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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-in-python-extras/
课程评论：没有评论
课程名称：Python中的机器学习 - 额外内容 课程概述：机器学习应用如今无处不在，从谷歌翻译和自然语言处理API，到YouTube、Netflix、亚马逊和Udemy等推荐系统。数据科学和机器学习对任何业务和行业的成功至关重要。那么，构建有效的机器学习系统需要什么呢？在进行机器学习和数据科学项目时，通常的工作流程是：识别待解决的问题，进行数据收集、数据准备、特征工程、模型构建与评估，然后部署模型。然而，这并不是全部，整个生命周期中还有许多其他重要内容。 本课程将介绍许多大多数机器学习课程未涵盖的额外内容，例如使用管道（特别是Scikit-learn管道和Spark管道）以及处理不平衡数据集等。我们还将探索除了Scikit-learn、Tensorflow或Pytorch之外的其他机器学习框架，如TuriCreate和Creme等在线机器学习工具。课程还将介绍模型的解释与说明。某些机器学习模型在生产中使用时可能会出现偏见，因此我们将探讨如何检测模型的公平性和偏见。 到本课程结束时，您将全面了解机器学习项目生命周期的额外概念和工具，以及在进行数据科学项目时需要考虑的事项。该课程轻松有趣，同时深入探讨机器学习生命周期的一些额外方面。具体学习内容包括： - 管道及其优势 - 如何使用Scikit-Learn构建ML管道 - 如何构建Spark NLP管道 - 如何处理和修复不平衡数据集 - 模型公平性与偏见检测 - 如何使用Lime、Eli5等工具解释和说明您的黑箱模型 - 增量/在线机器学习框架 - 数据科学项目的最佳实践 - 模型部署 - 替代机器学习库，如TuriCreate - 如何跟踪您的机器学习实验等 注意：本课程不包括CI/CD机器学习管道。欢迎加入我们，一起探索Python中的机器学习世界 - 额外内容。
Machine Learning applications are everywhere nowadays from Google Translate and NLP API,to Recommendation Systems used by YouTube,Netflix and Amazon,Udemy and more. As we have come to know, data science and machine learning is quite important to the success of any business and sector- so what does it take to build machine learning systems that works?In performing machine learning and data science projects, the normal workflow is that you have a problem you want to solve, hence you perform data collection,data preparation,feature engineering,model building and evaluation and then you deploy your model. However that is not all there is, there is a lot more to this life cycle.In this course we will be introducing to you some extra things that is not covered in most machine learning courses - such as working with pipelines specifically Scikit-learn pipelines, Spark Pipelines,etc and working with imbalanced dataset,etcWe will also explore other ML frameworks beyond Scikit-learn,Tensorflow or Pytorch such as TuriCreate, Creme for online machine learning and more.We will learn about model interpretation and explanation. Certain ML models when used in production tend to be bias, hence in this course we will explore how to detect model fairness and bias.By the end of the course you will have a comprehensive overview of extra concepts and tools in the entire machine learning project life cycle and things to consider when performing a data science project.This course is unscripted,fun and exciting but at the same time we dive deep into some extra aspects of the machine learning life cycle.Specifically you will learnPipelines and their advantages.How to build ML Pipelines with Scikit-LearnHow to build Spark NLP PipelinesHow to work with and fix Imbalanced DatasetsModel Fairness and Bias DetectionHow to interpret and explain your Black Box Models using Lime,Eli5,etcIncremental/Online Machine Learning FrameworksBest practices in data science projectModel DeploymentAlternative ML Libraries eg TuriCreate,etchow to track your ML experiments and moreetcNB: This course will not cover CI/CD ML PipelinesJoin us as we explore the world of machine learning in python - the Extras
