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Key benefits

Learn how to prepare data for machine learning processes

Understand which algorithms are based on prediction objectives and the properties of the data

Explore how to interpret and evaluate the results from machine learning

Description

Many individuals who know how to run machine learning algorithms do not have a good sense of the statistical assumptions they make and how to match the properties of the data to the algorithm for the best results. As you start with this book, models are carefully chosen to help you grasp the underlying data, including in-feature importance and correlation, and the distribution of features and targets. The first two parts of the book introduce you to techniques for preparing data for ML algorithms, without being bashful about using some ML techniques for data cleaning, including anomaly detection and feature selection. The book then helps you apply that knowledge to a wide variety of ML tasks. You’ll gain an understanding of popular supervised and unsupervised algorithms, how to prepare data for them, and how to evaluate them. Next, you’ll build models and understand the relationships in your data, as well as perform cleaning and exploration tasks with that data. You’ll make quick progress in studying the distribution of variables, identifying anomalies, and examining bivariate relationships, as you focus more on the accuracy of predictions in this book. By the end of this book, you’ll be able to deal with complex data problems using unsupervised ML algorithms like principal component analysis and k-means clustering.

Who is this book for?

This book is for professional data scientists, particularly those in the first few years of their career, or more experienced analysts who are relatively new to machine learning. Readers should have prior knowledge of concepts in statistics typically taught in an undergraduate introductory course as well as beginner-level experience in manipulating data programmatically.

What you will learn

Explore essential data cleaning and exploration techniques to be used before running the most popular machine learning algorithms

Understand how to perform preprocessing and feature selection, and how to set up the data for testing and validation

Model continuous targets with supervised learning algorithms

Model binary and multiclass targets with supervised learning algorithms

Execute clustering and dimension reduction with unsupervised learning algorithms

Understand how to use regression trees to model a continuous target

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Total $ 141.97

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Yiqiao Yin Sep 02, 2022

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Lance Oct 14, 2022

This book is amazingly comprehensive and clear in how to work with data and manage it effectively and ethically to be prepared for machine learning. Walker takes readers through each step with accessible language that feels more suggestive or open to consideration by the readers; in that way, it feels conversational rather than directional and that he is along for the deep dive into machine learning with the reader. He also provides various tips and insights that can anticipate issues or problems that the reader may run into.

Kedeisha B. Oct 29, 2022

This is hands down one of the best books I have gone through. So much hands on exercises with thoughtful explanations.What I enjoyed about this book is that is teaches what I find missing in many popular courses. The data cleaning and exploration techniques to prepare your data to best fit the statistical assumptions of a model that achieves optimal results.Rather than provide every data cleaning or feature selection technique known to man, it teaches the 20% that drive 80% of value.This is an amazing book that comes with a great GitHub repository where you can code along with each chapter. If you are looking to transition into a Data Science role or in the earlier stages of your career, don't walk, but run and get this book.

Jay Oct 18, 2022

This book clearly explains all the essential concepts of data cleaning and gives very good insight on machine learning algorithms. You also get all the example code which you can play with. People who want to explore the ML AI domain are often intimidated by the amount of resources they find online. This book gives you solid fundamentals and doesn’t distract you from your goal.

Adam Bush Sep 24, 2022

The author makes clear at the start that anyone can find themselves in this text. And it's true! This was not my field but I found myself turning the pages as I would a novel. The author is a teacher at heart-- at that comes across throughout. I was not talked at nor talked down to, but, instead, guided towards a new understanding of the iterative processes and possibilities of predictive analysis.

Data Cleaning and Exploration with Machine Learning: Get to grips with machine learning techniques to achieve sparkling-clean data quickly

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