Description

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Tags: Analytics, Lang:en

Summary

Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition.

Ready to crank up a virtual server and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?

Well, hold on there...

Before you embark on your epic journey into the world of machine learning, there is some theory and statistical principles to march through first.

But rather than spend $30-$50 USD on a dense long textbook, you may want to read this book first. As a clear and concise alternative to a textbook, this book provides a practical and high-level introduction to the practical components and statistical concepts found in machine learning.

Machine Learning for Absolute Beginners Second Edition has been written and designed for absolute beginners . This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home.

This major new edition features many topics not covered in the First Edition, including Cross Validation, Ensemble Modeling, Grid Search, Feature Engineering, and One-hot Encoding. Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition. Readers of the First Edition should not feel compelled to purchase this Second Edition.

Disclaimer: If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle coding and deep learning, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment - as a fully grown Simba looking over the Pride Lands of Africa - then this is the book to gently hoist you up and offer you a clear lay of the land.

In this step-by-step guide you will learn:

• How to download free datasets
• What tools and machine learning libraries you need
• Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data
• Preparing data for analysis, including k-fold Validation
• Regression analysis to create trend lines
• Clustering, including k-means and k-nearest Neighbors
• The basics of Neural Networks
• Bias/Variance to improve your machine learning model
• Decision Trees to decode classification
• How to build your first Machine Learning Model to predict house values using Python

Frequently Asked Questions
Q: Do I need programming experience to complete this e-book?
A: This e-book is designed for absolute beginners, so no programming experience is required. However, two of the later chapters introduce Python to demonstrate an actual machine learning model, so you will see programming language used in this book.

Q: I have already purchased the First Edition of Machine Learning for Absolute Beginners, should I purchase this Second Edition?
A: As many of the topics from the First Edition are covered in the Second Edition, you may be better served reading a more advanced title on machine learning.

Q: Does this book include everything I need to become a machine learning expert?
A: Unfortunately, no. This book is designed for readers taking their first steps in machine learning and further learning will be required beyond this book to master machine learning.

Please feel welcome to join this introductory course by buying a copy, or sending a free sample to your preferred device.

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