Machine Learning: Classification

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



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Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. We've also included optional content in every module, covering advanced topics for those who want to go even deeper! Learning Objectives: By the end of this course, you will be able to: -Describe the input and output of a classification model. -Tackle both binary and multiclass classification problems. -Implement a logistic regression model for large-scale classification. -Create a non-linear model using decision trees. -Improve the performance of any model using boosting. -Scale your methods with stochastic gradient ascent. -Describe the underlying decision boundaries. -Build a classification model to predict sentiment in a product review dataset. -Analyze financial data to predict loan defaults. -Use techniques for handling missing data. -Evaluate your models using precision-recall metrics. -Implement these techniques in Python (or in the language of your choice, though Python is highly recommended).

机器学习:分类:案例研究:情绪分析贷款违约预测 在我们的情绪分析案例研究中,您将创建模型,这些模型根据输入特征(评论文本,用户个人资料等)来预测类别(正/负情绪)。在本课程的第二个案例研究(贷款违约预测)中,您将处理财务数据,并预测何时贷款可能对银行有风险或安全。这些任务是分类的示例,分类是机器学习最广泛使用的领域之一,具有广泛的应用程序,包括广告定位,垃圾邮件检测,医学诊断和图像分类。 在本课程中,您将创建分类器,这些分类器可在各种任务上提供最新的性能。您将熟悉最成功的技术,这些技术在实践中使用最广泛,包括逻辑回归,决策树和提升。此外,您将能够设计和实现可使用随机梯度上升大规模学习这些模型的基础算法。您将在现实世界中的大型机器学习任务中实施这些技术。您还将解决在ML的实际应用中将要面对的重要任务,包括处理丢失的数据,测量精度和调用以评估分类器。本课程是动手操作,充满动感的课程,并提供有关这些技术在实际数据上的行为的可视化和插图说明。我们还在每个模块中都包含了可选内容,为那些想深入了解的人涵盖了高级主题! 学习目标:在本课程结束时,您将能够:    -描述分类模型的输入和输出。    -解决二进制和多类分类问题。    -实施用于大型分类的逻辑回归模型。    -使用决策树创建非线性模型。    -使用Boosting改善任何模型的性能。    -使用随机梯度上升来扩展您的方法。    -描述基本的决策边界。    -建立分类模型以预测产品评论数据集中的情绪。    -分析财务数据以预测贷款违约。    -使用技术来处理丢失的数据。    -使用精确调用指标评估模型。    -以Python(或您选择的语言,尽管强烈建议使用Python)实施这些技术。


Classification is one of the most widely used techniques in machine learning, with a broad array of applications, including sentiment analysis, ad targeting, spam detection, risk assessment, medical diagnosis and image classification. The core goal of classification is to predict a category or class y from some inputs x. Through this course, you will become familiar with the fundamental models and algorithms used in classification, as well as a number of core machine learning concepts. Rather than covering all aspects of classification, you will focus on a few core techniques, which are widely used in the real-world to get state-of-the-art performance. By following our hands-on approach, you will implement your own algorithms on multiple real-world tasks, and deeply grasp the core techniques needed to be successful with these approaches in practice. 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.





这门课程关注机器学习里面的另一个基本问题: 分类(Classification), 通过两个案例研究进行学习:情感分析和贷款违约预测,最终通过Python实现相关的算法(也可以选择其他语言,但是强烈推荐Python)。 Case Studies: Analyzing Sentiment & Loan Default Prediction


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