Machine Learning for Accounting with Python

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.

What you will learn

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.

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 technics to evaluate classification are introduced.