for making complex topics feel approachable. The section on decision trees clicked something in my brain that textbooks never could. Highly recommended for anyone starting out in ML.
Ngozi Eze
Jul 28, 2026
Came in with zero ML knowledge and now I can build and deploy a model end-to-end. The capstone project is genuinely challenging and rewarding. The community around this course is also very active and helpful.
Rafael Mendes
Jul 19, 2026
Great content overall. The deployment lesson at the end could go deeper — I wanted more on containerisation with Docker. That said, the core ML content is excellent and well-paced.
Amara Diallo
Jul 10, 2026
I took this course as part of my health informatics master's programme and it gave me exactly the ML foundation I needed. The cross-validation section saved my thesis project.
This course includes
30-day satisfaction guarantee · No credit card required