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  5. Introduction to the Principles and Practice of Amazon Machine Learning

Course Introduction

The course is part of these learning paths

Introduction to Machine Learning on AWS

course-steps 4 certification 1 lab-steps 3

Big Data – Specialty Certification Preparation for AWS

course-steps 14 lab-steps 5 quiz-steps 3

Contents

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Introduction
1
Course Introduction6m 2s
2
Problem types3m 23s
3
When to use Machine Learning4m 12s
4
Framing Problems2m 49s
Working with Data Sources
5
Acquiring Data9m 32s
6
CSV Cleanup15m 20s
Data Manipulation Within Amazon Machine Learning
7
Analyzing Data8m 58s
8
Data Insight12m 51s
Working with Machine Learning Models
9
Machine Learning Algorithms5m 41s
10
Feature Processing9m 12s
11
Create a Model8m 27s
12
Model Evaluation8m 18s
13
Evaluate Model6m 40s
14
Improving Models4m 15s
Predictions
15
Understanding Predictions4m 44s
16
Batch Predictions6m 59s
17
Real-Time Predictions10m 41s
18
Conclusion3m 27s
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Overview
Transcript
DifficultyAdvanced
Duration2h 12m
Students1395

Description

When we saw how incredibly popular our blog post on Amazon Machine Learning was, we asked data and code guru James Counts to create this fantastic in-depth introduction to the principles and practice of Amazon Machine Learning so we could completely satisfy the demand for ML guidance. If you've got a real-world need to apply predictive analysis to large data sources - for fraud detection or customer churn analysis, perhaps - then this course has everything you'll need to know to get you going.
James has got the subject completely covered:

  • What, exactly, Machine Learning can do
  • Why and when you should use it
  • Working with data sources
  • Manipulating data within Amazon Machine Learning to ensure a successful model
  • Working with Machine learning models
  • Generating accurate predictions

About the Author

Students1395
Courses1

James is most happy when creating or fixing code. He tries to learn more and stay up to date with recent industry developments.

James recently completed his Master’s Degree in Computer Science and enjoys attending or speaking at community events like CodeCamps or user groups.

He is also a regular contributor to the ApprovalTests.net open source projects, and is the author of the C++ and Perl ports of that library.