UIUC - Machine Learning for Accounting with Python
- Offered byCoursera
Machine Learning for Accounting with Python at Coursera Overview
Duration | 63 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Machine Learning for Accounting with Python at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 4 in the Accounting Data Analytics Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 63 hours to complete
- English Subtitles: French, Portuguese (European), Russian, English, Spanish
Machine Learning for Accounting with Python at Coursera Course details
- 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.
Machine Learning for Accounting with Python at Coursera Curriculum
INTRODUCTION TO THE COURSE
Course Introduction
About Linden Lu
Syllabus
Glossary
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Module 1 Introduction
1.1 Introduction to Machine Learning
1.2 Introduction to Data Preprocessing
1.3 Introduction to Machine Learning Algorithms
Module 1 Overview and Resources
Module 1 Quiz
MODULE 2: FUNDAMENTAL ALGORITHMS I
Module 2 Introduction
2.1 Introduction to Linear Regression
2.2 Introduction to Logistic Regression
2.3 Introduction to Decision Tree
Module 2 Overview and Resources
Module 2 Quiz
MODULE 3: Fundamental Algorithms II
Module 3 Introduction
3.1 Introduction to K-nearest Neighbors
3.2 Introduction to Support Vector Machine
3.3 Introduction to Bagging and Random Forest
Module 3 Overview and Resources
Module 3 Quiz
MODULE 4: MODEL EVALUATION
Module 4 Introduction
4.1 Regressive Evaluation Metrics
4.2 Classification Evaluation Metrics I
4.3 Classification Evaluation Metrics II
Module 4 Overview and Resources
Module 4 Quiz
MODULE 5: MODEL OPTIMIZATION
Module 5 Introduction
5.1 Introduction to Feature Selection
5.2 Introduction to Cross-Validation
5.3 Introduction to Model Selection
Module 5 Overview and Resources
Module 5 Quiz
MODULE 6: INTRODUCTION TO TEXT ANALYSIS
Module 6 Introduction
6.1 Introduction to Text Analytics
6.2 Introduction to Text Classification
6.3 Introduction to Text Classification II
Module 6 Overview and Resources
Module 6 Quiz
MODULE 7: INTRODUCTOIN TO CLUSTERING
Module 7 Introduction
7.1 Introduction to K-means Clustering
7.2 K-means Case Study
7.3 Introduction to Density Based Clustering
Module 7 Overview and Resources
Module 7 Quiz
MODULE 8: INTRODUCTION TO TIME SERIES DATA
Module 8 Introduction
8.1 Working with Dates and Times
8.2 Analyzing Time Series Data
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Module 8 Overview and Resources
Congratulations!
Module 8 Quiz
Machine Learning for Accounting with Python at Coursera Admission Process
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