Book Title: Machine Learning for Absolute Beginners: A Plain English Introduction
Publisher: Independently published
Author: Oliver Theobald
Ready to spin up a virtual GPU instance 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 basic theory 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 offers a practical and high-level introduction to machine learning. Machine Learning for Absolute Beginners
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 title opens with a general introduction to machine learning from a macro level. The second half of the book is more practical and dives into introducing specific algorithms applied in machine learning, including their pros and cons. At the end of the book, I share insights and advice on further learning and careers in this space. Disclaimer:
If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle deep learning and Scikit-learn, 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:
- The very basics
of Machine Learning that all beginners need to master
- Association Analysis
used in the retail and E-commerce space
- Recommender Systems
as you've seen online, including Amazon
- Decision Trees
for visually mapping and classifying decision processes
- Regression Analysis
to create trend lines and predict trends
- Data Reduction
and Principle Component Analysis
to cut through the noise
and k-nearest Neighbor (k-nn) Clustering
to discover new data groupings
- Introduction to Deep Learning/Neural Networks
to optimize your machine learning model
- How to build your first machine learning model
to predict video game sales using Python
in the field
Please also note that under Amazon’s Matchbook program, the purchaser of this book can add the Kindle version of this title (valued at $3.99 USD) to their Amazon Kindle library at no cost.