title: Python Machine Learning By Example: The easiest way to get into machine learning
author:  Yuxi (Hayden) Liu 
description: <b>Yuxi (Hayden) Liu</b><span>is currently an Applied Research Scientist working in the largest privately-owned Canadian Artificial Intelligence R&amp;D company. He is focused on developing machine learning systems and models, implementing appropriate architectures for given learning tasks. He has worked for a few years as a data scientist and applied his machine learning expertise in computational advertising. He earned his degree from the University of Toronto, and published five first-authored IEEE transaction and conference papers during his research. His book titled Python Machine Learning By Example was ranked the #1 Best Seller in Amazon India in 2017. He is also a machine learning education enthusiast.</span>
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  Machine Learning For Absolute Beginners:
  
  
  
  
  
  
  
  
  Introduction Guide to Artificial Intelligence. Machine Learning Tools and
  Techniques.
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  CONTENTS
  
  
  
  
  
  
  
  
  
  
  
  Introduction
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  What is machine learning?
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  Machine learning in the cloud
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  How do machine learning and containers mix?
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  Other tools for ML
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  How will enterprise ML change the way we work?
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  What is an algorithm?
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  Why do Python and data science mix well?
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  Python is a general-purpose programming language
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  Python is the perfect programming language for data science
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  Chapter 1 Machine learning
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  What is the definition of machine learning?
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  How does machine learning differ from statistical techniques?
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  What’s the difference between supervised and unsupervised machine learning?
  
  
  
  
  
  
  
  
  
  
  
  