This repository contains a collection of Python notebooks illustrating fundamental concepts and practical applications in machine learning. It is structured into five modules, each focusing on a specific area within the field.
Module 1: Linear and Logistic Regression
Module 2: Building Supervised Learning Models
Module 3: Building Unsupervised Learning Models
Module 4: Evaluating and Validating Machine Learning Models
This module introduces two classical statistical methods foundational to Machine Learning: Linear and Logistic Regression.
Learn how linear regression, pioneered in the 1800s, models linear relationships while logistic regression serves as a classifier. Through implementing these models, understand their limitations and gain insight into why modern machine-learning models are often preferred.
Notes: Pay close attention to the mathematical formulations of these models and the interpretation of their coefficients. Understanding the assumptions underlying each model is crucial for applying them correctly.
Example: For a practical demonstration of linear and logistic regression, please see the Linear_Logistic_Regression.ipynb notebook.
In this module, you’ll learn about implementing modern supervised machine learning models. You will start by understanding how binary classification works and discover how to construct a multiclass classifier from binary classification components. You’ll learn what decision trees are, how they learn, and how to build them. Decision trees, which are used to solve classification problems, have a natural extension called regression trees, which can handle regression problems. You’ll learn about other supervised learning models, like KNN and SVM. You’ll learn what bias and variance are in model fitting and the tradeoff between bias and variance that is inherent to all learning models in various degrees. You’ll learn strategies for mitigating this tradeoff and work with models that do a very good job accomplishing that goal.
Notes: Experiment with different hyperparameters for each model to observe their impact on performance. Understanding the bias-variance tradeoff is key to selecting and tuning supervised learning models effectively.
Example: Explore the implementation of various supervised learning models in the Supervised_Learning_Models.ipynb notebook.
In this module, you’ll dive into unsupervised learning, where algorithms uncover patterns in data without labeled examples. You’ll explore clustering strategies and real-world applications, focusing on techniques like hierarchical clustering, k-means, and advanced methods such as DBSCAN and HDBSCAN. Through practical labs, you’ll gain a deeper understanding of how to compare and implement these algorithms effectively. Additionally, you’ll delve into dimension reduction algorithms like PCA (Principal Component Analysis), t-SNE, and UMAP to reduce dataset features and simplify other modeling tasks. Using Python, you’ll implement these clustering and dimensionality reduction techniques, learning how to integrate them with feature engineering to prepare data for machine learning models.
Notes: Consider the strengths and weaknesses of each clustering and dimensionality reduction technique. The choice of algorithm often depends on the specific characteristics of your data.
Example: Discover how to implement unsupervised learning techniques in the Unsupervised_Learning_Models.ipynb notebook.
This module covers how to assess model performance on unseen data, starting with key evaluation metrics for classification and regression. You’ll also explore hyperparameter tuning to optimize models while avoiding overfitting using cross-validation. Special techniques, such as regularization in linear regression, will be introduced to handle overfitting due to outliers. Hands-on exercises in Python will guide you through model fine-tuning and cross-validation for reliable model evaluation.
Notes: Pay close attention to the different evaluation metrics and when each is most appropriate. Cross-validation is a crucial technique for obtaining a reliable estimate of a model's generalization performance.
Example: Learn about evaluating and validating machine learning models in the Model_Evaluation_Validation.ipynb notebook.
In this concluding module, you’ll review everything in a single project.
Notes: This project will integrate the concepts and techniques learned in the previous modules. Focus on the entire machine learning pipeline, from data preprocessing to model evaluation.
Example: Work through the complete project in the Titanic.ipynb notebook.
All the Jupyter notebooks containing the code examples for each module are located in the notebooks/ directory.