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Linden Lu
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/lnusql/machine
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What's inside
Syllabus
Introduction to the Course
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.
Read more
Syllabus
Introduction to the Course
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.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Builds a strong foundation in machine learning algorithms and their applications in accounting problems
Introduces the fundamental concepts behind machine learning, specifically how to perform machine learning by using Python and the scikit-learn machine learning module
Provides hands-on labs and interactive materials to reinforce learning
Requires the prerequisite course, 'Accounting Data Analytics with Python', which covers data understanding and data preparation
Covers a range of machine learning algorithms, including classification, regression, clustering, text analysis, and time series analysis
Taught by an experienced instructor named Linden Lu
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Reviews summary
Applying ml to accounting problems with python
According to learners, this course offers a strong practical introduction to applying machine learning algorithms to accounting and financial data using Python. Students particularly value the course's focus on real-world applications within the accounting domain, finding the modules on model evaluation and optimization highly relevant and useful. While the course covers a wide range of algorithms, some learners note that the pace can be fast and recommend having a solid foundation in Python and data analytics, ideally from the prerequisite course, to keep up and fully grasp the material. Overall, it's considered a valuable course for those with the necessary background looking to bridge their accounting knowledge with practical ML skills.
Covers a wide variety of ML techniques.
"The course covers a good selection of fundamental machine learning algorithms."
"I was introduced to many different models, from linear regression to random forests."
"Provides a decent overview of common techniques used in data science."
Modules on model evaluation are strong.
"The section on model evaluation and optimization techniques was incredibly insightful and practical."
"Understanding how to properly evaluate and tune models is crucial, and this course teaches it well."
"Learned key metrics and methods like cross-validation that are essential."
Applies ML directly to accounting problems.
"The focus on applying machine learning to accounting problems was exactly what I needed for my job."
"Loved seeing how these ML models can be used with financial datasets."
"This course bridges the gap between accounting and data science effectively for me."
Some learners find the course pace quick.
"The course moves at a quite fast pace, especially if you are new to some concepts."
"It felt rushed in some modules, requiring extra effort to fully grasp everything."
"You need to dedicate significant time each week to keep up with the material."
Essential to have Python & data analytics base.
"Seriously, make sure you have the prerequisite or strong Python skills before starting."
"I struggled initially because my data handling skills in Python weren't solid."
"Having completed the prior course really helped me keep up with the pace and coding."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Machine Learning for Accounting with Python with these
activities:
Prepare for Improved Knowledge Retention
Show steps
Maximize your learning by organizing and reviewing course materials, strengthening your understanding and solidifying your knowledge base.
Show steps
-
Review lecture notes and readings
-
Create a structured study guide
-
Summarize key concepts and ideas
-
Organize and store materials for easy access
Review of 'Machine Learning for Dummies'
Show steps
Gain a solid foundation in machine learning concepts to complement the course material, enhancing your overall understanding.
View
Data Analytics & Visualization All-in-One For...
on Amazon
Show steps
-
Read and understand the key chapters
-
Take notes and highlight important concepts
-
Discuss the key takeaways with classmates or online forums
Practice Linear Regression with Python
Show steps
Sharpen your understanding of linear regression with hands-on practice, enhancing your ability to apply it in accounting scenarios.
Browse courses on
Linear Regression
Show steps
-
Set up a Python session
-
Import necessary libraries
-
Load and prepare your accounting data
-
Create a linear regression model
-
Fit the model to your data
-
Evaluate the model's performance
Four other activities
Expand to see all activities and additional details
Show all seven activities
Guided Tutorial: Decision Trees for Accounting
Show steps
Enhance your understanding of decision trees through a guided tutorial, enabling you to effectively use them for accounting classification tasks.
Browse courses on
Decision Trees
Show steps
-
Find a suitable online tutorial
-
Follow the tutorial step-by-step
-
Implement the concepts in Python
-
Apply the decision tree to your own accounting data
Classify Text Data with Logistic Regression
Show steps
Gain proficiency in classifying text data using logistic regression, a crucial skill for analyzing financial documents.
Browse courses on
Logistic Regression
Show steps
-
Install the necessary libraries
-
Prepare your text data
-
Create a logistic regression model
-
Train the model on your data
-
Evaluate the model's performance
Create a Data Visualization Explaining Model Evaluation Metrics
Show steps
Develop your understanding of model evaluation metrics and communicate them effectively through a visually engaging data visualization.
