Machine Learning for Data Science and Analytics

Online Course

edX
Machine Learning for Data Science and Analytics

What is the course about?

Machine Learning for Data Science and Analytics
The course Machine Learning for Data Science and Analytics is an online class provided by Columbia University through edX. The skill level of the course is Introductory. It may be possible to receive a verified certification or use the course to prepare for a degree.
Learn the principles of machine learning and the importance of algorithms.
Course description
Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications. This data science course is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. We will also examine why algorithms play an essential role in Big Data analysis.

Prerequisites & Facts

Machine Learning for Data Science and Analytics

Course Topic

Business and Management, Computer Science, Data Analysis and Statistics

University, College, Institution

Columbia University

Course Skill Level

Introductory

Course Language

English

Place of class

Online, self-paced (see curriculum for more information)

Degree

Certificate

Degree & Cost

Machine Learning for Data Science and Analytics

To obtain a verified certificate from edX / Columbia University you have to finish this course or the latest version of it, if there is a new edition. The class may be free of charge, but there could be some cost to receive a verified certificate (99.00 USD) or to access the learning materials. The specifics of the course may have been changed, please consult the provider to get the latest quotes and news.
Columbia University
Machine Learning for Data Science and Analytics
provided by edX

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Machine Learning for Data Science and Analytics
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School: Columbia University
Topic: Business and Management, Computer Science, Data Analysis and Statistics
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