Machine Learning
- 9 published modules
- 66 Topics · 726 Lessons
- Individual topics take approximately 30 to 60 minutes. Work at your own pace.
Read the explanation, change the parameters, and inspect the result.
A model learns a relationship by adjusting its parameters until its predictions better match the observations. The learning rate controls how large each adjustment is: too small and training crawls; too large and the model can jump past a useful solution.
Edit the Python below and run it in your browser. Compare the learned line with the data, then change the noise, number of examples, or training steps to examine how the result changes.
- Step-by-step explanation
- Executable Python
- Interactive Recharts output
- Knowledge checks
This is real Python running in your browser. Edit it and experiment with the result.
Read the first chapter for free.
The complete first chapter is available before you choose a plan. It does not require a trial or payment details.
Statistics 101
Sampling: From Populations to Observations
This chapter explains how observed samples relate to a larger population. It covers terminology, sampling methods, and practical design choices.
- 9 lessons
- 1 interactive activity
- 9 knowledge checks
Choose a billing period.
The first chapter is free. A membership provides access to the remaining chapters in Machine Learning.
- Every lesson in this course
- Executable Python and interactive plots
- Activities, knowledge checks, and progress tracking
- New chapters added while your membership is active
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What you will learn
- Explain the statistical foundations behind common machine learning methods.
- Connect mathematical ideas to practical Python implementations.
- Evaluate models, assumptions, and results with appropriate skepticism.
Who this is for
- Software engineers, analysts, and technical learners who want a rigorous foundation.
- Self-directed learners comfortable with basic algebra and introductory programming.
Prerequisites
- Basic Python familiarity is helpful but not required for the opening statistics module.
Featured topics
Module 01
Statistics 101
Learn the statistical terms and reasoning used to study data, uncertainty, and model evaluation.
Module 02
Hypothesis Testing
Topics for this module are being prepared.
Module 03
Foundations
Topics for this module are being prepared.
Module 04
Regression
Topics for this module are being prepared.
Module 05
Tree Based Models
Topics for this module are being prepared.
Module 06
Explainability
Topics for this module are being prepared.
Module 07
Unsupervised Learning
Topics for this module are being prepared.
Module 08
Time Series
Topics for this module are being prepared.
Module 09
Optimization
Topics for this module are being prepared.