All current Decision Trees Courses courses in 2024
16 Courses
Decision Trees, Random Forests, Bagging & XGBoost: R Studio
Delve into the advanced world of Decision Trees and Ensembling techniques with our comprehensive course - Decision Trees, Random Forests, Bagging & XGBoost: R Studio, offered by Udemy. This course is specifically designed for those eager to enhance their understanding and application of R programming in machine learning.
Throughout this course.
Machine Learning with Python : COMPLETE COURSE FOR BEGINNERS
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Embark on a comprehensive learning journey with the Machine Learning with Python: Complete Course for Beginners. This course, available on Udemy, is perfect for newcomers eager to explore the fascinating world of machine learning.
In this course, you will:
Understand the fundamentals of Python programming related to data science..
Decision Trees, Random Forests, AdaBoost & XGBoost in Python
Join our comprehensive course on Udemy to master Decision Trees and Ensembling techniques using Python. Delve into the world of machine learning and data analysis as you learn to implement Bagging, Random Forest, Gradient Boosting Machine (GBM), AdaBoost, and XGBoost efficiently. Perfect for data enthusiasts aiming to enhance their skills in.
人工智能导论
探索人工智能基础概念，研究数据驱动的网络模型、算法与平台。本课程梳理人工智能的关键发展节点，激发您的学习兴趣。通过此课程，您将牢牢掌握人工智能基础，为深度学习的最前沿铺路，不仅适合计算机专业学生，也对其他学科的学生适宜。
人工智能
《人工智能》课程的主要特点包括以下几个方面：
门槛较低：不需要掌握计算机基础知识，只需大学工科数学基础和一门编程语言。
内容全面：涵盖经典人工智能技术、机器学习及深度学习相关知识，系统介绍发展历程中的各种成果和走过的弯路。
实践性强：不仅提供理论讲授，还有编程实践，学生普遍对这部分内容非常感兴趣。
本课程适合希望全面了解和掌握人工智能技术.
机器学习与模式识别
《机器学习与模式识别》是为人工智能、工业智能以及自动化专业设计的专业课程，研究如何利用机器学习方法完成模式识别任务中涉及的基本概念、原理和实现方法。
课程内容涵盖模式识别与机器学习的基本任务、核心概念与方法，包括贝叶斯决策论与概率模型估计、线性分类与回归、支持向量机、决策树、集成学习、模型评估、特征学习与选择，以及人工神经网络与深度学习。另外，本.
