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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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Module 1: Introduction to Machine Learning
This module provides the basis for the rest of the course by introducing the basic concepts behind machine learning, and, specifically, how to perform machine learning by using Python and the scikit-learn machine learning module. First, you will learn about the basic types of machine learning. Next, you will learn an important step before applying machine learning algorithms, data pre-processing. Finally, you will learn how to leverage different types of machine learning algorithms in a Python script.
Module 2: Fundamental Algorithms I
This module introduces three machine learning algorithms. First, you will learn how linear regression can be considered a machine learning problem with parameters that must be determined computationally by minimizing a cost function. Next, you will learn Logistic Regression. Despite its name, Logistic Regression is a classification algorithm. Lastly, you will learn Decision Tree, which is a popular machine learning algorithm that can be used for both classification and regression. This module will dive deeper into the concept of machine classification, where algorithms learn from existing, labeled data to classify new, unseen data into specific categories; and, the concept of machine regression, where algorithms learn a model from data to make predictions for new, unseen continuous data. While these algorithms all differ in their mathematical underpinnings, they are often used for classifying numerical, text, and image data or performing regression in a variety of domains.
Module 3: Fundamental Algorithms II
This module introduces three more machine learning algorithms, k-nearest neighbors, support vector machine and random forest. All of them can be used for either classification or regression tasks.
Module 4: Model Evaluation
Model Evaluation is an integral component of any data analytics project. It helps to find out how well the model will work on predicting future (out-of-sample) data. This module introduces basic model evaluation metrics for machine learning algorithms. First, the evaluation metrics for regression is presented. Next the metrics and techniques to evaluate classification are introduced.
Module 5: Model Optimization
This module introduces the techniques of model optimization. First, the basic techniques of feature selection is presented. Next, the technique of cross-validation is introduced, which can provide a more accurate evaluation on models. Finally, model selection, or hyperparameter tuning, which uses cross-validation, is introduced.
Module 6: Introduction to Text Analysis
In this module, you will start applying your new machine learning skills to an exciting data analytic topic: Text Analysis. First, we will review the process by which textual data is converted into numerical data that can be processed by a computer. Along with this are a number of new concepts that focus on manipulating these data to generate improved machine learning predictions. Second, we will apply machine learning algorithms, specifically classification, to text data. Finally, we will explore the more advanced concepts in text analysis and introduce a special kind of text classification: sentiment analysis.
Module 7: Introduction to Clustering
This module introduces clustering, where data points are assigned to sub groups of points based on some specific properties, such as spatial distance or the local density of points. While humans often find clusters visually with ease in a given data sets, computationally the problem is more challenging. This module starts by exploring the basic ideas behind this unsupervised learning technique. One of the most popular clustering techniques, K-means, is introduced. Next, a K-means case study is provided. Finally the density-based DBSCAN technique is introduced.
Module 8: Introduction to Time Series Data
This module introduces time and date data, which provide unique learning opportunities and challenges. First, we will discuss how to properly handle time and date features within a Python program. Next, we will extend this discussion to handle data indexed by time and date information, which is known as time series data.

Good to know

Know what's good
, what to watch for
, 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.
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.
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 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 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 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
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.
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.
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.
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 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
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 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 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 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 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.

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.
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.
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.
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.
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.

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