Hands-On Machine Learning with R Book Club
Welcome
- Book club meetings
- Pace
1 Introduction to Machine Learning
- SLIDE 1
- Meeting Videos
  - Cohort 1
2 Modeling Process
- SLIDE 1
- Meeting Videos
  - Cohort 1
3 Feature & Target Engineering
4 Linear Regression
- SLIDE 1
- Meeting Videos
  - Cohort 1
5 Logistic Regression
- SLIDE 1
- Meeting Videos
  - Cohort 1
6 Regularized Regression
7 Multivariate Adaptive Regression Splines (MARS)
8 K-Nearest Neighbors
- SLIDE 1
- Meeting Videos
  - Cohort 1
9 Decision Trees
10 Bagging (Bootstrap Aggregating)
11 Random Forests
- SLIDE 1
- Meeting Videos
  - Cohort 1
12 Gradient Boosting
- SLIDE 1
- Meeting Videos
  - Cohort 1
13 Deep Learning
- SLIDE 1
- Meeting Videos
  - Cohort 1
14 Support Vector Machines
- SLIDE 1
- Meeting Videos
  - Cohort 1
15 Stacked Models
16 Interpretable Machine Learning
17 Principal Components Analysis
- SLIDE 1
- Meeting Videos
  - Cohort 1
18 Generalized Low Rank Models
- SLIDE 1
- Meeting Videos
  - Cohort 1
19 Autoencoders
20 K-means Clustering
- SLIDE 1
- Meeting Videos
  - Cohort 1
21 Hierarchical Clustering
- SLIDE 1
- Meeting Videos
  - Cohort 1
22 Model-based Clustering
- SLIDE 1
- Meeting Videos
  - Cohort 1
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Hands-On Machine Learning with R Book Club

Chapter 9 Decision Trees

Learning objectives:

Structure
Partitioning
How deep?
AMES Housing example
Feature interpretation
Final thoughts