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Book 4: Learning from Data
Curriculum
8 Sections
33 Lessons
10 Weeks
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Chapter 1: Tools and Data for Machine Learning Projects
3
1.1
The UCI (University of California-Irvine) ML Repository
10 mins
1.2
Introducing the Rattle package
10 mins
1.3
Using Rattle with iris
10 mins
Chapter 2: Decisions, Decisions, Decisions
5
2.1
Decision Tree Components
10 mins
2.2
Decision Trees in R
10 mins
2.3
Decision Trees in Rattle
10 mins
2.4
Project: A More Complex Decision Tree
10 mins
2.5
Suggested Project: Titanic
10 mins
Chapter 3: Into the Forest, Randomly
4
3.1
Growing a Random Forest
10 mins
3.2
Random Forests in R
10 mins
3.3
Project: Identifying Glass
10 mins
3.4
Suggested Project: Identifying Mushrooms
10 mins
Chapter 4: Support Your Local Vector
4
4.1
Some Data to Work With
10 mins
4.2
Separability: It’s Usually Nonlinear
10 mins
4.3
Support Vector Machines in R
10 mins
4.4
Project: House Parties
10 mins
Chapter 5: K-Means Clustering
3
5.1
How It Works
10 mins
5.2
K-Means Clustering in R
10 mins
5.3
Project: Glass Clusters
10 mins
Chapter 6: Neural Networks
5
6.1
Networks in the Nervous System
10 mins
6.2
Artificial Neural Networks
10 mins
6.3
Neural Networks in R
10 mins
6.4
Project: Banknotes
10 mins
6.5
Suggested Projects: Rattling Around
10 mins
Chapter 7: Exploring Marketing
3
7.1
Analyzing Retail Data
10 mins
7.2
Enter Machine Learning
10 mins
7.3
Suggested Project: Another Data Set
10 mins
Chapter 8: From the City That Never Sleeps
6
8.1
Examining the Data Set
10 mins
8.2
Warming Up
10 mins
8.3
Quick Suggested Project: Airline Names
10 mins
8.4
Suggested Project: Departure Delays
10 mins
8.5
Quick Suggested Project: Analyze Weekday Differences
10 mins
8.6
Suggested Project: Delay and Weather
10 mins
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