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Pragmatic AI and Machine Learning Core Principles LiveLessons (Video Training)

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  • Copyright 2020
  • Edition: 1st
  • Online Video
  • ISBN-10: 0-13-655467-9
  • ISBN-13: 978-0-13-655467-7

4+ Hours of Video Instruction

Machine Learning is the scientific study of models and algorithms that train a computer to make predictions without explicit instruction. Machine Learning is a subset of Artificial Intelligence, which can be defined as computers that mimic human problem-solving. This video demonstrates the core principles of Machine Learning and AI, including supervised Machine Learning, unsupervised Machine Learning, neural networks, and social network theory.

Learn to master the foundational concepts of AI and Machine Learning. The LiveLessons video starts with an overview of Artificial Intelligence and covers applications of AI across industries and opportunities in AI for individuals, organizations, and ecosystems. It also covers the difference between narrow, general, and super AI.


Shore up the foundational knowledge necessary to work with Artificial Intelligence and Machine Learning! This LiveLesson video covers the core principles of Artificial Intelligence and Machine Learning, including how to frame a problem in terms of Machine Learning and how Machine Learning is different than statistics. Learn about fundamental concepts including nearest neighbors, decision trees, and neural networks. The video wraps up covering timely machine learning topics such as cluster analysis, dimensionality reduction, and social networks.

Skill Level

  • Beginning to Intermediate

What You Will Learn

  • Learn key concepts in Machine Language, AI, and cloud computing and how these technologies can be used in business assessment and growth
  • Meet the future head on with core coverage of AI, ML, and data science essentials
  • Distinguish between narrow, general, and super AI
  • Frame ML problems
  • Reason about Gradient Descent
  • Use neural networks
  • Understand social network theory

Who Should Take This Course


  • Data scientist (current or aspiring)
  • ML engineer who wants a stronger conceptual foundation
  • Business exec who needs to understand AI and ML concepts
  • Student who needs additional resources in a data-related course

Course Requirements


  • Basic understanding of high-school math and linear algebra

About Pearson Video Training

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que. Topics include IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.

Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.

Sample Content

Table of Contents


Lesson 1: Decoding Artificial Intelligence
1.1 Learn the evolution of AI
1.2 Learn the difference between narrow, general, and super AI
1.3 Understand applications of AI across industries
1.4 Learn about opportunities in AI for individuals, organizations, and the ecosystem

Lesson 2: Learn the Principles of Machine Learning--Part I
2.1 Learn the basics of Machine Learning
2.2 Understand the framing of Machine Learning problems
2.3 Comprehend the Nearest Neighbors algorithm
2.4 Learn decision trees
2.5 Learn the intuition behind Gradient Descent
2.6 Understand Neural Network theory
2.7 Comprehend the fundamentals of Supervised Learning

Lesson 3: Learn the Principles of Machine Learning--Part 2
3.1 Understand Cluster Analysis
3.2 Learn Expectation-Maximization
3.3 Comprehend Dimensionality Reduction theory
3.4 Understand Social Network theory
3.5 Learn Recommender Systems
3.6 Understand the fundamentals of Unsupervised Learning

Summary of Concepts


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