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所在平台: Udemy |
课程主页: https://www.udemy.com/course/decision-trees-for-machine-learning/
课程评论：没有评论
**课程名称:** 从零开始的决策树机器学习 **课程概述:** 本课程深入探讨机器学习领域中备受瞩目的决策树算法。决策树在当今的 Kaggle 竞赛中占据主导地位，掌握它们将让你在未来的挑战中更具优势。 课程内容涵盖了决策树算法的基础知识，包括 CHAID、ID3、C4.5、CART 以及回归树，并着重于它们的实际应用。此外，课程还将介绍装袋（Bagging）和增强（Boosting）方法，如随机森林（Random Forest）和梯度提升（Gradient Boosting），以提升决策树的准确性。最后，课程将聚焦于 LightGBM、XGBoost 和 Chefboost 等基于树的模型框架。 学员将有机会从零开始，使用 Python 构建自己的决策树框架，并通过循序渐进的练习深刻理解核心概念。 **目标受众:** 本课程适合对机器学习、数据科学和数据挖掘感兴趣的学习者。
Decision trees are one of the hottest topics in Machine Learning. They dominate many Kaggle competitions nowadays. Empower yourself for challenges.This course covers both fundamentals of decision tree algorithms such as CHAID, ID3, C4.5, CART, Regression Trees and its hands-on practical applications. Besides, we will mention some bagging and boosting methods such as Random Forest or Gradient Boosting to increase decision tree accuracy. Finally, we will focus on some tree based frameworks such as LightGBM, XGBoost and Chefboost.We will create our own decision tree framework from scratch in Python. Meanwhile, step by step exercises guide you to understand concepts clearly.This course appeals to ones who interested in Machine Learning, Data Science and Data Mining.
