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.
Machine Learning for Accounting with Python
Machine Learning for Accounting with Python
伊利诺伊大学香槟分校
课程信息
6,991 次近期查看
您将学到的内容有
The concept of various machine learning algorithms.
How to apply machine learning models on datasets with Python in Jupyter Notebook.
How to evaluate machine learning models.
How to optimize machine learning models.
您将获得的技能
Text AnalysisBasic Time Series AnalysisMachine Learning Model Evaluation and OptimizationPython ProgrammingMachine Learning Modeling
100% 在线
立即开始，按照自己的计划学习。
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中级
完成时间大约为30 小时
建议：Eight weeks of study, eight hours per week.
英语（English）
字幕：英语（English）
教学大纲 - 您将从这门课程中学到什么
完成时间为 1 小时
INTRODUCTION TO THE COURSE
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.
完成时间为 1 小时
2 个视频 （总计 9 分钟）, 3 个阅读材料
3 个阅读材料
Syllabus10分钟
Glossary10分钟
Update Your Profile10分钟
完成时间为 8 小时
MODULE 1: INTRODUCTION TO MACHINE LEARNING
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.
完成时间为 8 小时
4 个视频 （总计 24 分钟）, 1 个阅读材料, 2 个测验
4 个视频
1.1 Introduction to Machine Learning6分钟
1.2 Introduction to Data Preprocessing10分钟
1.3 Introduction to Machine Learning Algorithms3分钟
1 个阅读材料
Module 1 Overview and Resources10分钟
1 个练习
Module 1 Quiz20分钟
完成时间为 8 小时
MODULE 2: FUNDAMENTAL ALGORITHMS I
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.
完成时间为 8 小时
4 个视频 （总计 31 分钟）, 1 个阅读材料, 2 个测验
4 个视频
2.1 Introduction to Linear Regression12分钟
2.2 Introduction to Logistic Regression8分钟
2.3 Introduction to Decision Tree6分钟
1 个阅读材料
Module 2 Overview and Resources10分钟
1 个练习
Module 2 Quiz20分钟
完成时间为 8 小时
MODULE 3: Fundamental Algorithms II
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.
完成时间为 8 小时
4 个视频 （总计 15 分钟）, 1 个阅读材料, 2 个测验
4 个视频
3.1 Introduction to K-nearest Neighbors5分钟
3.2 Introduction to Support Vector Machine4分钟
3.3 Introduction to Bagging and Random Forest3分钟
1 个阅读材料
Module 3 Overview and Resources10分钟
1 个练习
Module 3 Quiz20分钟
完成时间为 8 小时
MODULE 4: MODEL EVALUATION
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 technics to evaluate classification are introduced.
完成时间为 8 小时
4 个视频 （总计 31 分钟）, 1 个阅读材料, 2 个测验
4 个视频
4.1 Regressive Evaluation Metrics8分钟
4.2 Classification Evaluation Metrics I13分钟
4.3 Classification Evaluation Metrics II7分钟
1 个阅读材料
Module 4 Overview and Resources10分钟
1 个练习
Module 4 Quiz20分钟
立即开始攻读硕士学位
此 课程 隶属于 伊利诺伊大学香槟分校 提供的 100% 在线 Master of Science in Accountancy (iMSA)。如果您被录取参加全部课程，您的课程将计入您的学位学习进程。
关于 伊利诺伊大学香槟分校
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
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