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Machine Learning for Accounting with Python
  • Level Professional
  • المدة 64 ساعات hours
  • الطبع بواسطة University of Illinois Urbana-Champaign
  • Offered by

عن

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.

الوحدات

About the Course

    • 2 Videos
    • 3 Readings
    • 1 Discussion
    • 1 Plugin
Show info about module content

1 Discussions

  • About the Discussion Forums

2 Videos

  • Course Introduction
  • About Linden Lu

3 Readings

  • Syllabus
  • Glossary
  • Online Education at Gies College of Business

About Your Classmates

    • 1 Readings
    • 1 Discussion
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1 Discussions

  • Getting to Know Your Classmates

1 Readings

  • Updating Your Profile

Module 1 Information

    • 1 Videos
    • 1 Readings
    • 1 Discussion
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1 Discussions

  • Make Connections to Topic

1 Videos

  • Module 1 Introduction

1 Readings

  • Module 1 Overview

Lesson 1.1: Introduction to Machine Learning

    • 1 Videos
    • 1 Lab
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1 Labs

  • Introduction to Machine Learning

1 Videos

  • 1.1 Introduction to Machine Learning

Lesson 1.2: Introduction to Data Preprocessing

    • 1 Videos
    • 1 Lab
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1 Labs

  • Introduction to Data Preprocessing

1 Videos

  • 1.2 Introduction to Data Preprocessing

Lesson 1.3: Introduction to Machine Learning Algorithms

    • 1 Videos
    • 1 Lab
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1 Labs

  • Introduction to Machine Learning Algorithms

1 Videos

  • 1.3 Introduction to Machine Learning Algorithms

Module 1 Conclusion

    • 1 Quiz
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 1 Programming Assignment Score

1 Labs

  • Module 1 Programming Assignment

1 Quiz

  • Module 1 Quiz

Module 2 Information

    • 1 Videos
    • 1 Readings
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1 Videos

  • Module 2 Introduction

1 Readings

  • Module 2 Overview

Lesson 2.1: Introduction to Linear Regression

    • 1 Videos
    • 1 Lab
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1 Labs

  • Introduction to Linear Regression

1 Videos

  • 2.1 Introduction to Linear Regression

Lesson 2.2: Introduction to Logistic Regression

    • 1 Videos
    • 1 Lab
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1 Labs

  • Introduction to Logistic Regression

1 Videos

  • 2.2 Introduction to Logistic Regression

Lesson 2.3: Introduction to Decision Tree

    • 1 Videos
    • 1 Lab
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1 Labs

  • Introduction to Decision Tree

1 Videos

  • 2.3 Introduction to Decision Tree

Module 2 Conclusion

    • 1 Quiz
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 2 Programming Assignment Score

1 Labs

  • Module 2 Programming Assignment

1 Quiz

  • Module 2 Quiz

Module 3 Information

    • 1 Videos
    • 1 Readings
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1 Videos

  • Module 3 Introduction

1 Readings

  • Module 3 Overview

Lesson 3.1: Introduction to K-nearest Neighbors

    • 1 Videos
    • 1 Lab
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1 Labs

  • Introduction to K-nearest Neighbors

1 Videos

  • 3.1 Introduction to K-nearest Neighbors

Lesson 3.2: Introduction to Support Vector Machine

    • 1 Videos
    • 1 Lab
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1 Labs

  • Introduction to Support Vector Machine

1 Videos

  • 3.2 Introduction to Support Vector Machine

Lesson 3.3: Introduction to Bagging and Random Forest

    • 1 Videos
    • 1 Lab
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1 Labs

  • Introduction to Bagging and Random Forest

1 Videos

  • 3.3 Introduction to Bagging and Random Forest

Module 3 Conclusion

    • 1 Quiz
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 3 Programming Assignment Score

1 Labs

  • Module 3 Programming Assignment

1 Quiz

  • Module 3 Quiz

Module 4 Information

    • 1 Videos
    • 1 Readings
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1 Videos

  • Module 4 Introduction

1 Readings

  • Module 4 Overview

Lesson 4.1: Regressive Evaluation Metrics

    • 1 Videos
    • 1 Lab
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1 Labs

  • Regressive Evaluation Metrics

1 Videos

  • 4.1 Regressive Evaluation Metrics

Lesson 4.2: Classification Evaluation Metrics I

    • 1 Videos
    • 1 Lab
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1 Labs

  • Classification Evaluation Metrics I

1 Videos

  • 4.2 Classification Evaluation Metrics I

Lesson 4.3: Classification Evaluation Metrics II

    • 1 Videos
    • 1 Lab
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1 Labs

