Machine Learning For Absolute Beginners: A Plain English Introduction (Second Edition) - No cost library

Machine Learning For Absolute Beginners: A Plain English Introduction - 2nd Edition 

Machine Learning For Absolute Beginners: A Plain English Introduction (Second Edition) pdf free download

   Author(s): Oliver Theobald  
         Year: 2017   

Can handle petabytes of data be processed quickly before the VM spins up? Would you like to include "machine learning" on your LinkedIn profile?
Stop for a moment, because...
Before you set out on your epic journey, you must first familiarise yourself with some basic theories and data.
On the other hand, you may want to read this book instead of buying a $30–50 USD dense textbook This book can be used as an alternative to a textbook and provides a clear and simple overview of machine learning methods.

This second edition of the textbook has been written and designed for beginners who know nothing about the field of machine learning. A gentle, simple, non-technical approach will be taken with this. inform algorithms and visual examples are used to help it be easier to understand where necessary

This book has been comprehensively revised and updated for a new and current readership.

This new edition includes topics such as cross-validation, ensemble modeling, feature engineering, and one-encoding. Please note that this book is a separate and revised version of the First Edition. It should be clear that there is no reason for readers of the First Edition to purchase this second edition.

You have successfully completed the 'beginner' levels of machine learning and will greatly benefit from a long-format textbook. Even if you are still growing into a majestic, fully mature lion, this is the book to give you a geography lesson of the Pride Lands of Africa.

Step-by-by-by-step you will learn this lesson

  • How to obtain datasets for free
  • The necessary tools and machine learning packages
  • One-hot encoding, binning, and dealing with missing data
  • Processing data, such as data for analysis, and performing the k-fold validation
  • Regression analysis [trends]
  • Intersecting to examine new relationships
  • The basic concepts of Neural Networks
  • Model bias/model variance to aid in machine learning
  • Decoding classifications using decision trees
  • Predicting house values with your first machine learning model in Python

A list of frequently asked questions

This: Can I complete this e-book without any prior programming experience?

This e-book is for those who have no programming experience, so no prior knowledge of computer programming is required. Two of the later chapters will use machine learning, but this book mainly focuses on the fundamentals of the Python language.

Should I buy the second edition of this book if I have the first?

B: You may be better served by reading a more advanced machine learning book on topics from the First Edition that were not already covered in the Second Edition.

Q:This book covers everything I need to know about machine learning?

Unfortunately, that is not possible. For readers who are new to machine learning, this book will serve as a guide, but beyond this point they will need more extensive experience.

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