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所在平台: Udemy |
课程主页: https://www.udemy.com/course/datascience-e2e-beginnerscourse-machinelearning-dataanalytics/
课程评论：没有评论
课程名称：数据科学 - 初学者全流程课程第一部分 课程概述： 本课程是为初学者设计的，涵盖基本的机器学习和数据分析概念。课程的目标是教授学生如何完成一个全流程的数据科学项目，从问题定义、数据获取、数据整理和建模，到分析、可视化以及模型的部署和维护。课程将介绍数据科学所需的主要原则和工具，适合对数据科学感兴趣的任何人群，包括分析师、程序员、非技术专业人士、学生等。 我们注意到当前的数据科学课程和书籍中，缺乏全流程的教学方法。许多课程虽然讲解了不同的算法，但往往未能提供一个整体的视角，特别是在流程和部署方面。此外，对于不同算法的数学细节，有时内容过多或过少。本课程将为学生提供构建机器学习模型所需的编程、数学、统计和概率基础知识。 在整个课程中，学生将接收到详细的讲解，涵盖算法的数学原理和逻辑，并提供Python代码示例和在线资源，以支持学习过程。学生将学习如何使用Anaconda、Spyder、Python、Pandas、Numpy、Scikit-learn、XGBoost、Matplotlib、Seaborn、Joblib、Flask以及AWS Cloud S3、Elastic Beanstalk和Sagemaker等工具和库来构建和部署机器学习模型。 更多详情请访问我们的网站：datawisdomx。此外，课程材料（包括Python代码和数据）可在GitHub库中找到，链接为：datawisdomx/DataScienceCourse。
This is a Beginner's course that covers basic Machine Learning and Data Analytics conceptsThe Objective of this course is to teach students how to do an End-2-End data science projectFrom Problem definition, data sourcing, wrangling and modellingTo analyzing, visualizing and deploying & maintaining the modelsIt will cover the main principles/tools that are required for data scienceThis course is for anyone interested in learning data science - analyst, programmer, non-technical professional, student, etcHaving seen available data science courses and books, we feel there is a lack of an End 2 End approachQuite often you learn the different algorithms but don't get a holistic view, especially around the process and deploymentAlso, either too much or limited mathematical details are provided for different algorithmsThe course will cover all the basics in programming, maths, statistics and probability required for building machine learning modelsThroughout the course detailed lectures covering the maths and logic of the algorithms, python code examples and online resources are provided to support the learning processStudents will learn how to build and deploy machine learning models using tools and libraries like anaconda, spyder, python, pandas, numpy, scikit-learn, xgboost, matplotlib, seaborn, joblib, flask, AWS Cloud S3, Elastic Beanstalk and SagemakerMore details are available on our website - datawisdomxCourse material including python code and data is available in github repository - datawisdomx, DataScienceCourse
