Machine Learning with Apache Spark

所在平台: Coursera

课程主页: https://www.coursera.org/learn/machine-learning-with-apache-spark

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课程简介

课程总结：机器学习与Apache Spark 本课程“机器学习与Apache Spark”由IBM提供，旨在带领学员深入探索机器学习的世界。课程首先介绍机器学习的基本概念，接着通过Apache Spark的强大功能，教授如何为数据工程应用构建和部署机器学习模型。学员将学习有监督和无监督学习技术，并通过教学阅读和视频了解生成式AI的革命性潜力。 课程大纲包括以下模块： 1. **机器学习入门**：本模块介绍使计算机能够在没有明确编程的情况下执行任务的机器学习技术。学员将了解机器学习模型的生命周期，以及数据工程在机器学习项目中的重要性。内容涵盖分类、回归和聚类等有监督和无监督学习技术，并探索生成式AI对多个行业的潜在影响。 2. **使用Apache Spark的机器学习**：此模块介绍Spark的关键特性及其在数据工程领域的应用。学员将学习如何通过SN labs连接至Spark集群，深入各种主题，如回归分析、里程预测、分类、糖尿病分类及使用SparkML的聚类。此外，学员还将了解如何使用Spark ML构建这些模型，并通过实践实验室掌握GraphFrames的应用。 3. **利用Apache Spark进行机器学习的数据工程**：此模块从Apache Spark的结构化流处理开始，重点介绍如何使用Spark SQL处理流数据。学员将学习与结构化流相关的关键术语，以及提取-转换-加载（ETL）流程，获得从一个源到另一个目的地传输不同格式或结构数据的实践经验。课程还将探讨使用Spark进行特征提取和转换，以及机器学习管道的构建和模型持久化的重要性。 4. **最终项目**：在本模块中，学员将应用在课程中获得的数据工程技能和技术。课程以一个最终项目结束，学员需要展示在ETL任务和机器学习管道建立方面的熟练程度，模拟数据工程师在知名航空咨询公司中的角色，为数据科学家提供必要的支持，以确保其任务的顺利执行。 通过本课程，学员将获得深入的理论知识和实践技能，为将来在机器学习和数据工程领域的职业生涯做好准备。

课程大纲

Name:Get Started with Machine Learning

Description:In this module, you will gain knowledge of machine learning techniques that enable computers to perform tasks without explicit programming. You will explore the lifecycle of machine learning models and understand the crucial role of data engineering in machine learning projects. The module covers supervised and unsupervised learning techniques, including classification, regression, and clustering. Furthermore, you will acquire valuable insights into Generative AI and its potential to revolutionize multiple industries, enhance people's lives, and generate newer and previously unimaginable data and experiences.

Name:Machine Learning with Apache Spark

Description:This module will introduce you to Spark and provide an overview of its key features and applications in the field of data engineering. You will discover the process of connecting to a Spark cluster using SN labs and delve into various topics such as regression, mileage prediction, classification, diabetic classification, clustering, and clustering load data using SparkML. Additionally, you will gain insights into how to construct these models using Spark ML. Moreover, this module will cover GraphFrames on Apache Spark and guide you in hands-on labs.

Name:Data Engineering for Machine Learning using Apache Spark

Description:This module begins with Apache Spark Structured Streaming and its role in processing streaming data with Spark SQL. You will acquire knowledge about key terms associated with Structured Streaming. The module then covers the Extract-Transform-Load process and provides hands-on experience in transferring data from one source to another destination with varying data formats or structures. Additionally, you will gain a practical understanding of feature extraction and transformation using Spark extract and transform features. The module also delves into machine learning pipelines using Spark, demonstrating the process and benefits involved. Lastly, you will grasp the concept of model persistence and its significant role in Machine Learning.

Name:Final Project

Description:In this module, you will apply the data engineering skills and techniques you have acquired throughout the course. The course concludes with a final project and assignments that allow you to demonstrate your proficiency in these areas. You will step into the role of a data engineer working at a renowned aeronautics consulting company recognized for its adeptness in handling large datasets. Your role as a data engineer is crucial as the data scientists rely on your expertise to carry out ETL (Extract, Transform, Load) tasks and establish machine learning pipelines. While data scientists possess expertise in machine learning, they depend on your specialized knowledge to handle various algorithms and data formats. Your contribution plays a vital role in ensuring the smooth execution of their tasks.

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