Practical Machine Learning

Overview

Embark on a journey into the world of Artificial Intelligence and Machine Learning with Johns Hopkins University's practical course, offered through Coursera. This course demystifies the basic components involved in building and applying prediction functions, targeting both budding data scientists and data analysts. Participants will gain foundational knowledge in essential concepts such as training and tests sets, the pitfalls of overfitting, and understanding error rates.

The curriculum extends to a variety of model-based and algorithmic machine learning methods, including but not limited to regression, classification trees, Naive Bayes, and random forests. Beyond theory, the course emphasizes practical application, guiding learners through the entire process of building prediction functions. From data collection and feature creation to algorithms and evaluation, this course ensures a comprehensive understanding of the subject matter. Delve into the realms of Artificial Intelligence and Machine Learning with this rigorous yet accessible course, categorized under Artificial Intelligence Courses and Machine Learning Courses.

Syllabus

Taught by

Jeff Leek

Tags

united states