Data Analytics Foundations for Accountancy II

所在平台: Coursera

课程主页: https://www.coursera.org/learn/data-analytics-accountancy-2

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课程简介

课程名称：会计数据分析基础 II 概述：欢迎参加《会计数据分析基础 II》课程！我们期待您的积极参与和贡献。建议您花几分钟浏览课程网站，查阅每周的学习内容，并预览需要完成的作业。您可以通过“讨论”区域与同学交流课程内容，如有问题，请在论坛发帖以获取帮助。 课程大纲： 1. 课程导览：熟悉课程、同学及学习环境，同时掌握课程所需的技术技能。 2. 模块一：机器学习简介 - 介绍机器学习的基本概念及其在商业中的变革作用，学习如何使用Python和scikit-learn进行机器学习。 3. 模块二：基础算法 - 深入讲解逻辑回归、决策树及支持向量机等重要机器学习算法，并探讨其在分类和回归中的应用。 4. 模块三：机器学习的实用概念 - 探讨在真实数据集上应用数据分析的挑战，介绍集成学习及机器学习管道的概念。 5. 模块四：过拟合与正则化 - 讨论过拟合的概念及其识别方法，介绍交叉验证及正则化技术。 6. 模块五：基础概率算法 - 讨论实际机器学习工作流程及乱码贝叶斯和高斯过程等基本算法。 7. 模块六：特征工程 - 讲解特征选择的重要性，探讨如何通过数据清洗和特征构建提高模型性能。 8. 模块七：聚类简介 - 介绍数据聚类的基本概念及K-means、DB-SCAN等聚类算法的应用。 9. 模块八：异常检测简介 - 讨论异常值的定义及检测技术，包括统计方法和机器学习技术。 祝您学习顺利，期待您在课程中的收获！

课程大纲

Name:Course Orientation

Description:You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.

Name:Module 1: Introduction to Machine Learning

Description:This module provides the basis for the rest of the course by introducing the basic concepts behind machine learning, and, specifically, how to perform machine learning by using Python and the scikit learn machine learning module. First, you will learn how machine learning and artificial intelligence are disrupting businesses. Next, you will learn about the basic types of machine learning and how to leverage these algorithms in a Python script. Third, you will learn how linear regression can be considered a machine learning problem with parameters that must be determined computationally by minimizing a cost function. Finally, you will learn about neighbor-based algorithms, including the k-nearest neighbor algorithm, which can be used for both classification and regression tasks.

Name:Module 2: Fundamental Algorithms

Description:This module introduces several of the most important machine learning algorithms: logistic regression, decision trees, and support vector machine. Of these three algorithms, the first, logistic regression, is a classification algorithm (despite its name). The other two, however, can be used for either classification or regression tasks. Thus, this module will dive deeper into the concept of machine classification, where algorithms learn from existing, labeled data to classify new, unseen data into specific categories; and, the concept of machine regression, where algorithms learn a model from data to make predictions for new, unseen data. While these algorithms all differ in their mathematical underpinnings, they are often used for classifying numerical, text, and image data or performing regression in a variety of domains. This module will also review different techniques for quantifying the performance of a classification and regression algorithms and how to deal with imbalanced training data.

Name:Module 3: Practical Concepts in Machine Learning

Description:This module introduces several important and practical concepts in machine learning. First, you will learn about the challenges inherent in applying data analytics (and machine learning in particular) to real world data sets. This also introduces several methodologies that you may encounter in the future that dictate how to approach, tackle, and deploy data analytic solutions. Next, you will learn about a powerful technique to combine the predictions from many weak learners to make a better prediction via a process known as ensemble learning. Specifically, this module will introduce two of the most popular ensemble learning techniques: bagging and boosting and demonstrate how to employ them in a Python data analytics script. Finally, the concept of a machine learning pipeline is introduced, which encapsulates the process of creating, deploying, and reusing machine learning models.

Name:Module 4: Overfitting & Regularization

Description:This module introduces the concept of regularization, problems it can cause in machine learning analyses, and techniques to overcome it. First, the basic concept of overfitting is presented along with ways to identify its occurrence. Next, the technique of cross-validation is introduced, which can mitigate the likelihood that overfitting can occur. Next, the use of cross-validation to identify the optimal parameters for a machine learning algorithm trained on a given data set is presented. Finally, the concept of regularization, where an additional penalty term is applied when determining the best machine learning model parameters, is introduced and demonstrated for different regression and classification algorithms.

Name:Module 5: Fundamental Probabilistic Algorithms

Description:This module starts by discussing practical machine learning workflows that are deployed in production environments, which emphasizes the big picture view of machine learning. Next this module introduces two additional fundamental algorithms: naive Bayes and Gaussian Processes. These algorithms both have foundations in probability theory but operate under very different assumptions. Naive Bayes is generally used for classification tasks, while Gaussian Processes are generally used for regression tasks. This module also discusses practical issues in constructing machine learning workflows.

Name:Module 6: Feature Engineering

Description:This module introduces an important concept in machine learning, the selection of the actual features that will be used by a machine learning algorithm. Along with data cleaning, this step in the data analytics process is extremely important, yet it is often overlooked as a method for improving the overall performance of an analysis. This module beings with a discussion of ethics in machine learning, in large part because the selection of features can have (sometimes) non-obvious impacts on the final performance of an algorithm. This can be important when machine learning is applied to data in a regulated industry or when the improper application of an algorithm might lead to discrimination. The rest of this module introduces different techniques for either selecting the best features in a data set, or the construction of new features from the existing set of features.

Name:Module 7: Introduction to Clustering

Description:This module introduces clustering, where data points are assigned to larger groups of points based on some specific property, such as spatial distance or the local density of points. While humans often find clusters visually with ease in given data sets, computationally the problem is more challenging. This module starts by exploring the basic ideas behind this unsupervised learning technique, as well as different areas in which clustering can be used by businesses. Next, one of the most popular clustering techniques, K-means, is introduced. Next the density-based DB-SCAN technique is introduced. This module concludes by introducing the mixture models technique for probabilistically assigning points to clusters.

Name:Module 8: Introduction to Anomaly Detection

Description:This module introduces the concept of an anomaly, or outlier, and different techniques for identifying these unusual data points. First, the general concept of an anomaly is discussed and demonstrated in the business community via the detection of fraud, which in general should be an anomaly when compared to normal customers or operations. Next, statistical techniques for identifying outliers are introduced, which often involve simple descriptive statistics that can highlight data that are sufficiently far from the norm for a given data set. Finally, machine learning techniques are reviewed that can either classify outliers or identify points in low density (or outside normal clusters) areas as potential outliers.

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