English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 4h 01m | 3.64 GB
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.
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
Roles:
- 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
Table of Contents
01 Pragmatic AI and Machine Learning Core Principles – Introduction
02 Learning objectives
03 1.1 Learn the evolution of AI
04 1.2 Learn the difference between narrow, general, and super AI
05 1.3 Understand applications of AI across industries
06 1.4 Learn about opportunities in AI for individuals, organizations, and the ecosystem
07 Learning objectives
08 2.1 Learn the basics of Machine Learning
09 2.2 Understand the framing of Machine Learning problems
10 2.3 Comprehend the Nearest Neighbors algorithm
11 2.4 Learn decision trees
12 2.5 Learn the intuition behind Gradient Descent
13 2.6 Understand Neural Network theory
14 2.7 Comprehend the fundamentals of Supervised Learning
15 Learning objectives
16 3.1 Understand Cluster Analysis
17 3.2 Learn Expectation-Maximization
18 3.3 Comprehend Dimensionality Reduction theory
19 3.4 Understand Social Network theory
20 3.5 Learn Recommender Systems
21 3.6 Understand the fundamentals of Unsupervised Learning
22 3.7 Challenges and opportunities
23 Pragmatic AI and Machine Learning Core Principles – Summary
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