AI, Machine Learning & Statistics

Interactive lessons on artificial intelligence, machine learning and statistics — three words often used as if they mean the same thing. They don't. AI is the goal (smart behaviour), machine learning is a method (learning patterns from data), and statistics is the toolkit for reasoning about data and uncertainty. Work through the lessons in order; each one is a self-contained interactive page with quizzes, drag-the-data charts and hands-on activities. New lessons are added as the course grows.

What the course covers:

• Introduction: how AI, machine learning, deep learning and statistics nest and overlap
• Data and data preparation: collecting, cleaning and splitting data
• Descriptive statistics: summarising data with centre, spread and shape
• Supervised and unsupervised learning: regression, classification and clustering
• Model evaluation: measuring how well a model works on data it has never seen
• AI ethics and bias, and building AI/ML on AWS

Free courses and tutorials to go further:

• Elements of AI — a gentle, non-technical introduction to AI (University of Helsinki)
• Google Machine Learning Crash Course — practical ML with exercises
• Andrew Ng — Machine Learning Specialization (Coursera, free to audit)
• Khan Academy — Statistics and probability
• Seeing Theory — visual introduction to probability and statistics (Brown University)
• TensorFlow Playground and Teachable Machine — train a model in your browser
• AWS Skill Builder — free AI/ML learning plans on AWS