Exploring Machine Learning Basics
With chapters selected by Luis Serrano
  • July 2020
  • ISBN 9781617298127
  • 82 pages
Machine learning applications can be found in virtually every aspect of our day-to-day lives. Our product recommendations, social media feeds, email spam filters, traffic predictions, virtual personal assistants, and more, are all driven by machine learning. Companies are increasingly on the hunt for talented machine learning practitioners, so there’s no time like the present to gain those highly sought-after skills!

About the book

Exploring Machine Learning Basics has been created by machine learning expert Luis G. Serrano with hand-picked chapters taken from three Manning books. The first chapter lays a foundation by explaining what machine learning is, the different kinds of machine learning, and how a machine learns. With those basics under your belt, you’ll explore the most widely used types of machine learning and how to choose the most effective one for your task. You’ll also discover the many benefits of using machine learning in your business and how automating as many processes as possible can significantly boost productivity. Lastly, you’ll examine the important role humans play in successful machine learning models, such as selecting the right data to review and creating the training data that machines will ultimately learn from. This introductory sampler is an excellent first step on the path to a successful—and lucrative!—career in machine learning.

What's inside

  • “What is machine learning?” Chapter 1 from Grokking Machine Learning by Luis G. Serrano
  • “Types of machine learning” – Chapter 2 from Grokking Machine Learning by Luis G. Serrano
  • “How machine learning applies to your business” – Chapter 1 from Machine Learning for Business by Doug Hudgeon and Richard Nichol
  • “Introduction to Human-in-the-Loop Machine Learning” – Chapter 1 from Human-in-the-Loop Machine Learning by Robert Munro

About the author

Luis G. Serrano has worked as the Head of Content for Artificial Intelligence at Udacity and as a Machine Learning Engineer at Google, where he worked on the YouTube recommendations system. He holds a PhD in mathematics from the University of Michigan, a Bachelor and Masters from the University of Waterloo, and worked as a postdoctoral researcher at the University of Quebec at Montreal. He shares his machine learning expertise on a YouTube channel with over 2 million views and 35 thousand subscribers, and is a frequent speaker at artificial intelligence and data science conferences.

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