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Machine Learning/Learning Center/Introduction to ML

Introduction to Machine Learning

Master the fundamentals of machine learning

ML Overview & Applications
Module 1
Understand what machine learning is, explore real-world applications from medical screening to autonomous vehicles, and learn about ML's impact on modern technology.
Classic ML Definition
Turing Award Winners
Real-World Applications
ML vs Data Mining & Big Data
Top Journals & Conferences
Basic Terminology & Concepts
Module 2
Master essential ML vocabulary including datasets, features, labels, and learning paradigms. Understand the difference between classification, regression, and clustering.
Data-Related Terms
Task Types (Classification, Regression)
Learning Paradigms
Generalization & I.I.D Assumption
No Free Lunch Theorem
Model Evaluation & Selection
Module 3
Learn how to evaluate ML models using training/test errors, cross-validation, and performance metrics. Understand overfitting, underfitting, and the bias-variance tradeoff.
Training vs Test Error
Evaluation Methods (Hold-out, Cross-validation)
Performance Metrics (Confusion Matrix, ROC)
Statistical Tests
Bias-Variance Tradeoff
Classical ML Algorithms
Module 4
Explore 10 classical machine learning algorithms including linear regression, decision trees, SVM, and neural networks with watermelon dataset examples for each.
10 Classical Algorithms
Algorithm Applications
AI History Timeline
Future Challenges (Robustness)
Recent ML Progress