Machine Learning: Regression

开始时间: 08/01/2020 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-regression

Explore 1600+ online courses from top universities. Join Coursera today to learn data science, programming, business strategy, and more.

课程评论:没有评论

第一个写评论        关注课程

课程详情

Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Analyze the performance of the model. -Describe the notion of sparsity and how LASSO leads to sparse solutions. -Deploy methods to select between models. -Exploit the model to form predictions. -Build a regression model to predict prices using a housing dataset. -Implement these techniques in Python.

机器学习:回归:案例研究-预测房价 在我们的第一个案例中,预测房价,您将创建模型,这些模型根据输入要素(平方英尺,卧室和浴室的数量等)预测连续值(价格)。这只是可以应用回归的众多场所之一。其他应用范围包括预测医学健康结果,财务中的股票价格以及高性能计算中的功耗,以及分析哪些调节剂对基因表达很重要。 在本课程中,您将探索用于预测和特征选择任务的正则化线性回归模型。您将能够处理非常多的功能,并可以在各种复杂程度的模型之间进行选择。您还将分析数据方面(例如异常值)对所选模型和预测的影响。为了适合这些模型,您将实现可扩展到大型数据集的优化算法。 学习成果:在本课程结束时,您将能够:    -描述回归模型的输入和输出。    -在对数据建模时比较并对比偏差和方差。    -使用优化算法估算模型参数。    -带有交叉验证的参数。    -分析模型的性能。    -描述稀疏性的概念以及LASSO如何导致稀疏解决方案。    -部署方法以在模型之间进行选择。    -利用模型来形成预测。    -使用住房数据集建立回归模型以预测价格。    -在Python中实施这些技术。

课程大纲

Regression is one of the most important and broadly used machine learning and statistics tools out there. It allows you to make predictions from data by learning the relationship between features of your data and some observed, continuous-valued response. Regression is used in a massive number of applications ranging from predicting stock prices to understanding gene regulatory networks.

This introduction to the course provides you with an overview of the topics we will cover and the background knowledge and resources we assume you have.

课程评论(0条)

欢迎关注我们的公众号

NLPJob

课程简介

这门课程关注机器学习里面的一个基本问题: 回归(Regression), 也通过案例研究(预测房价)的方式进行回归问题的学习,最终通过Python实现相关的机器学习算法。 Case Study - Predicting Housing Prices

课程标签

机器学习 华盛顿大学 机器学习专项 华盛顿大学机器学习 回归

4人关注该课程

主题相关的课程