COVID-19 STATEMENT: While this virus is impacting everyone differently, this online program is continuing as planned.
Please consider joining our global online classroom for an enriching and interactive experience to further your career.
While this virus is impacting everyone differently, this online program is continuing as planned. Please consider joining our global online classroom for an enriching and interactive experience to further your career.

STARTS ON

October 27, 2020

DURATION

5 Months, Online
8-10 hours per week

Who is this course for?

The Applied Machine Learning course teaches you a wide-ranging set of techniques of supervised and unsupervised machine learning approaches using Python as the programming language.

Since this course requires an intermediate knowledge of Python, you will spend the first part of this course learning Python for Data Analytics taught by Emeritus. This will provide you with the programming knowledge required to do the assignments and application projects that are part of the Applied Machine Learning course.

If you are looking to implement or lead a machine learning project or looking to incorporate machine learning capability in your software application, this course is appropriate for you. This is a programming course: you will be required to write code.

$57.6B

anticipated spending growth on AI and ML by 2021.

SOURCE: INTERNATIONAL DATA CORPORATION (IDC)

16%

expected wage growth for data scientists (vs. <2% average wage increase across all occupations).

SOURCE: U.S. BUREAU OF LABOR STATISTICS

225%

decrease in ‘click-to-ship’ time by Amazon using ML algorithm.

SOURCE: FORBES
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Course Highlights

240+
Faculty Video Lectures

45
Quizzes / Assignments

20+
Q&A Sessions with Course Leaders

18
Moderated Discussion Boards

12
Application
Projects

Includes Live Online Teaching

Going beyond the theory, our approach invites participants into a conversation, where learning is facilitated by live subject matter experts and enriched by practitioners in the field of machine learning:

Syllabus

PREREQUISITES:
● The course requires an undergraduate knowledge of statistics (descriptive statistics, regression, sampling distributions, hypothesis testing, interval estimation etc.), calculus (derivatives), linear algebra (vectors & matrix transformation) and probability (conditional probability/Bayes theorem).

*Assessment: Students will be given an assessment to test their math skills prior to commencement of the course. You can view sample questions by clicking here. To familiarize yourself with the topics of the assessment, refer to learning resources by clicking here.

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Application Projects

Note: All product and company names are trademarks™ or registered® trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them.

Faculty

John W. Paisley
Dr. John W. Paisley
Columbia University Associate Professor, Electrical Engineering
Affiliated Member, Data Sciences Institute

John has a PhD from Duke and has been a postdoctoral researcher in the Computer Science departments at Princeton University and UC Berkeley. John Paisley’s research focuses on developing models for large-scale text and image processing applications. He is particularly interested in Bayesian models and posterior inference techniques that address the big data problem.

Course FAQs

The course familiarizes you with Machine learning algorithms and applications. It will also help you understand the approach to a business problem and provide you with the tool knowledge needed to transition to a Machine Learning or a Data Science role.

Certificate

Applied Machine Learning Certificate

Certificate

Upon successful completion of the course, participants will receive a verified digital certificate from Emeritus in collaboration with Columbia Engineering Executive Education.

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Early applications encouraged. Seats fill up soon!

Flexible payment options available. Click here to know more.