Machine Learning for Accounting with Python
- 0.0
-
![]()
![]()
![]()
![]()
![]()
Course Summary
This course teaches machine learning techniques in accounting using Python programming language. Students will learn how to apply machine learning to financial analysis and decision making.Key Learning Points
- Learn how to use Python for financial analysis and machine learning
- Apply machine learning techniques to accounting and finance
- Gain practical skills for data-driven decision making in financial analysis
Job Positions & Salaries of people who have taken this course might have
- USA: $65,000 - $110,000
- India: ₹300,000 - ₹1,000,000
- Spain: €30,000 - €60,000
- USA: $65,000 - $110,000
- India: ₹300,000 - ₹1,000,000
- Spain: €30,000 - €60,000
- USA: $60,000 - $100,000
- India: ₹300,000 - ₹1,000,000
- Spain: €25,000 - €45,000
- USA: $65,000 - $110,000
- India: ₹300,000 - ₹1,000,000
- Spain: €30,000 - €60,000
- USA: $60,000 - $100,000
- India: ₹300,000 - ₹1,000,000
- Spain: €25,000 - €45,000
- USA: $80,000 - $150,000
- India: ₹500,000 - ₹2,500,000
- Spain: €40,000 - €80,000
Related Topics for further study
Learning Outcomes
- Ability to use Python for financial analysis and machine learning
- Application of machine learning techniques to accounting and finance
- Practical skills for data-driven decision making in financial analysis
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Understanding of accounting and finance
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
- Hands-on exercises
Similar Courses
- Data Analysis with Python
- Financial Markets
Related Education Paths
Related Books
Description
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.
Knowledge
- 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.
Outline
- INTRODUCTION TO THE COURSE
- Course Introduction
- About Linden Lu
- Syllabus
- Glossary
- Update Your Profile
- MODULE 1: INTRODUCTION TO MACHINE LEARNING
- 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
- Gies Online Programs
- Module 8 Overview and Resources
- Congratulations!
- Module 8 Quiz
Summary of User Reviews
Discover the power of machine learning in accounting with this comprehensive course on Coursera. Students rave about the practical nature of the course, which provides hands-on experience with real-world scenarios. Many students appreciate the in-depth explanations and step-by-step tutorials provided throughout the lessons.Key Aspect Users Liked About This Course
Practical nature of the coursePros from User Reviews
- Hands-on experience with real-world scenarios
- In-depth explanations and step-by-step tutorials
- Great for beginners
- Excellent instructor
- Good value for the price
Cons from User Reviews
- Some topics may be too basic for advanced users
- Lack of interaction with other students
- No certificate offered for free version
- Requires basic programming knowledge
- Some technical issues reported
Python for Absolute Beginners | Python Beginner to Pro 2021
- 4.7
-
![]()
![]()
![]()
![]()
![]()
Beginner friendly Python! Become a fluent Python programmer Learn the Python basics then build inspiring Python projects Python for Absolute Beginners 2021...
Introduction to Data Science
- 4.3
-
![]()
![]()
![]()
![]()
![]()
An overview of modern data science: the practice of obtaining, exploring, modeling, and interpreting data. Welcome to this course on Data Science....
Using Machine Learning in Trading and Finance
- 4
-
![]()
![]()
![]()
![]()
![]()
This course provides the foundation for developing advanced trading strategies using machine learning techniques. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading....
IBM Machine Learning Professional Certificate
- 0.0
-
![]()
![]()
![]()
![]()
![]()
Machine Learning, Time Series & Survival Analysis. Develop working skills in the main areas of Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning....
