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Artificial Intelligence for Business - Course

(AI-BUS.AP1.E0T)
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1

Preface

  • About This eBook
  • Foreword
2

What Is Artificial Intelligence?

  • What Is Intelligence?
  • Testing Machine Intelligence
  • The General Problem Solver
  • Strong and Weak Artificial Intelligence
  • Artificial Intelligence Planning
  • Learning over Memorizing
  • Lesson Takeaways
3

The Rise of Machine Learning

  • Practical Applications of Machine Learning
  • Artificial Neural Networks
  • The Fall and Rise of the Perceptron
  • Big Data Arrives
  • Lesson Takeaways
4

Zeroing in on the Best Approach

  • Expert System Versus Machine Learning
  • Supervised Versus Unsupervised Learning
  • Backpropagation of Errors
  • Regression Analysis
  • Lesson Takeaways
5

Common AI Applications

  • Intelligent Robots
  • Natural Language Processing
  • The Internet of Things
  • Lesson Takeaways
6

Putting AI to Work on Big Data

  • Understanding the Concept of Big Data
  • Teaming Up with a Data Scientist
  • Machine Learning and Data Mining: What’s the Difference?
  • Making the Leap from Data Mining to Machine Learning
  • Taking the Right Approach
  • Lesson Takeaways
7

Weighing Your Options

  • Lesson Takeaways
8

What Is Machine Learning?

  • How a Machine Learns
  • Working with Data
  • Applying Machine Learning
  • Different Types of Learning
  • Lesson Takeaways
9

Different Ways a Machine Learns

  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Semi-Supervised Machine Learning
  • Reinforcement Learning
  • Lesson Takeaways
10

Popular Machine Learning Algorithms

  • Decision Trees
  • k-Nearest Neighbor
  • k-Means Clustering
  • Regression Analysis
  • Näive Bayes
  • Lesson Takeaways
11

Applying Machine Learning Algorithms

  • Fitting the Model to Your Data
  • Choosing Algorithms
  • Ensemble Modeling
  • Deciding on a Machine Learning Approach
  • Lesson Takeaways
12

Words of Advice

  • Start Asking Questions
  • Don’t Mix Training Data with Test Data
  • Don’t Overstate a Model’s Accuracy
  • Know Your Algorithms
  • Lesson Takeaways
13

What Are Artificial Neural Networks?

  • Why the Brain Analogy?
  • Just Another Amazing Algorithm
  • Getting to Know the Perceptron
  • Squeezing Down a Sigmoid Neuron
  • Adding Bias
  • Lesson Takeaways
14

Artificial Neural Networks in Action

  • Feeding Data into the Network
  • What Goes on in the Hidden Layers
  • Understanding Activation Functions
  • Adding Weights
  • Adding Bias
  • Lesson Takeaways
15

Letting Your Network Learn

  • Starting with Random Weights and Biases
  • Making Your Network Pay for Its Mistakes: The Cost Function
  • Combining the Cost Function with Gradient Descent
  • Using Backpropagation to Correct for Errors
  • Tuning Your Network
  • Employing the Chain Rule
  • Batching the Data Set with Stochastic Gradient Descent
  • Lesson Takeaways
16

Using Neural Networks to Classify or Cluster

  • Solving Classification Problems
  • Solving Clustering Problems
  • Lesson Takeaways
17

Key Challenges

  • Obtaining Enough Quality Data
  • Keeping Training and Test Data Separate
  • Carefully Choosing Your Training Data
  • Taking an Exploratory Approach
  • Choosing the Right Tool for the Job
  • Lesson Takeaways
18

Harnessing the Power of Natural Language Processing

  • Extracting Meaning from Text and Speech with NLU
  • Delivering Sensible Responses with NLG
  • Automating Customer Service
  • Reviewing the Top NLP Tools and Resources
  • Lesson Takeaways
19

Automating Customer Interactions

  • Choosing Natural Language Technologies
  • Review the Top Tools for Creating Chatbots and Virtual Agents
  • Lesson Takeaways
20

Improving Data-Based Decision-Making

  • Choosing Between Automated and Intuitive Decision-Making
  • Gathering Data in Real Time from IoT Devices
  • Reviewing Automated Decision-Making Tools
  • Lesson Takeaways
21

Using Machine Learning to Predict Events and Outcomes

  • Machine Learning Is Really about Labeling Data
  • Looking at What Machine Learning Can Do
  • Use Your Power for Good, Not Evil: Machine Learning Ethics
  • Review the Top Machine Learning Tools
  • Lesson Takeaways
22

Building Artificial Minds

  • Separating Intelligence from Automation
  • Adding Layers for Deep Learning
  • Considering Applications for Artificial Neural Networks
  • Reviewing the Top Deep Learning Tools
  • Lesson Takeaways

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