Learn foundational machine learning techniques from data manipulation to unsupervised and supervised algorithms.
First, we'll start by teaching you how to train and test models using sklearn. We'll go over the main metrics used for evaluating models, such as accuracy, precision, recall, etc. Then, we'll learn about common supervised learning algorithms, including linear and logistic regression, decision trees, naive Bayes, neural networks, and support vector machines. We'll also learn to combine these algorithms to achieve their full potential, in the ensemble methods section. Every section is equipped with a lab, where you'll get to apply your knowledge using sklearn.
In this section, we'll be learning the foundational math and theory behind neural networks that can learn to find patterns in some given data. You'll implement backpropagation and optimization using NumPy to get an understanding of neural networks, then build up to training neural networks using the deep learning framework, TensorFlow. You'll see how to build your own image classifier in TensorFlow.
In this section, we'll be going over the main unsupervised learning algorithms, including several clustering methods, and dimensionality reduction. Unsupervised learning is all about finding groupings in data without specific metrics, like accuracy, to aim for; it is also used heavily in reducing the dimensionality of data. You'll go through several mini-projects and labs in which you'll be able to apply these concepts with real data.