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RESOURCE: 50+ Ready-to-use Machine Learning R recipes for you!
Introduction
What do you get here?
Reference reading for you
Step 0: Getting Started With R
Some R basics (including package installation)
Step 1: Analyze Data
Load Data
Data Summarization
Data Visualization
Step 2: Prepare Data
Data Cleaning
Feature Selection
Transform Data
Step 3: Algorithms
Linear Regression
Non-Linear Regression
Linear Classification
Non-Linear Classification
Caret Algorithms
Step 4: Evaluate Algorithms
Resampling Methods
Metrics
Model Selection
Step 5: Improve Results
Tune Algorithms
Ensemble Algorithms
Step 6: The Final Model
Predict
Standalone model using regression
Save and Load Model
BONUS: Practical applications using case studies
Regression
Binary Classification
Multiclass Classification
Some R basics (including package installation)
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