Applied Machine Learning: Supervised Learning
2h 27mIntermediate2025-07-25
Authors
Matt Harrison
Python and Data Science Corporate Trainer, Author, Speaker, Consultant
Course details
In this course, Matt Harrison—a Python and data science corporate trainer, author, speaker, consultant, and advisor—shows you how to apply supervised learning techniques to real-world problems, focusing on both classification and regression tasks. Start with basic models like linear regression, and then advance to more complex algorithms like decision trees and XGBoost. Plus, explore model evaluation, optimization, and deployment techniques. With practical challenges and solutions, this course prepares you to apply supervised learning to solve problems in industries like healthcare, finance, and real estate.
Learning objectives
Implement and evaluate both classification and regression models using supervised learning.
Fine-tune and optimize machine learning models using cross-validation and hyperparameter tuning.
Apply advanced techniques like ensemble methods (bagging and boosting) and XGBoost.
Deploy a trained supervised learning model in a real-world application using Flask.
Learning objectives
Implement and evaluate both classification and regression models using supervised learning.
Fine-tune and optimize machine learning models using cross-validation and hyperparameter tuning.
Apply advanced techniques like ensemble methods (bagging and boosting) and XGBoost.
Deploy a trained supervised learning model in a real-world application using Flask.
Skills covered
Machine Learning FundamentalsTraditional AI and Machine LearningArtificial Intelligence (AI)One-Off
Concepts
Introduction
- A look at supervised learning
- What you should know
- How to use Codespaces
Introduction to Supervised Learning
- What is supervised learning
- Data - Features, labels, training sets
- Metrics for classification and regression
Linear Regression
- What is linear regression
- Implementing linear regression in Python
- Evaluating linear regression
- Challenge - Evaluate linear regression
- Solution - Evaluate linear regression
Classification Algorithms
- What is classification
- Logistic regression
- K-nearest neighbors
- Decision trees
- Challenge - A classification model
- Solution - A classification model
Overfitting and Underfitting
- Understanding overfitting and underfitting
- Decision stumps
- Overfitting
- Cross-validation and Goldilocks
- Challenge - Goldilocks model
- Solution - Goldilocks model
Additional Techniques
- Ensembles - Bagging and boosting
- Tuning hyperparameters
- Explaining simple models
- SHAP (SHapley Additive exPlanations)
- Challenge - XGBoost model
- Solution - XGBoost model
Deployment
- Deploying with Flask
- Querying the model
- Challenge - Deployment
- Solution - Deployment
Conclusion
- Next steps in your machine learning journey