Credit Scoring With Machine Learning: A Practical Guide

Learn Credit Scoring, Machine Learning, and Python

What you'll learn

Develop a solid understanding of credit scoring and risk-based pricing, and how these concepts are used in real-world lending decisions

Build, train, and evaluate machine learning models using Scikit-learn and Python

Explore and prepare credit data using pandas and Jupyter Notebook

Interpret model outputs and performance metrics, including confusion matrices, ROC curves, AUC, and cost-based evaluation

Understand the impact of false positives and false negatives, and how to balance them in credit scoring use cases

Apply cross-validation techniques, divergence analysis, and risk-based grouping

Use Scikit-learn Pipelines to streamline preprocessing and ensure reproducible, production-ready workflows

Translate technical results into business insights, empowering data-driven decision-making in credit risk and beyond

Requirements

Basic knowledge of data analysis concepts

Basic knowledge of Python (helpful but not required)

No prior experience with credit scoring or machine learning needed

Description

This course is designed to give you practical, hands-on skills and a clear, structured path to understanding credit scoring with machine learning - a vital topic in today's data-driven finance and fintech sectors.Led by a data scientist with over 12 years of experience in analytics, machine learning, and developing AI-powered applications, this course focuses on real-world implementation - not just theory.This course will give you the tools and mindset you need to build, evaluate, and understand credit scoring models using Python and Scikit-learn.Tools and Technologies:PythonJupyter NotebookPandasMatplotlib & SeabornScikit-learnThis course is project-driven, beginner-friendly, and highly practical. Each topic includes step-by-step demonstrations and visual explanations to help you confidently apply what you learn.By the end of this course, you'll not only be able to build a credit scoring model, but also understand the business implications of your predictions - a skill that’s essential in regulated industries like lending and finance.At the same time, credit scoring serves as an excellent real-world case study for learning machine learning. So even if your goal is to break into machine learning more broadly - beyond finance - you'll gain valuable experience working with data, applying algorithms to solve classification problems, and interpreting model performance in a practical context.

Overview

Section 1: Welcome to the Course

Lecture 1 Course Introduction

Section 2: Credit Scoring and Risk-Based Pricing

Lecture 2 Introduction

Lecture 3 Loan Application Process

Lecture 4 Credit Score

Lecture 5 Credit Scoring

Lecture 6 Risk-Based Pricing

Section 3: Introduction to Data Exploration and Analysis

Lecture 7 Introduction

Lecture 8 Installing Jupyter Notebook Using Anaconda

Lecture 9 Jupyter Notebook Interface

Lecture 10 Key Python Libraries for Data Analysis

Lecture 11 Dataset Analysis

Section 4: Machine Learning in Credit Scoring

Lecture 12 Introduction

Lecture 13 Exploring the Credit Scoring Dataset

Lecture 14 Types of Machine Learning

Lecture 15 Machine Learning Workflow Overview

Lecture 16 Introduction to Scikit-Learn

Lecture 17 Confusion Matrix

Lecture 18 Implications of False Positives in Credit Scoring

Lecture 19 Implications of False Negatives in Credit Scoring

Lecture 20 Performance Metrics

Lecture 21 Logistic Regression Classifier

Lecture 22 Balancing False Positives and False Negatives

Lecture 23 Logistic Regression Classifier – demo

Lecture 24 Random Forest

Lecture 25 Decision Tree Structure

Lecture 26 Random Forest – demo

Lecture 27 Scikit-learn Pipeline

Lecture 28 Scikit-learn Pipeline – demo

Lecture 29 Saving and Loading Machine Learning Models for Predictions

Lecture 30 Predictions with Random Forest Pipeline – demo

Lecture 31 k-fold cross-validation

Lecture 32 k-fold cross-validation – demo

Lecture 33 ROC, AUC, and Cost-Based Metrics

Lecture 34 Divergence Analysis

Lecture 35 Risk-Based Grouping

Lecture 36 Wrapping Up: Key Takeaways and Next Steps

Data scientists and analysts who want to deepen their understanding of credit scoring,Beginner Python developers who are curious about machine learning and want a practical, applied case study to start with,Data analysts looking to transition into machine learning roles,Credit risk professionals seeking to understand how machine learning can be used in lending decisions,Software developers and engineers interested in how credit scoring systems work and how to implement them,Anyone interested in AI applications