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Machine Learning: Classification
课程
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35 时
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课程详情
教学大纲
- Week 1 - Welcome!
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 classifi... - Week 1 - Linear Classifiers & Logistic Regression
Linear classifiers are amongst the most practical classification methods. For example, in our sentiment analysis case-study, a linear classifier associates a coefficient with the counts of each word in the sentence. In this module, you will become proficient i... - Week 2 - Learning Linear Classifiers
Once familiar with linear classifiers and logistic regression, you can now dive in and write your first learning algorithm for classification. In particular, you will use gradient ascent to learn the coefficients of your classifier from data. You first will ne... - Week 2 - Overfitting & Regularization in Logistic Regression
As we saw in the regression course, overfitting is perhaps the most significant challenge you will face as you apply machine learning approaches in practice. This challenge can be particularly significant for logistic regression, as you will discover in this m... - Week 3 - Decision Trees
Along with linear classifiers, decision trees are amongst the most widely used classification techniques in the real world. This method is extremely intuitive, simple to implement and provides interpretable predictions. In this module, you will become familiar... - Week 4 - Preventing Overfitting in Decision Trees
Out of all machine learning techniques, decision trees are amongst the most prone to overfitting. No practical implementation is possible without including approaches that mitigate this challenge. In this module, through various visualizations and investigatio... - Week 4 - Handling Missing Data
Real-world machine learning problems are fraught with missing data. That is, very often, some of the inputs are not observed for all data points. This challenge is very significant, happens in most cases, and needs to be addressed carefully to obtain great per... - Week 5 - Boosting
One of the most exciting theoretical questions that have been asked about machine learning is whether simple classifiers can be combined into a highly accurate ensemble. This question lead to the developing of boosting, one of the most important and practical ... - Week 6 - Precision-Recall
In many real-world settings, accuracy or error are not the best quality metrics for classification. You will explore a case-study that significantly highlights this issue: using sentiment analysis to display positive reviews on a restaurant website. Instead of... - Week 7 - Scaling to Huge Datasets & Online Learning
With the advent of the internet, the growth of social media, and the embedding of sensors in the world, the magnitudes of data that our machine learning algorithms must handle have grown tremendously over the last decade. This effect is sometimes called "Big D...
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讲师
Carlos Guestrin
Amazon Professor of Machine Learning
Computer Science and Engineering
Emily Fox
Amazon Professor of Machine Learning
Statistics
编辑
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平台
Coursera是一家数字公司，提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。
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