Artificial Intelligence for Business

(AI-BUS.AP1) / ISBN: 9781644593004
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Lessons
TestPrep
Lab
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Artificial Intelligence for Business

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.
Here's what you will get

Lessons
  • 22+ Lessons
  • 57+ Exercises
  • 144+ Quizzes
  • 108+ Flashcards
  • 108+ Glossary of terms
TestPrep
Lab
  • 8+ Performance lab
Here's what you will learn
Download Course Outline
  • About This eBook
  • Foreword
  • What Is Intelligence?
  • Testing Machine Intelligence
  • The General Problem Solver
  • Strong and Weak Artificial Intelligence
  • Artificial Intelligence Planning
  • Learning over Memorizing
  • Lesson Takeaways
  • Practical Applications of Machine Learning
  • Artificial Neural Networks
  • The Fall and Rise of the Perceptron
  • Big Data Arrives
  • Lesson Takeaways
  • Expert System Versus Machine Learning
  • Supervised Versus Unsupervised Learning
  • Backpropagation of Errors
  • Regression Analysis
  • Lesson Takeaways
  • Intelligent Robots
  • Natural Language Processing
  • The Internet of Things
  • Lesson Takeaways
  • 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
  • Lesson Takeaways
  • How a Machine Learns
  • Working with Data
  • Applying Machine Learning
  • Different Types of Learning
  • Lesson Takeaways
  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Semi-Supervised Machine Learning
  • Reinforcement Learning
  • Lesson Takeaways
  • Decision Trees
  • k-Nearest Neighbor
  • k-Means Clustering
  • Regression Analysis
  • Näive Bayes
  • Lesson Takeaways
  • Fitting the Model to Your Data
  • Choosing Algorithms
  • Ensemble Modeling
  • Deciding on a Machine Learning Approach
  • Lesson Takeaways
  • Start Asking Questions
  • Don’t Mix Training Data with Test Data
  • Don’t Overstate a Model’s Accuracy
  • Know Your Algorithms
  • Lesson Takeaways
  • Why the Brain Analogy?
  • Just Another Amazing Algorithm
  • Getting to Know the Perceptron
  • Squeezing Down a Sigmoid Neuron
  • Adding Bias
  • Lesson Takeaways
  • Feeding Data into the Network
  • What Goes on in the Hidden Layers
  • Understanding Activation Functions
  • Adding Weights
  • Adding Bias
  • Lesson Takeaways
  • 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
  • Solving Classification Problems
  • Solving Clustering Problems
  • Lesson Takeaways
  • 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
  • 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
  • Choosing Natural Language Technologies
  • Review the Top Tools for Creating Chatbots and Virtual Agents
  • Lesson Takeaways
  • Choosing Between Automated and Intuitive Decision-Making
  • Gathering Data in Real Time from IoT Devices
  • Reviewing Automated Decision-Making Tools
  • Lesson Takeaways
  • 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
  • Separating Intelligence from Automation
  • Adding Layers for Deep Learning
  • Considering Applications for Artificial Neural Networks
  • Reviewing the Top Deep Learning Tools
  • Lesson Takeaways

Hands on Activities (Performance Labs)

  • Analyzing the Artificial Intelligence, Machine Learning, and Deep Learning
  • Analyzing the Similarities and Differences Betwe...telligence, Machine Learning, and Deep Learning.
  • Understanding Concepts Used to Automate Decision-Making Processes
  • Understanding Approaches Used to Automate Computer Decision-Making Processes
  • Analyzing Algorithms to Parse and Analyze Data
  • Identifying Algorithms to Parse and Analyze Data
  • Summarizing Algorithms to Parse and Analyze Data
  • Summarizing Methods Used to Automate Computer Decision-Making Processes
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