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.

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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