In the previous course, Foundations of Machine Learning you already learnt about the core machine learning concepts as well as the linear regression and gradient descent algorithms. In this course, you will learn various other machine learning algorithms, both for supervised learning as well as unsupervised learning.
Prerequisites: Foundations of Machine Learning
In this section, we'll learn about deep learning! We'll also compare deep learning to traditional machine learning algorithms (which we encountered in the previous section), and see why it is different.
If you're interested in learning more about deep learning, check out the comprehensive deep learning course here: Learn Deep Learning: From Beginner to Expert
In this section, we'll cover unsupervised learning concepts such as clustering, dimensionality reduction, the curse of dimensionality, feature extraction and feature selection. We'll also learn some algorithms such as K-means clustering and Principal Component Analysis.
Note that K-nearest neighbors and matrix factorization algorithms covered in the previous section can also be looked at as unsupervised learning algorithms for modeling similarity / relatedness between items.
