Machine Learning Basics
Master Machine Learning Basics from core theoretical foundations to practical industry applications.
Core modules and technical concepts for Machine Learning Basics.
Topic 1.1: Supervised Learning Algorithms
Foundational and applied principles of Supervised Learning Algorithms.
Linear/Logistic Regression and Regularization (L1/L2)
Comprehensive technical guide and practical walkthrough of Linear/Logistic Regression and Regularization (L1/L2).
Support Vector Machines, Decision Trees, and Random Forests
Comprehensive technical guide and practical walkthrough of Support Vector Machines, Decision Trees, and Random Forests.
Topic 1.2: Unsupervised Learning and Evaluation
Foundational and applied principles of Unsupervised Learning and Evaluation.
Clustering (K-Means, DBSCAN) and PCA Dimensionality Reduction
Comprehensive technical guide and practical walkthrough of Clustering (K-Means, DBSCAN) and PCA Dimensionality Reduction.
Cross-Validation, Bias-Variance Trade-off, and Metrics (F1, AUC-ROC)
Comprehensive technical guide and practical walkthrough of Cross-Validation, Bias-Variance Trade-off, and Metrics (F1, AUC-ROC).