Book Image

Training Systems using Python Statistical Modeling

By : Curtis Miller
Book Image

Training Systems using Python Statistical Modeling

By: Curtis Miller

Overview of this book

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.
Table of Contents (9 chapters)

Introduction to clustering

Let's get started. In this section, we will contrast unsupervised learning with supervised learningthe latter is what we have been doing up to this point. We will then learn what clustering is.

Supervised and unsupervised learning are two different learning paradigms. So far in this book, we have been performing different kinds of supervised learning. There was a variable, which we called the target variable, that we wanted to predict. This was the case both for classification and regression. Learning consisted of finding a classifier that could accurately predict the target variable. The following diagram represents supervised learning:

With unsupervised learning, there is no target variable. We believe there are hidden features that differentiate the data, but we don't observe them. The objective of unsupervised learning is to find...