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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-course/
课程评论：没有评论
Coursera 机器学习从入门到进阶课程总结 本课程旨在帮助学习者踏上机器学习工程师之路，通过 Python 深入学习数据分析、数据可视化以及强大的机器学习算法。课程面向有一定编程基础的初学者以及希望转向机器学习领域的经验开发者和数据科学家。 课程内容分为以下几个部分： * **第一部分：数据预处理** - 教授数据清洗、转换等预处理技术。 * **第二部分：回归** - 涵盖简单线性回归、多元线性回归、多项式回归、支持向量回归 (SVR)、决策树回归和随机森林回归等多种回归算法。 * **第三部分：分类** - 详细讲解逻辑回归、K近邻 (K-NN)、支持向量机 (SVM)、核函数支持向量机 (Kernel SVM)、朴素贝叶斯、决策树分类和随机森林分类等分类算法。 * **第四部分：聚类** - 介绍 K-Means 聚类和层次聚类。 此外，本课程还提供 Python 代码模板，供学习者在自己的项目中使用。课程由 ML 爱好者团队精心设计，以简单易懂的方式讲解复杂的理论、算法和编程库，引导学习者一步步掌握机器学习的核心技能。
Are you ready to start your path to becoming a Machine Learning Engineer!This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Machine Learning as well as Data Scientist!Interested in the field of Machine Learning? Then this course is for you!This course has been designed by Code Warriors the ML Enthusiasts so that we can share our knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way.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 is fun and exciting, but 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 Regression.Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest ClassificationPart 4 - Clustering: K-Means, Hierarchical Clustering.And as a bonus, this course includes Python code templates which you can download and use on your own projects.
