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Machine Learning for Accounting with Python

4.6 Filled star Filled star Filled star Filled star Half star
Based on 43 ratings
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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.

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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.

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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.
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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

Machine learning for accounting course

Learners say that this well-structured course on machine learning is great for beginners. However, it can be challenging and some find the instructor's accent difficult to understand.
The course is well-structured and easy to understand.
"This course is very well structured and formatted for better understanding the variety of methods in machine learning."
"This is a great introductory course on machine learning with really practical examples. It does not go too deep."
"The course is a great one for Machine Learning Journey"
The difficulty of the course varies a lot.
"The information provided is very good, however it's very difficult to understand the teacher's accent."
"The course overall is good but there is too much information for a beginner and plus there is no proper explanation in the videos"
"I found the subjects quite challenging and without hints provided I am not sure if I was able to complete all the tasks."

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
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'
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
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
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
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
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
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

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 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.
See salaries and explore the career path for Machine Learning Engineer
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.
See salaries and explore the career path for Data Scientist
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.
See salaries and explore the career path for Financial 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.
See salaries and explore the career path for Business Analyst
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.
See salaries and explore the career path for Risk Manager
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.
See salaries and explore the career path for Auditor
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.
See salaries and explore the career path for Forensic 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.
See salaries and explore the career path for Tax Accountant
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.
See salaries and explore the career path for Management Consultant
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.
See salaries and explore the career path for Operations Research Analyst
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.
See salaries and explore the career path for Statistician
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.
See salaries and explore the career path for Data Analyst
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.
See salaries and explore the career path for Software Engineer
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.
See salaries and explore the career path for Computer Scientist
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.
See salaries and explore the career path for Quantitative Analyst

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.
Machine Learning Con Pytorch Y Scikit Lean
Save
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.
(Español) Machine Learning con PyTorch y Scikit-Learn:...
Paperback
$$$
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
This practical guide provides step-by-step instructions for implementing machine learning algorithms using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It's a valuable reference for those looking to apply machine learning techniques in Python.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Natural Language Processing with Python
Save
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.
Natural Language Processing with Python: Analyzing...
Paperback
$$$
Natural Language Processing with Python: Analyzing...
Kindle Edition
$$
Cover image
Data Driven Business Transformation
Save
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.
Data Driven Business Transformation: How to Disrupt...
Hardcover
$$
Data Driven Business Transformation: How to Disrupt...
Kindle Edition
$$
Cover image
Data Analytics & Visualization All-in-One For...
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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.
Data Analytics & Visualization All-in-One For...
Paperback
$$
Data Analytics & Visualization All-in-One For...
Kindle Edition
$$
Cover image
Introduction to Machine Learning with Python
Save
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.
Introduction to Machine Learning with Python: A...
Kindle Edition
$$
Cover image
The Elements of Statistical Learning
Save
This classic textbook provides a comprehensive overview of statistical learning theory and methods, including machine learning algorithms.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
Machine Learning for Hackers
Save
Provides a practical introduction to machine learning for those with a background in programming and data analysis.
Machine Learning for Hackers: Case Studies and...
Paperback
$$
Machine Learning for Hackers: Case Studies and...
Kindle Edition
$$
Interpretable Machine Learning
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Provides a practical guide to understanding and interpreting machine learning models, addressing the challenges of model interpretability and explainability.
Interpretable Machine Learning: A Guide For Making...
Paperback
$$$
Cover image
Data Mining with R
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While this course focuses on Python, this book provides a comprehensive overview of data mining techniques using R, another popular programming language for data analysis and machine learning.
Data Mining with R: Learning with Case Studies,...
Hardcover
$$$
Data Mining with R: Learning with Case Studies...
Hardcover
$$
Data Mining with R: Learning with Case Studies,...
Kindle Edition
$$
Cover image
Causal Factor Investing
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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.
Causal Factor Investing (Elements in Quantitative...
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Causal Factor Investing: Can Factor Investing...
Kindle Edition
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Cover image
Machine Learning with R
Save
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.
Machine Learning with R - Fourth Edition: Learn...
Paperback
$$
Machine Learning with R - Second Edition: Expert...
Kindle Edition
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Machine Learning with R - Third Edition: Expert...
Paperback
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(Italiano) Machine Learning con R: Conoscere le tecniche per...
Kindle Edition
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R: Unleash Machine Learning Techniques
Kindle Edition
$$$
Machine Learning with R: Learn How to Use R to...
Paperback
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(中文) Machine learning and R language (3rd edition of the...
Paperback
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Cover image
Probabilistic Machine Learning
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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.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$
Cover image
Deep Learning
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As deep learning is not a major focus of this course, those interested in gaining a deeper understanding of this advanced topic, this book provides a comprehensive overview of deep learning theory and applications.
Deep Learning
Paperback
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Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
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Rating
4.6 Filled star Filled star Filled star Filled star Half star
Effort
Approx. 64 hours
Level
Intermediate
Via
Coursera
Institution
University of Illinois at Urbana-Champaign
Instructor
Linden Lu
Language
English
Transcripts
Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова magyar nyelv Bahasa Indonesia ไทย қазақша اُردُو, فارسی বাংলা پښتو آذربایجان دیلی
Machine Learning for Accounting with Python belongs to the following collections:
  1. Accounting Data Analytics
  2. Accounting Data Analytics
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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