Machine Learning: Executive Briefing

Machine Learning: Executive Briefing

 


 

 


Machine Learning: Executive Briefing

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 0h 39m | 466 MB
eLearning | Skill level: All Levels

A practical, pragmatic, jargon-free introduction to Machine Learning. Quickly cover the most important ideas and concepts — and learn approaches and techniques to apply Machine Learning in your own career.

Tech leaders need a fundamental understanding of the tools and technologies their teams use to build solutions. This course, Machine Learning: Executive Briefing, takes a fast-paced, practical, and pragmatic approach to Machine Learning. First, we’ll explore common cliches around Machine Learning and how they get in the way of learning. Next, you’ll get clear on the most important jargon and terminology you need to know. We’ll then cover the steps and sequence of developing a Machine Learning application, Finally, you will explore the most common practical applications of Machine Learning in real-world projects. When you’re finished with this course, you will have the skills and knowledge to help implement Machine Learning to support your product, team, or organization.

Introduction – Solving New Kinds of Problems
1 How Anyone Can Write an Introduction to Machine Learning
2 Thinking Clearly About Machine Learning
3 Comparing ‘Conventional’ Programming and Machine Learning
4 Embracing Everyday Machine Learning Examples

But What Is Machine Learning, Really
5 Comparing Multiple Definitions of Machine Learning
6 The Difference between AI and ML
7 Writing a More Useful Working Definition of Machine Learning

Training a Machine Learning Model
8 Step 1 – Beginning with Existing Data
9 Step 2 – Analyzing Data to Identify Patterns
10 Exploring Machine Learning Platforms and Frameworks
11 Making Predictions with a Trained Model

The Marketplace of Machine Learning
12 Understanding ML Classification Algorithms
13 Exploring Regression in Machine Learning
14 Comparing Supervised and Unsupervised Learning Algorithms
15 Reviewing the Process – Where to Go from Here


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