In this module, you will explore two foundational statistical modeling methods, linear regression and logistic regression, which are considered classical machine learning models. Linear regression, often applied in real-world problem-solving, models a linear relationship between independent variables and a dependent variable. Logistic regression, an extension of linear regression, functions as a classifier and can handle nonlinear relationships through input transformation. By implementing these models, you'll gain insight into their limitations and better understand the advancements offered by modern machine learning models.

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, such as KNN and SVM. You’ll learn what bias and variance are in model fitting and the tradeoff between bias and variance 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.

In this module, you'll explore unsupervised learning, a machine-learning approach that doesn't require labeled data. Instead of using correct answers, these algorithms identify patterns in data based on similarity. These patterns form clusters in an N-dimensional feature space, where data points that are close together can be considered clusters. Clusters may have a hierarchical structure, similar to natural systems such as galaxies or biological taxonomies. You'll learn about clustering algorithms and how unsupervised learning can reduce features for other modeling tasks, using Python to implement various clustering and dimensionality reduction techniques.

In this module, you will learn how to evaluate the performance of supervised machine learning models using various metrics, depending on whether you are building classification or regression models. You will explore hyperparameter tuning techniques like cross-validation to prevent overfitting and ensure an unbiased model evaluation. Additionally, you will learn about regularization techniques for linear regression to mitigate overfitting caused by noise and outliers. Finally, you will gain hands-on experience in building, fine-tuning, and evaluating models using these techniques.

This module focuses on applying and demonstrating the skills you have gained throughout the course by completing a comprehensive final assignment. In this assignment, you will analyze historical rainfall data to develop and optimize a classification model. You will perform feature engineering, evaluate the model's performance using different classifiers, and summarize your findings through visualizations. Once completed, your assignment will be graded automatically by an AI grading tool in the next section.