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Learner Reviews & Feedback for Machine Learning: Concepts and Applications by The University of Chicago

About the Course

This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning. A key feature of this course is that you not only learn how to apply these techniques, you also learn the conceptual basis underlying them so that you understand how they work, why you are doing what you are doing, and what your results mean. The course also features real-world datasets, drawn primarily from the realm of public policy. It is based on an introductory machine learning course offered to graduate students at the University of Chicago and will serve as a strong foundation for deeper and more specialized study....
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1 - 3 of 3 Reviews for Machine Learning: Concepts and Applications

By Daniil K

Jan 8, 2023

Course is pretty raw. Most of practice tasks are ambiguous. Some tasks can be done in different ways but the author wants it to be done in one specific.

By Js S

Oct 24, 2022

Good content gone to waste. The outograder doesn't work, so you have to quit the course in the second week.

By Anastasia S

Jul 21, 2022

Autograder gives zero feedback.