ACADEMY / COURSES / COURSES / ML-FUNDAMENTALS15 LESSONS · 16H 30M
COURSE 04 / 08
Machine LearningIntermediateClassical Machine Learning: Supervised Learning Foundations
Master the mathematical foundations and practical implementation of classical supervised learning algorithms. Build intuition through interactive visualizations while understanding the theory that powers modern ML.
COURSE DATASHEETv2.1
Lessons15
Total runtime16h 30m
DifficultyIntermediate
CategoryMachine Learning
TierFree
Last updated2024-12-19
LocaleEN · RU · ZH
§A · OBJECTIVES
What you’ll be able to do.
01.Understand the mathematical foundations of supervised learning
02.Master linear models: regression and classification
03.Build intuition for decision trees and their ensembles
04.Implement Support Vector Machines from scratch
05.Master model evaluation and validation techniques
06.Understand bias-variance tradeoff and regularization
07.Learn feature engineering and selection strategies
08.Apply ML algorithms to real-world problems
§B · INSTRUMENTS IN THIS COURSE
Working tools, used throughout.
FIG. 01VISUALIZATION
Graph Plotter
FIG. 02INTERACTIVE
DataFrame Explorer
FIG. 03INTERACTIVE
Feature Engineering Workbench
FIG. 04INTERACTIVE
Model Evaluation Dashboard
FIG. 05VISUALIZATION
Gradient Descent Animator
FIG. 06VISUALIZATION
SVM Explorer
FIG. 07VISUALIZATION
Probability Field
FIG. 08VISUALIZATION
Gradient Lab
§C · SYLLABUS
All lessons. Read in order, or jump.
№LESSONKINDTIME
01Mathematical Foundations of Machine LearningCONCEPT· 60 min60 min→02The Supervised Learning FrameworkCONCEPT· 60 min60 min→03Linear Regression: From Theory to PracticeCONCEPT· 60 min60 min→04Logistic Regression and ClassificationCONCEPT· 60 min60 min→05Regularization: L1, L2, and Elastic NetCONCEPT· 60 min60 min→06Decision Trees: Intuition and ImplementationCONCEPT· 75 min75 min→07Random Forests and BaggingCONCEPT· 60 min60 min→08Gradient Boosting: From AdaBoost to XGBoostCONCEPT· 75 min75 min→09Support Vector Machines: Linear CaseCONCEPT· 75 min75 min→10Kernel Methods and Non-linear SVMsCONCEPT· 75 min75 min→11Evaluation Metrics: Beyond AccuracyCONCEPT· 60 min60 min→12Cross-Validation and Model SelectionCONCEPT· 60 min60 min→13The Art of Feature EngineeringCONCEPT· 60 min60 min→14Feature Selection and Dimensionality ReductionCONCEPT· 60 min60 min→15End-to-End ML Project: From Data to DeploymentCONCEPT· 90 min90 min→