学院 / 课程 / COURSES / ML-FUNDAMENTALS15 LESSONS · 16时 30分
课程 04 / 08
机器学习中级Classical 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.
课程数据表v2.1
课时15
总时长16时 30分
难度中级
分类机器学习
类型免费
更新于2024-12-19
语言EN · RU · ZH
§A · 目标
您将能够做什么。
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 · 大纲
所有课程。按顺序阅读，或跳转。
№课程类型时间
01Mathematical Foundations of Machine Learning概念· 60 min60 min→02The Supervised Learning Framework概念· 60 min60 min→03Linear Regression: From Theory to Practice概念· 60 min60 min→04Logistic Regression and Classification概念· 60 min60 min→05Regularization: L1, L2, and Elastic Net概念· 60 min60 min→06Decision Trees: Intuition and Implementation概念· 75 min75 min→07Random Forests and Bagging概念· 60 min60 min→08Gradient Boosting: From AdaBoost to XGBoost概念· 75 min75 min→09Support Vector Machines: Linear Case概念· 75 min75 min→10Kernel Methods and Non-linear SVMs概念· 75 min75 min→11Evaluation Metrics: Beyond Accuracy概念· 60 min60 min→12Cross-Validation and Model Selection概念· 60 min60 min→13The Art of Feature Engineering概念· 60 min60 min→14Feature Selection and Dimensionality Reduction概念· 60 min60 min→15End-to-End ML Project: From Data to Deployment概念· 90 min90 min→