This course consists of 25 tutorials to learn machine learning algorithms. You can think of this course as a "Free Online Nano Book". You'll learn core machine learning algorithms and applications. Everything is 100% free!
Once done, you should have a good conceptual understanding of machine learning algorithms and be able to reason about how to improve the accuracy of your predictions and reason about model performance.
Related: If you are a beginner, I recommend starting out with the following course instead: Learn Machine Learning: From Beginner to Expert.
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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.
In this section, we'll see various algorithms for supervised machine learning. Different algorithms perform better for different types of data, where deciding factors would include total number of dimensions in input data, whether the data is text or numerical or a time series, whether or not the data is sparse, size of dataset, and so on. For each algorithm, we'll explain how it works and the applications for which it produces state-of-the-art results.
This section covers more algorithms for supervised machine learning. However, some of these algorithms require more mathematical maturity, such as Support Vector Machines and Hidden Markov Models.
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 how they differ. If you're interested in learning more about deep learning, check out the comprehensive deep learning course here: Deep learning, from novice to expert, self-paced
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: K-nearest neighbors and matrix factorization algorithms covered in the previous sections can also be used for unsupervised learning, and in particular, for modeling similarity or relatedness between items.
