The algorithms that you will learn about in this book are broadly classified into the following two categories:

• Supervised learning algorithms
• Unsupervised learning algorithms

Supervised learning algorithms can be used to solve both classification and regression problems. In this book, you will learn how to implement some of the most popular supervised machine learning algorithms. Popular supervised machine learning algorithms are the ones that are widely used in industry and research, and have helped us solve a wide range of problems across a wide range of domains. These supervised learning algorithms are as follows:

• Linear regression: This supervised learning algorithm is used to predict continuous numeric outcomes such as house prices, stock prices, and temperature, to name a few
• Logistic regression: The logistic learning algorithm is a popular classification algorithm that is especially used in the credit industry in order to predict loan defaults
• k-Nearest Neighbors: The k-NN algorithm is a classification algorithm that is used to classify data into two or more categories, and is widely used to classify houses into expensive and affordable categories based on price, area, bedrooms, and a whole range of other features
• Support vector machines: The SVM algorithm is a popular classification algorithm that is used in image and face detection, along with applications such as handwriting recognition
• Tree-Based algorithms: Tree-based algorithms such as decision trees, Random Forests, and Boosted trees are used to solve both classification and regression problems
• Naive Bayes: The Naive Bayes classifier is a machine learning algorithm that uses the mathematical model of probability to solve classification problems

Unsupervised machine learning algorithms are typically used to cluster points of data based on distance. The unsupervised learning algorithm that you will learn about in this book is as follows:

• k-means: The k-means algorithm is a popular algorithm that is typically used to segment customers into unique categories based on a variety of features, such as their spending habits. This algorithm is also used to segment houses into categories based on their features, such as price and area.