Classical Machine Learning in Depth for Tabular and Unsupervised Work
by cinojosa0705 · Classical ML algorithms in depth · taken 1×
A 10-hour, mastery-oriented course on decision trees, random forests, gradient boosting, SVMs, k-means, PCA, t-SNE, and UMAP, with from-scratch implementations and a Kaggle-style capstone. Designed from the supplied learner profile and curriculum constraints.
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modules
12
lessons
11h
estimated work · target 10h
What's inside
52 tasks · 48 flashcards · 73 concepts · 37 recall prompts
Sample recall prompts — this is what studying it feels like
- From memory, define Gini impurity and explain how a decision tree converts node impurities into split gain.
- Explain why an unrestricted decision tree can fit training data extremely well yet generalize poorly, and name three controls that reduce this problem.
- Contrast the decision boundary and feature assumptions of a classification tree with logistic regression.
Syllabus
Trees from First Principles
Build decision trees and random forests from scratch, then reason about their bias, variance, and diagnostics.
- Classification Trees: Splits, Impurity, and Stopping flashcard · quiz · project · reading · 8 cards
- Regression Trees and Recursive Implementation note · quiz · project · reading
- Random Forests: Bagging, Decorrelation, and Out-of-Bag Evaluation reading · flashcard · quiz · project · teach_back · 8 cards
Boosted Trees for Tabular Prediction
Understand boosting as stagewise loss minimization, then use XGBoost and LightGBM within a leakage-resistant model-selection workflow.
- Gradient Boosting as Functional Gradient Descent flashcard · quiz · project · reading · 8 cards
- XGBoost: Regularized Second-Order Tree Boosting quiz · note · project · reading
- LightGBM and a Disciplined Tabular Workflow reading · flashcard · quiz · project · essay · 8 cards
Margins and Unsupervised Structure
Develop geometric and practical working knowledge of SVMs, then learn what k-means optimizes and how to diagnose misleading clusters.
- Support Vector Machines: Margins, Hinge Loss, and Kernels flashcard · quiz · project · reading · 8 cards
- SVM Practice: Scaling, Hyperparameters, and Multiclass Decisions note · quiz · project · reading
- k-Means: Objective, Lloyd's Algorithm, and Diagnostics note · quiz · project · teach_back · reading
Dimensionality Reduction and Integrated Practice
Derive PCA from covariance eigendecomposition, use t-SNE and UMAP responsibly, and complete an end-to-end tabular capstone.
- PCA from Covariance Eigendecomposition flashcard · quiz · project · reading · 8 cards
- t-SNE and UMAP as Responsible Visualization Tools reading · quiz · project · teach_back
- Kaggle-Style Tabular Capstone essay · reading · note · quiz · project