book

Artificial Intelligence for Business, 2nd Edition

by Doug Rose

December 2020

Beginner to intermediate

272 pages

6h 8m

English

Pearson FT Press

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What Is Intelligence?Testing Machine IntelligenceThe General Problem SolverStrong and Weak Artificial IntelligenceArtificial Intelligence PlanningLearning over MemorizingChapter Takeaways
Practical Applications of Machine LearningArtificial Neural NetworksThe Fall and Rise of the PerceptronBig Data ArrivesChapter Takeaways
Expert System Versus Machine LearningSupervised Versus Unsupervised LearningBackpropagation of ErrorsRegression AnalysisChapter Takeaways
Intelligent RobotsNatural Language ProcessingThe Internet of ThingsChapter Takeaways
Understanding the Concept of Big DataTeaming Up with a Data ScientistMachine Learning and Data Mining: What’s the Difference?Making the Leap from Data Mining to Machine LearningTaking the Right ApproachChapter Takeaways
Chapter Takeaways
How a Machine LearnsWorking with DataApplying Machine LearningDifferent Types of LearningChapter Takeaways
Supervised Machine LearningUnsupervised Machine LearningSemi-Supervised Machine LearningReinforcement LearningChapter Takeaways
Decision Treesk-Nearest Neighbork-Means ClusteringRegression AnalysisNäive BayesChapter Takeaways
Fitting the Model to Your DataChoosing AlgorithmsEnsemble ModelingDeciding on a Machine Learning ApproachChapter Takeaways
Start Asking QuestionsDon’t Mix Training Data with Test DataDon’t Overstate a Model’s AccuracyKnow Your AlgorithmsChapter Takeaways
Why the Brain Analogy?Just Another Amazing AlgorithmGetting to Know the PerceptronSqueezing Down a Sigmoid NeuronAdding BiasChapter Takeaways
Feeding Data into the NetworkWhat Goes on in the Hidden LayersUnderstanding Activation FunctionsAdding WeightsAdding BiasChapter Takeaways
Starting with Random Weights and BiasesMaking Your Network Pay for Its Mistakes: The Cost FunctionCombining the Cost Function with Gradient DescentUsing Backpropagation to Correct for ErrorsTuning Your NetworkEmploying the Chain RuleBatching the Data Set with Stochastic Gradient DescentChapter Takeaways
Solving Classification ProblemsSolving Clustering ProblemsChapter Takeaways
Obtaining Enough Quality DataKeeping Training and Test Data SeparateCarefully Choosing Your Training DataTaking an Exploratory ApproachChoosing the Right Tool for the JobChapter Takeaways
Extracting Meaning from Text and Speech with NLUDelivering Sensible Responses with NLGAutomating Customer ServiceReviewing the Top NLP Tools and ResourcesChapter Takeaways
Choosing Natural Language TechnologiesReview the Top Tools for Creating Chatbots and Virtual AgentsChapter Takeaways
Choosing Between Automated and Intuitive Decision-MakingGathering Data in Real Time from IoT DevicesReviewing Automated Decision-Making ToolsChapter Takeaways
Machine Learning Is Really about Labeling DataLooking at What Machine Learning Can DoUse Your Power for Good, Not Evil: Machine Learning EthicsReview the Top Machine Learning ToolsChapter Takeaways
Separating Intelligence from AutomationAdding Layers for Deep LearningConsidering Applications for Artificial Neural NetworksReviewing the Top Deep Learning ToolsChapter Takeaways

Overview

Millions of non-technical professionals and leaders want to understand Artificial Intelligence (AI) and Machine Learning (ML) — whether to improve their businesses, be more effective citizens, consumers or policymakers, or just out of sheer curiosity. Until now, most books on the subject have either been too complicated and mathematical, or have simply avoided the big picture by focusing on the use of specific software libraries. In Artificial Intelligence for Business, Doug Rose bridges the gap, offering today’s most accessible and useful introduction to AI and ML technologies — and what they can and can’t do.

Rose begins by tracing AI’s evolution from the early 1950s to the present, illuminating core ideas that still drive its development. Next, he explores recent innovations that have reinvigorated the field by providing the “big data” that makes machine learning so powerful – innovations such as GPS, social media and electronic transactions. Finally, he explains how today’s machines learn by combining powerful processing, advanced algorithms, and artificial neural networks that mimic the human brain.

Throughout, he illustrates key concepts with practical examples that help you connect AI, ML, and neural networks to specific problems and solutions. Step by step, he systematically demystifies these powerful technologies, removing the fear, bewilderment, and advanced math — so you can understand the new possibilities they create, and start using them.