Browse courses on
Data Visualization
Show steps
-
Choose appropriate data visualization tools
-
Select relevant model evaluation metrics
-
Create a visually appealing and informative visualization
-
Share your data visualization with others
Participate in a Machine Learning Hackathon
Show steps
Test your skills and knowledge in a real-world setting, pushing the boundaries of your abilities and gaining valuable experience.
Browse courses on
Machine Learning
Show steps
-
Find a suitable machine learning hackathon
-
Form a team or work independently
-
Develop a solution to the problem statement
-
Submit your solution and present it
Prepare for Improved Knowledge Retention
Show steps
Maximize your learning by organizing and reviewing course materials, strengthening your understanding and solidifying your knowledge base.
Show steps
Show steps
Show steps
- Review lecture notes and readings
- Create a structured study guide
- Summarize key concepts and ideas
- Organize and store materials for easy access
Review of 'Machine Learning for Dummies'
Show steps
Gain a solid foundation in machine learning concepts to complement the course material, enhancing your overall understanding.
View
Data Analytics & Visualization All-in-One For...
on Amazon
Show steps
Show steps
Show steps
- Read and understand the key chapters
- Take notes and highlight important concepts
- Discuss the key takeaways with classmates or online forums
Practice Linear Regression with Python
Show steps
Sharpen your understanding of linear regression with hands-on practice, enhancing your ability to apply it in accounting scenarios.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Set up a Python session
- Import necessary libraries
- Load and prepare your accounting data
- Create a linear regression model
- Fit the model to your data
- Evaluate the model's performance
Four other activities
Expand to see all activities and additional details
Show all seven activities
Guided Tutorial: Decision Trees for Accounting
Show steps
Enhance your understanding of decision trees through a guided tutorial, enabling you to effectively use them for accounting classification tasks.
Browse courses on
Decision Trees
Show steps
Show steps
Show steps
- Find a suitable online tutorial
- Follow the tutorial step-by-step
- Implement the concepts in Python
- Apply the decision tree to your own accounting data
Classify Text Data with Logistic Regression
Show steps
Gain proficiency in classifying text data using logistic regression, a crucial skill for analyzing financial documents.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Install the necessary libraries
- Prepare your text data
- Create a logistic regression model
- Train the model on your data
- Evaluate the model's performance
Create a Data Visualization Explaining Model Evaluation Metrics
Show steps
Develop your understanding of model evaluation metrics and communicate them effectively through a visually engaging data visualization.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose appropriate data visualization tools
- Select relevant model evaluation metrics
- Create a visually appealing and informative visualization
- Share your data visualization with others
Participate in a Machine Learning Hackathon
Show steps
Test your skills and knowledge in a real-world setting, pushing the boundaries of your abilities and gaining valuable experience.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a suitable machine learning hackathon
- Form a team or work independently
- Develop a solution to the problem statement
- Submit your solution and present it
Career center
Learners who complete Machine Learning for Accounting with Python will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers use their knowledge of machine learning to build, deploy, and maintain machine learning models. This course can help you build a foundation in machine learning, which is a key skill for machine learning engineers. The course also covers topics such as model optimization and hyperparameter tuning, which are essential for machine learning engineers.
Data Scientist
Data Scientist
Data scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. This course can help you build a foundation in machine learning, which is a key skill for data scientists. The course also covers topics such as data pre-processing and model evaluation, which are essential for data scientists.
Financial Analyst
Financial Analyst
Financial analysts use their knowledge of accounting, finance, and data analysis to provide insights and recommendations to businesses and investors. This course can help you build a foundation in machine learning, which is increasingly being used by financial analysts to automate tasks, improve decision-making, and identify new opportunities.
Business Analyst
Business Analyst
Business analysts use their knowledge of business and data analysis to identify and solve business problems. This course can help you build a foundation in machine learning, which is increasingly being used by business analysts to automate tasks, improve decision-making, and identify new opportunities.
Risk Manager
Risk Manager
Risk managers use their knowledge of risk management and data analysis to identify and mitigate risks. This course can help you build a foundation in machine learning, which is increasingly being used by risk managers to automate tasks, improve decision-making, and identify new risks.
Auditor
Auditor
Auditors use their knowledge of accounting and auditing to ensure that financial statements are accurate and complete. This course can help you build a foundation in machine learning, which is increasingly being used by auditors to automate tasks, improve decision-making, and identify fraud.
