This 29-part course consists of tutorials on ML concepts and algorithms, as well as end-to-end follow-along ML examples, quizzes, and hands-on projects. You can think of this course as a "Free Online Nano Book".

Once done, you will have an excellent conceptual and practical understanding of machine learning and feel comfortable applying ML thinking and algorithms in your projects and work.

The **primary objectives** of this course are:

- Understand the core concepts in machine learning — model parameters, optimization, generalization, regularization, and so on.
- Understand some popular machine learning algorithms - this course covers 8 ML algorithms, I recommend you learn at-least 5 well
- Implement machine learning algorithms from scratch (recommend doing at-least 2)
- Apply machine learning algorithms for prediction tasks (recommend doing at-least 2)
- Do a more extensive machine learning project (recommend doing at-least 1)

**Prerequisites:** Python and Linear Algebra, Statistics and NumPy

**Related course**: Learn Data Science with Python

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