  • Classification Evaluation Metrics II

1 Videos

  • 4.3 Classification Evaluation Metrics II

Module 4 Conclusion

    • 1 Quiz
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 4 Programming Assignment Score

1 Labs

  • Module 4 Programming Assignment

1 Quiz

  • Module 4 Quiz

Module 5 Information

    • 1 Videos
    • 1 Readings
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1 Videos

  • Module 5 Introduction

1 Readings

  • Module 5 Overview

Lesson 5.1: Feature Selection

    • 1 Videos
    • 1 Lab
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1 Labs

  • Introduction to Feature Selection

1 Videos

  • 5.1 Introduction to Feature Selection

Lesson 5.2: Cross-Validation

    • 1 Videos
    • 1 Lab
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1 Labs

  • Introduction to Cross-Validation

1 Videos

  • 5.2 Introduction to Cross-Validation

Lesson 5.3: Model Selection

    • 1 Videos
    • 1 Lab
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1 Labs

  • Introduction to Model Selection

1 Videos

  • 5.3 Introduction to Model Selection

Module 5 Conclusion

    • 1 Quiz
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 5 Programming Assignment Score

1 Labs

  • Module 5 Programming Assignment

1 Quiz

  • Module 5 Quiz

Module 6 Information

    • 1 Videos
    • 1 Readings
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1 Videos

  • Module 6 Introduction

1 Readings

  • Module 6 Overview

Lesson 6.1: Introduction to Text Analytics

    • 1 Videos
    • 1 Lab
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1 Labs

  • Introduction to Text Analytics

1 Videos

  • 6.1 Introduction to Text Analytics

Lesson 6.2: Text Classification

    • 1 Videos
    • 1 Lab
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1 Labs

  • Introduction to Text Classification

1 Videos

  • 6.2 Introduction to Text Classification

Lesson 6.3: Advanced Topics and Sentiment Analysis

    • 1 Videos
    • 1 Lab
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1 Labs

  • Introduction to Text Classification II

1 Videos

  • 6.3 Introduction to Text Classification II

Module 6 Conclusion

    • 1 Quiz
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 6 Programming Assignment Score

1 Labs

  • Module 6 Programming Assignment

1 Quiz

  • Module 6 Quiz

Module 7 Information

    • 1 Videos
    • 1 Readings
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1 Videos

  • Module 7 Introduction

1 Readings

  • Module 7 Overview

Lesson 7.1: K-means

    • 1 Videos
    • 1 Lab
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1 Labs

  • Introduction to K-means Clustering

1 Videos

  • 7.1 Introduction to K-means Clustering

Lesson 7.2: Case Study—Credit Card Data

    • 1 Videos
    • 1 Lab
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1 Labs

  • K-means Case Study

1 Videos

  • 7.2 K-means Case Study

Lesson 7.3: DBSCAN

    • 1 Videos
    • 1 Lab
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1 Labs

  • Introduction to Density Based Clustering

1 Videos

  • 7.3 Introduction to Density Based Clustering

Module 7 Conclusion

    • 1 Quiz
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 7 Programming Assignment Score

1 Labs

  • Module 7 Programming Assignment

1 Quiz

  • Module 7 Quiz

Module 8 Information

    • 1 Videos
    • 1 Readings
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1 Videos

  • Module 8 Introduction

1 Readings

  • Module 8 Overview

Lesson 8.1: Working With Dates and Times

    • 1 Videos
    • 1 Lab
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1 Labs

  • Working With Dates and Times

1 Videos

  • 8.1 Working With Dates and Times

Lesson 8.2: Analyzing Time Series Data

    • 1 Videos
    • 1 Lab
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1 Labs

  • Analyzing Time Series Data

1 Videos

  • 8.2 Analyzing Time Series Data

Module 8 Conlusion

    • 1 Videos
    • 2 Readings
    • 1 Quiz
    • 1 Plugin
    • 1 Lab
    • 1 Programming
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1 Programming

  • Module 8 Programming Assignment Score

1 Labs

  • Module 8 Programming Assignment

1 Videos

  • Learn on Your Terms

2 Readings

  • Congratulations on completing the course!
  • Get Your Course Certificate

1 Quiz

  • Module 8 Quiz

Auto Summary

Unlock the power of machine learning in accounting with the "Machine Learning for Accounting with Python" course by Coursera. Explore classification, regression, clustering, and more, while mastering model evaluation and optimization. Ideal for professionals with a background in Accounting Data Analytics with Python, this 3840-minute course uses Jupyter Notebook to enhance your data analytics skills. Subscribe to the Starter plan and elevate your accounting expertise through practical Python applications.

Linden Lu
Instructor

Linden Lu