Forensic Accountant
Forensic Accountant
Forensic accountants use their knowledge of accounting and auditing to investigate financial crimes. This course can help you build a foundation in machine learning, which is increasingly being used by forensic accountants to automate tasks, improve decision-making, and identify fraud.
Tax Accountant
Tax Accountant
Tax accountants use their knowledge of tax laws and accounting to help businesses and individuals minimize their tax liability. This course can help you build a foundation in machine learning, which is increasingly being used by tax accountants to automate tasks, improve decision-making, and identify tax savings opportunities.
Management Consultant
Management Consultant
Management consultants use their knowledge of business and management to help organizations improve their performance. This course can help you build a foundation in machine learning, which is increasingly being used by management consultants to automate tasks, improve decision-making, and identify new opportunities.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use their knowledge of mathematics, statistics, and computer science to solve business problems. This course can help you build a foundation in machine learning, which is increasingly being used by operations research analysts to automate tasks, improve decision-making, and identify new opportunities.
Statistician
Statistician
Statisticians use their knowledge of mathematics, statistics, and computer science to collect, analyze, and interpret data. This course can help you build a foundation in machine learning, which is increasingly being used by statisticians to automate tasks, improve decision-making, and identify new insights.
Data Analyst
Data Analyst
Data analysts use their knowledge of data analysis to identify and solve business problems. This course can help you build a foundation in machine learning, which is increasingly being used by data analysts to automate tasks, improve decision-making, and identify new opportunities.
Software Engineer
Software Engineer
Software engineers use their knowledge of computer science to design, develop, and maintain software applications. This course can help you build a foundation in machine learning, which is increasingly being used by software engineers to automate tasks, improve decision-making, and identify new opportunities.
Computer Scientist
Computer Scientist
Computer scientists use their knowledge of computer science to solve problems and develop new technologies. This course can help you build a foundation in machine learning, which is a key area of computer science. The course also covers topics such as data pre-processing and model evaluation, which are essential for computer scientists.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use their knowledge of mathematics, statistics, and computer science to develop and implement quantitative models for financial institutions. This course can help you build a foundation in machine learning, which is increasingly being used by quantitative analysts to automate tasks, improve decision-making, and identify new opportunities.
For more career information including salaries, visit:
OpenCourser.com/course/lnusql/machine
Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Machine Learning for Accounting with Python.
This comprehensive book provides a practical introduction to machine learning with Python. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation, with a focus on real-world applications.
This practical guide provides step-by-step instructions for implementing machine learning algorithms using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It's a valuable reference for those looking to apply machine learning techniques in Python.
Since this course module covers text analysis, this book provides a comprehensive overview of natural language processing (NLP) techniques with a focus on Python implementation. It covers topics such as text preprocessing, text classification, and sentiment analysis.
This comprehensive book provides an overview of machine learning techniques and their applications in business settings. It covers use cases, real-world examples, and hands-on exercises, making it a practical guide for business professionals.
This beginner-friendly guide introduces the basics of machine learning, covering topics such as supervised and unsupervised learning, data preprocessing, and model evaluation. It's a valuable resource for those with little to no prior knowledge of machine learning.
This textbook provides a comprehensive introduction to machine learning with a focus on Python implementation. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
This classic textbook provides a comprehensive overview of statistical learning theory and methods, including machine learning algorithms.
Provides a practical introduction to machine learning for those with a background in programming and data analysis.
Provides a practical guide to understanding and interpreting machine learning models, addressing the challenges of model interpretability and explainability.
Save
While this course focuses on Python, this book provides a comprehensive overview of data mining techniques using R, another popular programming language for data analysis and machine learning.
This specialized book explores the applications of machine learning in the financial industry. It covers topics such as risk management, portfolio optimization, and algorithmic trading.
While this course focuses on Python, this book provides a comprehensive overview of machine learning techniques using R, another popular programming language for data analysis and machine learning.
This advanced textbook provides a comprehensive overview of machine learning from a Bayesian and optimization perspective, covering topics such as probabilistic models, Bayesian inference, and optimization techniques.
Save
As deep learning is not a major focus of this course, those interested in gaining a deeper understanding of this advanced topic, this book provides a comprehensive overview of deep learning theory and applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/lnusql/machine
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Rating
4.6
Effort
Approx. 64 hours
Level
Intermediate
Via
Coursera
Institution
University of Illinois at Urbana-Champaign
Instructor
Linden Lu
Language
English
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