Machine Learning: Classification

Overview

Explore the dynamic field of Machine Learning with a focus on Classification in this comprehensive course offered by the University of Washington through Coursera. Dive into practical case studies including Analyzing Sentiment and predicting Loan Default to master classification techniques.

You'll learn how to develop models that can distinguish between classes, such as determining sentiment from textual data or assessing financial risk.

This course is designed to equip you with the skills to apply state-of-the-art classifiers across a range of tasks. By the end, you'll be proficient in logistic regression, decision trees, boosting, and implementing these models on large-scale, real-world data sets.

Additionally, the course tackles challenges you'll encounter in practical applications of ML, such as handling missing data and using precision-recall metrics for evaluation.

Key learning objectives include understanding the ins and outs of classification models, dealing with binary and multiclass problems, scaling methods with stochastic gradient ascent, and much more. You'll also get hands-on experience in Python, preparing you for real-world machine learning tasks.

Whether you're looking to boost your skills in Artificial Intelligence, Python, or Machine Learning, this course offers invaluable insights, techniques, and practical knowledge to advance your career or academic pursuits in these fields.

Taught by

Carlos Guestrin and Emily Fox

Subjects

united states