Machine Learning for Accounting with Python

This module provides an overview of the course, including details about the instructor, syllabus, glossary, and the online education platform used. Students can also familiarize themselves with the discussion forums and engage with their peers.

Module 1 introduces students to the fundamentals of machine learning, focusing on topics such as data preprocessing, machine learning algorithms, and their application. The module includes a quiz and a programming assignment to reinforce the concepts learned.

Module 2 delves into fundamental machine learning algorithms, including linear regression, logistic regression, and decision trees. Students gain practical experience through a quiz and a programming assignment to solidify their understanding.

Module 3 expands on fundamental algorithms, covering topics such as K-nearest neighbors, support vector machine, and bagging and random forest. The module includes a quiz and a programming assignment to assess students' grasp of the concepts.

Module 4 focuses on model evaluation, including regressive evaluation metrics and classification evaluation metrics. Students engage in a quiz and a programming assignment to apply their knowledge to real-world scenarios.

Module 5 explores model optimization, discussing feature selection, cross-validation, and model selection. The module includes a quiz and a comprehensive programming assignment to sharpen students' skills in optimizing machine learning models.

Module 6 provides an introduction to text analysis, covering topics such as text analytics and text classification. Students can test their understanding through a quiz and a programming assignment focused on text analysis.

Module 7 introduces students to clustering, including K-means clustering, density-based clustering, and a case study. The module incorporates a quiz and a programming assignment to enhance students' proficiency in clustering techniques.

Module 8 offers insights into time series data, encompassing working with dates and times and analyzing time series data. After completing this module, students can obtain their course certificate and provide feedback on the course.