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Machine Learning Fundamentals

University of California, San Diego via edX

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Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world?

In this course, part of the Data Science MicroMasters program, you will learn a variety of supervised and unsupervised learning algorithms, and the theory behind those algorithms.

Using real-world case studies, you will learn how to classify images, identify salient topics in a corpus of documents, partition people according to personality profiles, and automatically capture the semantic structure of words and use it to categorize documents.

Armed with the knowledge from this course, you will be able to analyze many different types of data and to build descriptive and predictive models.

All programming examples and assignments will be in Python, using Jupyter notebooks.

Taught by

Sanjoy Dasgupta

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Review for edX's Machine Learning Fundamentals
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3.0 2 months ago
Anonymous completed this course.
This is the second course from UCSanDiegoX (early 2018 run) that breaks the enrollment clause stating "Audit this course for free and have complete access to all the course material, activities, tests, and forums". There was no final exam for Audit students. Hopefully this will get corrected in the future runs of this course. The presence of staff on the Audit mode forum is very scarce, and the forums are divided into Verified/Audit students.

The course provides the "fundamentals" in only first few chapters. The more the courses advances the less mathematics is presented which ham…
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