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Ready to crank up a virtual server to 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 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 short book has become a Best Seller on Amazon as a practical and high-level introduction to the practical components and statistical concepts found in 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 statistical concepts and 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. As an absolute beginner's introduction, the book does not cover programming language.


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


- k-means and k-nearest Neighbor (k-nn) Clustering to discover new data groupings


- Introduction to Deep Learning/Neural Networks


- Bias/Variance to optimize your machine learning model


- Careers in the field

If you want to learn more, please go ahead and send a free sample to your device or check out Amazon's handy 'Look Inside' feature.