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:
We'll start by describing what machine learning is, and introduce a simple learning algorithm: linear regression + gradient descent. Using this algorithm, we'll introduce the core concepts in machine learning: model parameters, cost function, optimization method, and overfitting and regularization. This section ends with a visual review of these concepts and a tutorial on the different types of machine learning problems.
It's now time to implement some of the things you learned so far. Suppose you want to make a personal website for yourself. For now, you will take the first step and just set up the project with an HTML and a CSS file, and make the home page display your name and style it a bit so that it looks like this:
