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所在平台: Udemy |
课程主页: https://www.udemy.com/course/jumpstart-to-data-science-machine-learning-using-python/
课程评论：没有评论
课程名称：初探机器学习、数据科学与Python 课程概述：本课程由UNP创建，聚焦于数据科学中最重要的主题。85%的数据科学问题是通过探索性数据分析（EDA）、可视化和回归（线性与逻辑回归）来解决的。因此，85%的面试问题也来自这些领域。本课程旨在为学生建立数据科学的坚实基础，使他们能够独立构建机器学习和预测分析模型，信心满满地应对探索性数据分析、基础数据科学和Python相关的面试。 课程结束时，学员将能够： - 独立构建机器学习和预测分析模型 - 自信地参加与探索性数据分析、基础数据科学和Python相关的面试 - 展示对探索性数据科学和Python的掌握 - 深入掌握线性和逻辑回归，这两者是数据科学的核心技术 课程内容设计合理，旨在帮助学生掌握数据科学的基础知识，理解行业标准，对机器学习有更广泛的理解，并能够将理论应用于实践。课程特别强调回归分析，详细讨论了关于过拟合和正则化的概念，这些基础理解对于几乎所有机器学习方法的应用至关重要。 课程从机器学习概念和环境设置的基本介绍开始，接着讨论使用Pandas进行数据整理和探索性数据分析，通过实际案例进行学习。随后将详细讲解线性和逻辑回归，并应用于解决实际行业问题。学习过程中还会涵盖行业标准的最佳实践以及模型评估，以保证可持续发展。 最后，课程将针对一些核心挑战进行探讨，并提供应对策略。本课程提供深入的内容，将理论与实践紧密结合，旨在为学生在行业中解决实际问题做好准备。
85% of data science problems are solved using exploratory data analysis (EDA), visualization, regression (linear & logistic). So naturally, 85% of the interview questions come from these topics as well.This concise course, created by UNP, focuses on what matter most. This course will help you create a solid foundation of the essential topics of data science. With this solid foundation, you will go a long way, understand any method easily, and create your own predictive analytics models.At the end of this course, you will be able to:independently build machine learning and predictive analytics modelsconfidently appear for exploratory data analysis, foundational data science, python interviews demonstrate mastery in exploratory data science and pythondemonstrate mastery in logistic and linear regression, the workhorses of data scienceThis course is designed to get students on board with data science and make them ready to solve industry problems. This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications. Special emphasis is given to regression analysis. Linear and logistic regression is still the workhorse of data science. These two topics are the most basic machine learning techniques that everyone should understand very well. In addition, concepts of overfitting, regularization etc., are discussed in detail. These fundamental understandings are crucial as these can be applied to almost every machine learning method. This course also provides an understanding of the industry standards, best practices for formulating, applying and maintaining data-driven solutions. It starts with a basic explanation of Machine Learning concepts and how to set up your environment. Next, data wrangling and EDA with Pandas are discussed with hands-on examples. Next, linear and logistic regression is discussed in detail and applied to solve real industry problems. Learning the industry standard best practices and evaluating the models for sustained development comes next.Final learnings are around some of the core challenges and how to tackle them in an industry setup. This course supplies in-depth content that put the theory into practice.
