Machine Learning Foundations

Classifications / Machine Learning / Machine Learning Foundations

TRACK

A decision-centered introduction to supervised learning: frame prediction problems, trace how models learn, compare model biases, evaluate generalization honestly, and diagnose what to improve next.

How do I turn a supervised prediction problem into a trustworthy learning workflow: choose a representation and model bias, evaluate generalization, and diagnose what to improve next?

16 lessons

Continues to AI Research Literacy and Paper Reading/ Causal ML and Decision Making/ Deep Learning and Neural Networks/ Large-Scale Data Mining/ ML Systems and Training Infrastructure/ MLOps, DataOps, and Model Operations/ Optimization, Generalization, and Learning Theory/ Probabilistic Modeling and Bayesian Inference

LESSONS

1. 001
   Machine Learning Fundamentals
   [TODO]
2. 002
   Linear Regression Fundamentals
   [TODO]
3. 003
   Gradient Descent Fundamentals
   [TODO]
4. 004
   Polynomial Features and Regularization
   [TODO]
5. 005
   Logistic Regression Fundamentals
   [TODO]
6. 006
   Decision Trees and Rule-Based Splits
   [TODO]
7. 007
   Naive Bayes for Text Classification
   [TODO]
8. 008
   Classification Metrics and Trade-Offs
   [TODO]
9. 009
   Support Vector Machines and Margin
   [TODO]
10. 010
    K-Nearest Neighbors and Similarity
    [TODO]
11. 011
    Random Forest and Bagging
    [TODO]
12. 012
    Gradient Boosting and Sequential Correction
    [TODO]
13. 013
    Feature Engineering and Representation
    [TODO]
14. 014
    Cross-Validation and Reliable Evaluation
    [TODO]
15. 015
    Overfitting, Underfitting, and Regularization
    [TODO]
16. 016
    Learning Curves and Model Diagnosis CAPSTONE
    [TODO]

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