|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ml-classification
课程评论：没有评论
课程总结：机器学习：分类 本课程主要围绕分类问题展开，包含两个实际案例：情感分析和贷款违约预测。在情感分析的案例中，学员将创建模型来预测评论的情感类别（正面/负面），而在贷款违约预测中，则利用金融数据预测贷款对银行的风险。分类是机器学习中广泛应用的一种技术，具有众多应用场景，例如广告定向、垃圾邮件检测、医学诊断及图像分类等。 在课程中，学员将学习如何创建具备先进性能的分类器，掌握多种实用的分类技术，包括逻辑回归、决策树和提升方法。同时，学员还将能够设计与实施能够大规模学习这些模型的算法，使用随机梯度上升法进行训练。此外，课程重点探讨真实世界应用中常见的问题，如缺失数据处理和通过精确度与召回率评估分类器。课程结合了大量的可视化资料，以帮助理解各种技术在实际数据中的应用，并针对希望深入学习的学员提供可选扩展内容。 学习目标： - 描述分类模型的输入和输出。 - 处理二分类和多分类问题。 - 实现用于大规模分类的逻辑回归模型。 - 运用决策树创建非线性模型。 - 使用提升方法改善模型性能。 - 使用随机梯度上升法扩展方法的规模。 - 描述决策边界的基本原理。 - 构建分类模型以预测产品评论中的情感。 - 分析金融数据以预测贷款违约。 - 使用处理缺失数据的技术。 - 使用精确度与召回率评估模型效果。 - 在Python或其他语言中实施上述技术（推荐使用Python）。 课程内容： 1. 线性分类器与逻辑回归 2. 逻辑回归中的过拟合与正则化 3. 决策树 4. 处理缺失数据 5. 提升方法 6. 精确度与召回率 7. 扩展至大规模数据集与在线学习 通过本课程的学习，学员将具备处理分类任务的能力，并能将机器学习技术应用于实际问题的解决中。
Part: 1
Title:Linear Classifiers & Logistic Regression
Description: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 in this type of representation. You will focus on a particularly useful type of linear classifier called logistic regression, which, in addition to allowing you to predict a class, provides a probability associated with the prediction. These probabilities are extremely useful, since they provide a degree of confidence in the predictions. In this module, you will also be able to construct features from categorical inputs, and to tackle classification problems with more than two class (multiclass problems). You will examine the results of these techniques on a real-world product sentiment analysis task.
Part: 2
Title:Overfitting & Regularization in Logistic Regression
Description: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 module, since we not only risk getting an overly complex decision boundary, but your classifier can also become overly confident about the probabilities it predicts. In this module, you will investigate overfitting in classification in significant detail, and obtain broad practical insights from some interesting visualizations of the classifiers' outputs. You will then add a regularization term to your optimization to mitigate overfitting. You will investigate both L2 regularization to penalize large coefficient values, and L1 regularization to obtain additional sparsity in the coefficients. Finally, you will modify your gradient ascent algorithm to learn regularized logistic regression classifiers. You will implement your own regularized logistic regression classifier from scratch, and investigate the impact of the L2 penalty on real-world sentiment analysis data.
Part: 3
Title:Decision Trees
Description: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 with the core decision trees representation. You will then design a simple, recursive greedy algorithm to learn decision trees from data. Finally, you will extend this approach to deal with continuous inputs, a fundamental requirement for practical problems. In this module, you will investigate a brand new case-study in the financial sector: predicting the risk associated with a bank loan. You will implement your own decision tree learning algorithm on real loan data.
Part: 4
Title:Handling Missing Data
Description: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 performance. And, this issue is rarely discussed in machine learning courses. In this module, you will tackle the missing data challenge head on. You will start with the two most basic techniques to convert a dataset with missing data into a clean dataset, namely skipping missing values and inputing missing values. In an advanced section, you will also design a modification of the decision tree learning algorithm that builds decisions about missing data right into the model. You will also explore these techniques in your real-data implementation.
Part: 5
Title:Boosting
Description: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 techniques in machine learning today. This simple approach can boost the accuracy of any classifier, and is widely used in practice, e.g., it's used by more than half of the teams who win the Kaggle machine learning competitions. In this module, you will first define the ensemble classifier, where multiple models vote on the best prediction. You will then explore a boosting algorithm called AdaBoost, which provides a great approach for boosting classifiers. Through visualizations, you will become familiar with many of the practical aspects of this techniques. You will create your very own implementation of AdaBoost, from scratch, and use it to boost the performance of your loan risk predictor on real data.
Part: 6
Title:Precision-Recall
Description: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 accuracy, you will define two metrics: precision and recall, which are widely used in real-world applications to measure the quality of classifiers. You will explore how the probabilities output by your classifier can be used to trade-off precision with recall, and dive into this spectrum, using precision-recall curves. In your hands-on implementation, you will compute these metrics with your learned classifier on real-world sentiment analysis data.
Part: 7
Title:Scaling to Huge Datasets & Online Learning
Description: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 Data". Thus, our learning algorithms must scale to bigger and bigger datasets. In this module, you will develop a small modification of gradient ascent called stochastic gradient, which provides significant speedups in the running time of our algorithms. This simple change can drastically improve scaling, but makes the algorithm less stable and harder to use in practice. In this module, you will investigate the practical techniques needed to make stochastic gradient viable, and to thus to obtain learning algorithms that scale to huge datasets. You will also address a new kind of machine learning problem, online learning, where the data streams in over time, and we must learn the coefficients as the data arrives. This task can also be solved with stochastic gradient. You will implement your very own stochastic gradient ascent algorithm for logistic regression from scratch, and evaluate it on sentiment analysis data.
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).
