Teach your machine to think for itself!

Key Features

• Delve into supervised learning and grasp how a machine learns from data
• Implement popular machine learning algorithms from scratch, developing a deep understanding along the way
• Explore some of the most popular scientific and mathematical libraries in the Python language

Book Description

Supervised machine learning is used in a wide range of sectors (such as finance, online advertising, and analytics) because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own. As a result, it's crucial to know how a machine “learns” under the hood.

This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You'll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you'll wrap up with a brief foray into neural networks and transfer learning.

By the end of this book, you'll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and powerfully apply algorithms to new problems.

What you will learn

• Crack how a machine learns a concept and generalize its understanding to new data
• Uncover the fundamental differences between parametric and non-parametric models
• Implement and grok several well-known supervised learning algorithms from scratch
• Work with models in domains such as ecommerce and marketing
• Expand your expertise and use various algorithms such as regression, decision trees, and clustering
• Build your own models capable of making predictions
• Delve into the most popular approaches in deep learning such as transfer learning and neural networks

Who this book is for

This book is for aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming—and some fundamental knowledge of supervised learning—are expected.

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