Contents
- 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
