|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/prerequisites-to-machine-learning-a-beginners-guide/
课程评论：没有评论
课程名称：机器学习入门的先决条件：初学者指南 课程概述： 欢迎机器学习爱好者！你是否在学习机器学习过程中犯了99%的新手常见错误？学习的顺序至关重要。理解某些基础概念对于后续学习的成功至关重要。如果你对学习机器学习感兴趣，却不知道从何入手，那么这个课程正适合你。 本课程“机器学习入门的先决条件：初学者指南”旨在为你提供清晰的学习路径，帮助你掌握机器学习的概念和实现。课程内容生动有趣，涵盖学习机器学习所需的所有先决知识，具体安排如下： 第一部分：引言 - 课程概述 - 认识你的讲师 - 机器学习简介 - Google Colab Notebook介绍 第二部分：机器学习的Python基础 - 打印语句和变量 - 函数 - 范围、列表、元组 - 模块导入 第三部分：机器学习的包和统计 - NumPy - Pandas - Matplotlib - 均值、中位数和众数 - 方差和标准差 此外，课程设置了实践练习，确保学员不仅能掌握理论知识，还能进行操作实践。课程还提供可下载的资源，包括在Google Colab Notebook上执行的Python代码和数据集，供你练习或在个人项目中使用，作为额外的收益。 这个课程为想要入门机器学习的初学者提供了理想的起点。
Hello Machine Learning Enthusiasts!Are you making the same mistakes that 99% of fresh machine learning students do?Yes…Order is important to learn anything. And if you guessed correctly, you realize how important it is to understand certain things before moving on to others.So, if you are interested to learn Machine Learning, but no idea where to start from, then this course is exactly for you.This course "Prerequisites to Machine Learning: A Beginners Guide" has been designed in such a way that you will get clear path to move into learning the concepts and implementations of machine learning.This course is entertaining and interesting, and it covers all the prerequisites to learn Machine Learning. It is organized in the following manner:Section-1: IntroductionCourse OverviewMeet Your InstructorIntroduction to Machine LearningGoogle Colab NotebookSection-2: Python for Machine LearningPrint Statement and VariablesFunctionsRange, Lists, TuplesImporting ModulesSection-3: Packages and Statistics for Machine LearningNumPyPandasMatplotlibMean, Median and ModeVariance and Standard DeviationFurthermore, the curriculum is dip with practical exercises. As a result, you'll not only discover the theory, but you'll also get some practice.This course also provides the downloadable resources Python Code, executed on Google Colab Notebook, and Data Set that you can practice or use on your own projects as a bonus.
