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Introduction to Machine Learning With R
Welcome
Welcome to the Course! (11:11)
Supervised Learning
Data Analyst, Teacher (16:56)
Why Trees? (8:33)
The Data Sets
The Data (14:17)
Exploratory Data Analysis (EDA) (36:48)
Lab 1 - Titanic EDA
Lab Instructions
Lab Walkthrough (41:44)
Classification Trees
Classification Tree Intuition (16:53)
Overfitting Intuition (15:20)
Gini Impurity (14:36)
Gini Change (22:18)
Many Categories Impurity (10:20)
Numeric Feature Impurity (8:55)
Classification Trees With Tidymodels (24:12)
Lab 2 - Titanic Classification Tree
Lab Instructions
Lab Walkthrough (22:58)
Awesome Classification Trees
Under/Overfitting (12:47)
The Bias-Variance Tradeoff (14:37)
Supervising the Data (21:17)
Model Tuning Intuition (23:36)
Classification Tree Pruning (15:39)
Measuring Awesomeness (22:18)
Model Tuning With Tidymodels (30:54)
Lab 3 - Titanic Tree Tuning
Lab Instructions
Lab Walkthrough (31:24)
Feature Engineering
Feature Engineering Intuition (18:36)
Data Leakage (15:10)
Decision Tree Feature Engineering (14:50)
Missing Data (13:17)
Lab 4 - Titanic Feature Engineering
Lab Instructions
Lab Walkthrough (35:15)
Regression Trees
Regression Trees Basics (10:29)
Numeric Feature SSE (5:48)
Many Categories SSE (5:06)
Regression Trees With Tidymodels (15:14)
The Mighty Random Forest
Bad, Tree! Bad! (9:15)
Ensembles (7:28)
Bagging (17:32)
Feature Randomization (12:13)
Tuning Random Forests (15:38)
Feature Importance (19:47)
Random Forests With Tidymodels (15:54)
Lab 5 - Titanic Random Forest
Lab Instructions
Lab Walkthrough (21:21)
Course Wrap-up
Want to Kaggle? (13:10)
Additional Resources (4:17)
Feature Engineering Intuition
If you are interested in learning more about feature engineering, then be sure to check out the free online book,
Feature Engineering and Selection
.
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