Introduction to Machine Learning: Supervised Learning

University of Colorado Boulder via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Paid Certificate Available
Duration & workload

22 hours 48 minutes
Sessions

On-Demand
Subtitles

English, Arabic, French, Portuguese, German, Thai, Indonesian, Spanish, Kazakh, Hindi, Russian, Swedish, Korean, Greek, Italian, Chinese, Ukrainian, Japanese, Polish, Dutch, Turkish

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Overview

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Introduction to Machine Learning: Supervised Learning offers a clear, practical introduction to how machines learn from labeled data to make predictions and decisions. You’ll build a strong foundation in regression and classification, starting with linear and logistic regression and progressing to resampling, regularization, and tree-based ensemble methods. Along the way, you’ll learn how to evaluate models, manage bias–variance trade-offs, and balance interpretability with predictive power, all while working hands-on in Python. By the end of the course, you’ll have the skills and intuition needed to confidently apply supervised learning techniques to real-world problems. This course can be taken for academic credit as part of CU Boulder’s Masters of Science in Computer Science (MS-CS), Master of Science in Artificial Intelligence (MS-AI), and Master of Science in Data Science (MS-DS) degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Artificial Intelligence: https://www.coursera.org/degrees/ms-artificial-intelligence-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder

Syllabus

Introduction to Supervised Learning & Linear Regression Basics

Welcome to Introduction to Machine Learning: Supervised Learning. In this first module, you will begin your journey into supervised learning by exploring how machines learn from labeled data to make predictions. You will learn to distinguish between supervised and unsupervised learning, and understand the key differences between regression and classification tasks. You will also gain insight into the broader machine learning workflow, including the roles of predictors, response variables, and the importance of training versus testing data. By the end of this module, you will have a solid foundation in the goals and mechanics of supervised learning.

Linear Regression for Prediction & Inference

In this module, you will expand your understanding of linear models by incorporating multiple predictors, including categorical variables and interaction terms. You will learn how to interpret partial regression coefficients and assess the fit of your models using metrics like R² and RMSE. As you build more complex models, you will also explore the risks of overfitting and the importance of model validation. By the end of this module, you will be equipped to build and evaluate multiple linear regression models with confidence.

Classification – Logistic Regression, Discriminant Analysis, & K- Nearest Neighbors

In this module, you will transition from predicting continuous outcomes to modeling categorical ones. You will learn how logistic regression models binary outcomes, like whether a customer will default on a loan, using probabilities and odds, and how to interpret the results. You will also explore k-Nearest Neighbors, a flexible, non-parametric method that classifies observations based on their proximity to others in the dataset. To evaluate your models, you will use tools like confusion matrices, accuracy, and precision/recall, gaining insight into how well your classifiers perform. This module lays the groundwork for tackling real-world classification problems with confidence and clarity.

Model Evaluation, Resampling, & Regularization

In this module, you will learn how to evaluate your models more reliably and improve their generalization to new data. You will explore resampling methods like k-fold cross-validation and the bootstrap, which help estimate test performance without needing a separate test set. You will also be introduced to the regularization techniques Ridge and Lasso that prevent overfitting by constraining model complexity. Using cross-validation, you will learn how to select the optimal regularization strength, balancing predictive accuracy with model simplicity. These tools are essential for building models that perform well not just in theory, but in practice.

Tree-Based Methods & Ensembles

This module introduces you to one of the most intuitive and interpretable machine learning models: decision trees. You will explore how trees split the feature space into regions, how to read their structure, and why they are prone to overfitting if left unchecked. Trees are just the beginning; this module also introduces ensemble techniques that elevate predictive accuracy by combining many models. You will get a first look at methods like bagging, random forests, and boosting, and see how they compare to the models you have already studied. By the end, you will understand when and why tree-based models can outperform simpler approaches, especially in capturing complex, non-linear relationships.