About This Course

Loan application rating is a critical task in finance that aims to evaluate the creditworthiness of loan applicants. Accurate loan application rating is essential for financial institutions to manage risks and make informed lending decisions. This course aims to provide students with the knowledge and skills required to build loan application rating models using machine learning techniques.

The course will cover the fundamental concepts of credit risk assessment. The course will be divided into 5 modules, each covering a specific topic.

Introduction to Loan Application Rating -> estimated time = 3 minutes

In the first module of this course, you will be introduced to what is loan application rating system and the purpose to create the loan applicaton rating. Through a combination of text materials and engaging video tutorials, you will gain a comprehensive understanding of the underlying concepts and methodologies employed in loan application rating. Here are the sections that exist in this module:

Preprocessing Data and Feature Selection -> estimated time = 1 hour

This second module covers 8 sections that delve into the intricacies of data pre-processing. Through text materials, video tutorials, a theoretical quiz, and a coding assignment, you will gain a deep understanding of pre-processing techniques. Learn how to load dataset, define variables, clean and transform data, and feature selections through a video-based learning approach. It also has text materials provided to make it easier for you to learn data pre-processing.

To assess their understanding of the material covered in the video, you will be required to complete a quiz consisting of 10 multiple-choice questions. In addition to the quiz, you will be given a coding assignment that will require them to apply the data preprocessing and feature selection techniques covered in the module to a loan application dataset. This assignment will provide you with hands-on experience in implementing these techniques, and will help to reinforce their understanding of the concepts covered in the module. Here are the sections that exist in this module:

Building Model Machine Learning Classification -> estimated time = 51 minutes

This third module covers 4 sections that delve into the intricacies of building machine learning model. Through text materials, video tutorials, a theoretical quiz, and a coding assignment, you will gain a deep understanding of building machine learning model techniques. Learn how to build machine learning model for loan application rating through a video-based learning approach. It also has text materials provided to make it easier for you to learn the module.

To assess their understanding of the material covered in the video, you will be required to complete a quiz consisting of 10 multiple-choice questions. In addition to the quiz, you will be given a coding assignment that will require them to build a machine learning model for loan application rating. This assignment will build upon the data preprocessing and feature selection techniques covered in the previous module, and will provide you with hands-on experience in applying machine learning techniques to real-world problems. Here are the sections that exist in this module:

Evaluate Model Machine Learning -> estimated time = 56 minutes

This fourth module covers 5 sections that delve into the intricacies of how to evaluate the machine learning models they have build in previous module. Through text materials, video tutorials, a theoretical quiz, and a coding assignment, you will gain a deep understanding of evaluating machine learning model techniques. Learn how to evaluate the machine learning model for loan application rating system with evaluation metrics such as confusion matrix, accuracy, precision, recall, F1 score, and ROC curves through a video-based learning approach. It also has text materials provided to make it easier for you to learn the module.

To assess their understanding of the material covered in the video, you will be required to complete a quiz consisting of 10 multiple-choice questions. In addition to the quiz, you will be given a coding assignment that will require them to evaluate the machine learning models they built in the previous module for loan application rating. This assignment will provide you with hands-on experience in applying the evaluation metrics and techniques covered in the module to real-world problems, and will help to reinforce their understanding of the material covered in the course. Here are the sections that exist in this module:

Hyperparamter Tuning -> estimated time = 51 minutes

This fifth module covers 4 sections that delve into the intricacies of how to tune their machine learning models. Through text materials, video tutorials, a theoretical quiz, and a coding assignment, you will gain a deep understanding of hyperparameter tuning techniques. Learn how to tune the machine learning model for loan application rating system through a video-based learning approach. It also has text materials provided to make it easier for you to learn the module.

To assess their understanding of the material covered in the video, you will be required to complete a quiz consisting of 10 multiple-choice questions. In addition to the quiz, you will be given a coding assignment that will require them to perform hyperparameter tuning on the machine learning models they built in the previous module for loan application rating. This assignment will provide you with hands-on experience in applying hyperparameter tuning techniques to real-world problems, and will help to reinforce their understanding of the material covered in the course. Here are the sections that exist in this module:

By the end of this course, you will have a solid understanding of the fundamentals of loan application rating and how to build accurate loan application rating models using machine learning techniques. Students will also have the skills necessary to apply their models to real-world loan application data and evaluate their performance. This course is an excellent starting point for anyone interested in credit risk assessment or pursuing a career in finance-related fields that require loan application rating expertise.