Get hands-on experience in Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support with the Artificial Intelligence for Business course and lab. The course provides a vivid introduction to technologies collectively called analytics and the fundamental methods, techniques, and software used to design and develop these systems with clear and approachable lesson flowcharts, and other tools. It illustrates how to enable technologies, including AI, machine learning, robotics, chatbots, and IoT. The Artificial Intelligence for Business course will assist you in learning artificial neural networks, machine learning, neural networks, and many more.

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Learning Objectives

Get hands-on experience in Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support with the Artificial Intelligence for Business course and lab. The course provides a vivid introduction to technologies collectively called analytics and the fundamental methods, techniques, and software used to design and develop these systems with clear and approachable lesson flowcharts, and other tools. It illustrates how to enable technologies, including AI, machine learning, robotics, chatbots, and IoT. The Artificial Intelligence for Business course will assist you in learning artificial neural networks, machine learning, neural networks, and many more.

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

Audience

Anyone wanting to learn about AI will benefit.

Language

English

Prerequisites

While there are no prerequisites for this course, please ensure you have the right level of experience to be successful in this training.

Length: 365.0 days ( hours)

Level:

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