АКАДЕМИЯ / КУРСЫ / COURSES / ML-FUNDAMENTALS15 LESSONS · 16Ч 30М
КУРС 04 / 08
Машинное обучениеСреднийClassical 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.
ХАРАКТЕРИСТИКИ КУРСАv2.1
Уроков15
Общая длительность16ч 30м
СложностьСредний
КатегорияМашинное обучение
ТарифБесплатно
Обновлено2024-12-19
ЛокальEN · RU · ZH
§A · ЦЕЛИ
Что вы научитесь делать.
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 · ПРОГРАММА
Все уроки. По порядку или вразброс.
№УРОКТИПВРЕМЯ
01Mathematical Foundations of Machine LearningТЕОРИЯ· 60 min60 min→02The Supervised Learning FrameworkТЕОРИЯ· 60 min60 min→03Linear Regression: From Theory to PracticeТЕОРИЯ· 60 min60 min→04Logistic Regression and ClassificationТЕОРИЯ· 60 min60 min→05Regularization: L1, L2, and Elastic NetТЕОРИЯ· 60 min60 min→06Decision Trees: Intuition and ImplementationТЕОРИЯ· 75 min75 min→07Random Forests and BaggingТЕОРИЯ· 60 min60 min→08Gradient Boosting: From AdaBoost to XGBoostТЕОРИЯ· 75 min75 min→09Support Vector Machines: Linear CaseТЕОРИЯ· 75 min75 min→10Kernel Methods and Non-linear SVMsТЕОРИЯ· 75 min75 min→11Evaluation Metrics: Beyond AccuracyТЕОРИЯ· 60 min60 min→12Cross-Validation and Model SelectionТЕОРИЯ· 60 min60 min→13The Art of Feature EngineeringТЕОРИЯ· 60 min60 min→14Feature Selection and Dimensionality ReductionТЕОРИЯ· 60 min60 min→15End-to-End ML Project: From Data to DeploymentТЕОРИЯ· 90 min90 min→