Machine learning for dummies


Machine Learning For Dummies gives you insights into what machine learning is all about and how it can impact the way you can weaponise data to gain unimaginable insights. Your data is only as good as what you do with it and how you manage it.

In this book, you will discover types of machine learning techniques, models, and algorithms that can help achieve results for your company. This information helps both business and technical leaders learn how to apply machine learning to anticipate and predict the future.



  • Understanding machine learning
    • What Is Machine Learning?
    • Iterative learning from data
    • What’s old is new again
    • Defining Big Data
    • Big Data in Context with Machine Learning
    • The Need to Understand and Trust your Data
    • The Importance of the Hybrid Cloud
    • Leveraging the Power of Machine Learning
    • Descriptive analytics
    • Predictive analytics
    • The Roles of Statistics and Data Mining with Machine Learning
    • Putting Machine Learning in Context
    • Approaches to Machine Learning
    • Supervised learning
    • Unsupervised learning
    • Reinforcement learning
    • Neural networks and deep learning
  • Applying machine learning
    • Getting Started with a Strategy
    • Using machine learning to remove biases from strategy
    • More data makes planning more accurate
    • Understanding Machine Learning Techniques
    • Tying Machine Learning Methods to Outcomes
    • Applying Machine Learning to Business Needs
    • Understanding why customers are leaving
    • Recognizing who has committed a crime
    • Preventing accidents from happening
  • A look inside machine learning
    • The Impact of Machine Learning on Applications
    • The role of algorithms
    • Types of machine learning algorithms
    • Training machine learning systems
    • Data Preparation
    • Identify relevant data
    • Governing data
    • The Machine Learning Cycle
  • Getting started with machine learning
    • Understanding How Machine Learning Can Help
    • Focus on the Business Problem
    • Bringing data silos together
    • Avoiding trouble before it happens
    • Getting customer focused
    • Machine Learning Requires Collaboration
    • Executing a Pilot Project
      • Step 1: Define an opportunity for growth
      • Step 2: Conducting a pilot project
      • Step 3: Evaluation
      • Step 4: Next actions
    • Determining the Best Learning Model
    • Tools to determine algorithm selection
    • Approaching tool selection
  • Learning machine skills
    • Defining the Skills That You Need
    • Getting Educated
    • Recommended Resources
  • Using machine learning to provide solutions to business problems
    • Applying Machine Learning to Patient Health
    • Leveraging IoT to Create More Predictable Outcomes
    • Proactively Responding to IT Issues
    • Protecting Against Fraud
  • Ten predictions on the future of machine learning
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