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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machinelearning/
课程评论：没有评论
课程名称：机器学习A-Z：AI，Python与R + ChatGPT奖 [2025] 课程概述： 你对机器学习领域感兴趣吗？那么这个课程非常适合你！课程由一位数据科学家和机器学习专家设计，旨在以简单易懂的方式分享复杂的理论、算法和编码库知识。全球超过一百万名学生信任此课程。我们将一步步引导你进入机器学习的世界。在每一个教程中，你都会学习到新技能，并加深对这一具有挑战性且收入丰厚的数据科学子领域的理解。 课程可以通过Python教程、R教程或两者兼修来完成，让你选择与职业发展相关的编程语言。课程内容既有趣又令人兴奋，同时深入探讨机器学习的各个方面。课程结构如下： 第一部分 - 数据预处理 第二部分 - 回归：简单线性回归、多重线性回归、多项式回归、支持向量回归、决策树回归、随机森林回归 第三部分 - 分类：逻辑回归、K最近邻、支持向量机、核支持向量机、朴素贝叶斯、决策树分类、随机森林分类 第四部分 - 聚类：K均值聚类、层次聚类 第五部分 - 关联规则学习：Apriori算法、Eclat算法 第六部分 - 强化学习：上置信界、汤普森采样 第七部分 - 自然语言处理：词袋模型及NLP算法 第八部分 - 深度学习：人工神经网络、卷积神经网络 第九部分 - 降维：主成分分析（PCA）、线性判别分析（LDA）、核主成分分析 第十部分 - 模型选择与提升：k折交叉验证、参数调整、网格搜索、XGBoost 每个部分内部的每个章节都是独立的，因此你可以选择从头到尾学习整个课程，或者直接进入特定章节，学习当前职业所需的内容。此外，课程还包含大量基于真实案例的实践练习，不仅让你了解理论，还能够动手实践，构建自己的模型。最重要的是，课程提供了Python和R代码模板，方便你在自己的项目中使用。
Interested in the field of Machine Learning? Then this course is for you!This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.Over 1 Million students world-wide trust this course.We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.This course can be completed by either doing either the Python tutorials, or R tutorials, or both - Python & R. Pick the programming language that you need for your career.This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way:Part 1 - Data PreprocessingPart 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest RegressionPart 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest ClassificationPart 4 - Clustering: K-Means, Hierarchical ClusteringPart 5 - Association Rule Learning: Apriori, EclatPart 6 - Reinforcement Learning: Upper Confidence Bound, Thompson SamplingPart 7 - Natural Language Processing: Bag-of-words model and algorithms for NLPPart 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural NetworksPart 9 - Dimensionality Reduction: PCA, LDA, Kernel PCAPart 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoostEach section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now.Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.And last but not least, this course includes both Python and R code templates which you can download and use on your own projects.
