Machine Learning Foundations: A Case Study Approach

开始时间: 07/04/2020 持续时间: Unknown

所在平台: Coursera

课程类别: 计算机科学

大学或机构: CourseraNew

   

课程主页: https://www.coursera.org/learn/ml-foundations

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课程详情

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.

机器学习基础:案例研究方法:您是否拥有数据,并想知道它能告诉您什么?您是否需要更深入地了解机器学习可以改善您的业务的核心方式?您是否想与专家就从回归和分类到深度学习和推荐系统的任何事情进行交谈? 在本课程中,您将从一系列实用的案例研究中获得有关机器学习的动手经验。在第一门课程的最后,您将学习如何根据房屋级别的功能预测房价,从用户评论中分析情绪,检索感兴趣的文档,推荐产品以及搜索图像。通过使用这些用例的动手实践,您将能够在广泛的领域中应用机器学习方法。 第一门课程将机器学习方法视为黑匣子。使用此抽象,您将专注于理解感兴趣的任务,将这些任务与机器学习工具匹配,以及评估输出的质量。在随后的课程中,您将通过检查模型和算法来深入研究黑匣子的组成部分。这些部分共同构成了机器学习管道,您将在开发智能应用程序时使用它们。 学习成果:在本课程结束时,您将能够:    -确定机器学习在实践中的潜在应用。    -描述通过回归,分类和聚类实现的分析中的核心差异。    -为潜在的应用选择适当的机器学习任务。    -应用回归,分类,聚类,检索,推荐系统和深度学习。    -将数据表示为功能,以作为机器学习模型的输入。    -根据每个任务的相关错误度量来评估模型质量。    -利用数据集以适合模型以分析新数据。    -构建一个以机器学习为核心的端到端应用程序。    -在Python中实施这些技术。

课程大纲

Machine learning is everywhere, but is often operating behind the scenes.

This introduction to the specialization provides you with insights into the power of machine learning, and the multitude of intelligent applications you personally will be able to develop and deploy upon completion.

We also discuss who we are, how we got here, and our view of the future of intelligent applications.

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课程简介

你是否好奇数据可以告诉你什么?你是否想在关于机器学习促进商业的核心方式上有深层次的理解?你是否想能同专家们讨论关于回归,分类,深度学习以及推荐系统的一切?在这门课上,你将会通过一系列实际案例学习来获取实践经历。在这门课结束的时候,

课程标签

机器学习基础 案例研究 机器学习 机器学习专项 华盛顿大学 机器学习专项课程 华盛顿大学机器学习

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