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Introduction to Supervised Learning

In this chapter, we will discuss supervised learning and what it is all about. We will start this chapter by discussing the principles involved in different types of machine learning, with a particular focus on supervised learning. Then, we will look at various techniques used when training models. Finally, we will look at some common metrics that people use to judge how well an algorithm is performing.

The following topics will be covered in this chapter:

Principles of machine learning
Training models
Evaluating models

Key benefits

Get started with Python's rich suite of libraries for statistical modeling

Implement regression and clustering, and train neural networks from scratch

Discover real-world examples on training end-to-end machine learning systems in Python

Description

Python's ease-of-use and multi-purpose nature has made it one of the most popular tools for data scientists and machine learning developers. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book is designed to guide you through using these libraries to implement effective statistical models for predictive analytics. You’ll start by delving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will focus on supervised learning, which will help you explore the principles of machine learning and train different machine learning models from scratch. Next, you will work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. The book will also cover algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. In later chapters, you will learn how neural networks can be trained and deployed for more accurate predictions, and understand which Python libraries can be used to implement them. By the end of this book, you will have the knowledge you need to design, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive analytics.

What you will learn

Understand the importance of statistical modeling

Learn about the different Python packages for statistical analysis

Implement algorithms such as Naive Bayes and random forests

Build predictive models from scratch using Python s scikit-learn library

Implement regression analysis and clustering

Learn how to train a neural network in Python

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