Machine Learning in a Random Forest

Celebrating the KSML Science Comic app surpassing 10,000 downloads on Google Play, we now officially release “Machine Learning in a Random Forest,” the Ultimate Edition.

Module 1: model, evaluation, overfitting, train-test-split

1. The model that’s not a girl!
2. Supervised learning: who’s supervising the forest?
3. Unsupervised learning helps detect shady people
4. Parameters and Loss function
5. Simple Linear Regression Review: Sunlight & Selfie
6. Evaluation measure: MSE versus MAE, RMSE
7. model generalization overfitting, underfitting
8. train-test split
a crab’s feeling of underfitting

Module 2: Regression

Multiple linear regression
Residual plot for model diagnostic
Non-constant variance in linear regression: a duck’s mood swing problem
Sum of Squares & coefficients of determination with Python & R codes
Limitation of R-squared, Adjusted R squared

Module 3: Feature Selection & Regularization

Feature selection & Model Selection
Forward feature selection: a step by step example

Backward feature selection + example
Stepwise Feature Selection +example
Comparing forward, backward, stepwise feature selection
Review: Maximum Likelihood Estimation
Lasso & Ridge regression: method & codes
ElasticNet Regression: Method & Codes

Module 4: Hyper parameter tuning

Grid search and train-validation-test split for hyperparameter tuning-intro
Hyperparameter tuning by train-validation-test split – process & example

Module 5: Classification

What’s classification
K-Nearest Neighbors (KNN): an introduction
KNN classification: practical notices & implementation using Python & R
Logistic Regression: method + Python & R codes
Logistic regression with L1 or L2 penalty (advanced)
Support Vector Machine + Python & R Codes
Kernel tricks, SVM properties & kernel choice
What’s a decision tree? Where should the squirrels store her acorns?

Module 6: Unsupervised learning

Dimension reduction and Principal Component Analysis (PCA)
Feature selection versus dimensionality reduction
K-Means Clustering

Module 7: Advanced topics

Bootstrap: introductory comics & quizzes
Bagging & Random Forest: intro & quizzes

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