Machine Learning with Python

In this module, you will explore two essential regression techniques used in machine learning—linear and logistic regression. You’ll explain the role of regression in predicting outcomes, describe the differences between simple and multiple linear regression, and apply both using scikit-learn on real-world data. You will also interpret how polynomial and non-linear regression models capture complex patterns. The module introduces logistic regression as a classification method and guides you in training and testing classification models effectively. To support your learning, you’ll receive a Cheat Sheet: Linear and Logistic Regression that summarizes key concepts, formulas, and use cases.

In this module, you will build and evaluate a range of supervised machine learning models to solve both classification and regression problems. You’ll start by describing how classification models predict categorical outcomes, and implement multi-class classification strategies using real-world data. You’ll then explore how decision trees make predictions and apply them to both classification and regression tasks. The module also covers using support vector machines (SVM) for fraud detection, applying K-Nearest Neighbors (KNN) for customer classification, and training ensemble models like Random Forest and XGBoost to improve accuracy and efficiency. You’ll differentiate bias and variance in model performance and explore how ensemble methods help balance this tradeoff. To support your learning, you’ll receive a Cheat Sheet: Building Supervised Learning Models with key terms, model types, and evaluation tips.

In this module, you will learn how unsupervised learning techniques uncover hidden patterns in data without using labeled responses. You’ll describe clustering concepts and apply K-Means to real-world customer segmentation tasks. You’ll also compare DBSCAN and HDBSCAN models to identify dense clusters in spatial data. Moving beyond clustering, you’ll explore dimensionality reduction as a tool for simplifying high-dimensional datasets. You’ll apply PCA to uncover key components and use advanced techniques like t-SNE and UMAP to visualize data structure. To support your learning, you’ll receive a Cheat Sheet: Building Unsupervised Learning Models, highlighting core methods, practical use cases, and comparison guidelines.

In this module, you will learn how to assess the effectiveness of machine learning models using industry-standard evaluation and validation techniques. You’ll explain key classification and regression metrics, evaluate models using real-world data, and interpret results with tools like confusion matrices and feature importance charts. You'll explore how to assess clustering quality in unsupervised learning and apply cross-validation to reduce overfitting. The module also introduces regularization methods to improve model generalization and reduce feature complexity. Finally, you'll build complete machine learning pipelines and optimize them with GridSearchCV, while identifying common pitfalls like data leakage. To support your learning, you’ll receive a Cheat Sheet: Evaluating and Validating Machine Learning Models covering key metrics, techniques, and model tuning strategies.

In this final module, you will apply and demonstrate the full range of skills you have gained throughout the course. You will start with a practice project using the Titanic dataset to build and optimize classification models using pipelines, cross-validation, and hyperparameter tuning. Then, you will complete the final project by developing a rainfall prediction classifier using historical weather data. This includes data cleaning, feature engineering, model building, and evaluating performance. To conclude the course, you will take a graded final exam that tests your knowledge across all six modules. This module gives you the opportunity to showcase your learning in both practical and theoretical contexts.