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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/machine-learning-accounting-python
课程评论：没有评论
课程名称：使用Python进行会计机器学习 课程概述：本课程为学生介绍机器学习算法及其在会计问题中的应用。内容包括分类、回归、聚类、文本分析和时间序列分析，并讨论模型评估与优化。通过本课程，学生将能够在商业相关的数据集上应用合适的机器学习模型，以解决各种问题。完成《Python会计数据分析》课程是参与本课程的先决条件。两门课程均在Jupyter Notebook平台上进行，前者侧重于数据理解和准备，而本课程则涵盖建模和模型评估两个步骤。完成这两门课程后，学生将能够用Python完成整个数据分析过程。 课程大纲： 1. 课程介绍：了解课程内容、教师和同学，以及学习环境，掌握学习所需的技术技能。 2. 模块1：机器学习简介：介绍机器学习的基本概念及如何使用Python和scikit-learn模块进行机器学习，包括数据预处理。 3. 模块2：基本算法I：介绍线性回归、逻辑回归和决策树，探讨机器分类和回归的基本原理。 4. 模块3：基本算法II：介绍k最近邻、支持向量机和随机森林算法，适用于分类和回归任务。 5. 模块4：模型评估：介绍模型评估在数据分析项目中的重要性，讨论回归和分类模型的评估指标。 6. 模块5：模型优化：介绍特征选择、交叉验证及模型选择（超参数调优）等基本的模型优化技术。 7. 模块6：文本分析简介：讲解如何将文本数据转换为计算机可处理的数字数据，并应用分类算法进行文本分析，最后介绍情感分析。 8. 模块7：聚类简介：介绍数据聚类的基本概念和技术，如K-means聚类及其案例研究，以及密度基础的DBSCAN技术。 9. 模块8：时间序列数据简介：讨论如何处理时间和日期特征，特别是时间序列数据的应用和挑战。 通过系统学习该课程，学生将掌握使用Python进行会计领域数据分析的机器学习技能。
Name:Introduction to the Course
Description:In this module, you will become familiar with the course, your instructor and your classmates, and our learning environment. This orientation will also help you obtain the technical skills required to navigate and be successful in this 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 about the basic types of machine learning. Next, you will learn an important step before applying machine learning algorithms, data pre-processing. Finally, you will learn how to leverage different types of machine learning algorithms in a Python script.
Name:Module 2: Fundamental Algorithms I
Description:This module introduces three machine learning algorithms. First, 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. Next, you will learn Logistic Regression. Despite its name, Logistic Regression is a classification algorithm. Lastly, you will learn Decision Tree, which is a popular machine learning algorithm that can be used for both classification and regression. 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 continuous 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.
Name:Module 3: Fundamental Algorithms II
Description:This module introduces three more machine learning algorithms, k-nearest neighbors, support vector machine and random forest. All of them can be used for either classification or regression tasks.
Name:Module 4: Model Evaluation
Description:Model Evaluation is an integral component of any data analytics project. It helps to find out how well the model will work on predicting future (out-of-sample) data. This module introduces basic model evaluation metrics for machine learning algorithms. First, the evaluation metrics for regression is presented. Next the metrics and techniques to evaluate classification are introduced.
Name:Module 5: Model Optimization
Description:This module introduces the techniques of model optimization. First, the basic techniques of feature selection is presented. Next, the technique of cross-validation is introduced, which can provide a more accurate evaluation on models. Finally, model selection, or hyperparameter tuning, which uses cross-validation, is introduced.
Name:Module 6: Introduction to Text Analysis
Description:In this module, you will start applying your new machine learning skills to an exciting data analytic topic: Text Analysis. First, we will review the process by which textual data is converted into numerical data that can be processed by a computer. Along with this are a number of new concepts that focus on manipulating these data to generate improved machine learning predictions. Second, we will apply machine learning algorithms, specifically classification, to text data. Finally, we will explore the more advanced concepts in text analysis and introduce a special kind of text classification: sentiment analysis.
Name:Module 7: Introduction to Clustering
Description:This module introduces clustering, where data points are assigned to sub groups of points based on some specific properties, such as spatial distance or the local density of points. While humans often find clusters visually with ease in a given data sets, computationally the problem is more challenging. This module starts by exploring the basic ideas behind this unsupervised learning technique. One of the most popular clustering techniques, K-means, is introduced. Next, a K-means case study is provided. Finally the density-based DBSCAN technique is introduced.
Name:Module 8: Introduction to Time Series Data
Description:This module introduces time and date data, which provide unique learning opportunities and challenges. First, we will discuss how to properly handle time and date features within a Python program. Next, we will extend this discussion to handle data indexed by time and date information, which is known as time series data.
This course, Machine Learning for Accounting with Python, introduces machine learning algorithms (models) and their applications in accounting problems. It covers classification, regression, clustering, text analysis, time series analysis. It also discusses model evaluation and model optimization. This course provides an entry point for students to be able to apply proper machine learning models on business related datasets with Python to solve various problems. Accounting Data Analytics with Python is a prerequisite for this course. This course is running on the same platform (Jupyter Notebook) as that of the prerequisite course. While Accounting Data Analytics with Python covers data understanding and data preparation in the data analytics process, this course covers the next two steps in the process, modeling and model evaluation. Upon completion of the two courses, students should be able to complete an entire data analytics process with Python.
