Applied Machine Learning

Learn Python programming, write programs to implement machine learning in business. *The course requires an undergraduate knowledge of statistics, calculus, linear algebra, and probability.
Inquiring For
Total Work Experience

STARTS ON

TBD

DURATION

5 months, online

8-10 hours per week

FOR TEAMS

Enroll your team and learn with your peers

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, but no prior programming knowledge is required.

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.

$ 200B

The forecasted amount of global AI investment by 2025
SOURCE: Goldman Sachs

11.5 million

The number of jobs that will be created by 2026 due to developments in data science.
SOURCE: U.S. BUREAU OF LABOR STATISTICS

$ 132k

The average annual salary of data scientists in the United States in 2024
SOURCE: Talent.com

Course Highlights

Decorative image relating to 240+ Faculty Video Lectures

240+ Faculty Video Lectures

Decorative image relating to 45 Quizzes / Assignments

45 Quizzes / Assignments

Decorative image relating to 18 Moderated Discussion Boards

18 Moderated Discussion Boards

Decorative image relating to 20+ Q&A Sessions with Course Leaders

20+ Q&A Sessions with Course Leaders

Decorative image relating to 12 Application Projects

12 Application Projects

Decorative image relating to Includes Live Online Teaching

Includes Live Online Teaching

Learning Journey

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:

  • Define a model for your data and make the model learn.

  • Build regression models to predict an unknown output from a given set of inputs.

  • Create classification models to categorize datasets such as email messages as spam or non-spam.

  • Develop unsupervised models like topic models or recommender systems to extract hidden patterns from large amounts of data

  • Determine hidden parameters in data to improve the accuracy of your model's predictions.

  • Create probabilistic data models to predict a range of possible outcomes that account for real-world risks and uncertainties.

Program Topics

Designed to teach you models and methods used in machine learning for real-world applications such as recommender systems and classification models, this 5-month program begins by building your foundational skills in Python, followed by the supervised and unsupervised learning techniques of applied machine learning.

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

LP - CE-AMLPY - Faculty - Image
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...

Testimonials

Certificate

Certificate

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

Financing Options

Invite a Colleague
Amplify your impact

Receive US$247

For each referral when you invite colleagues to the program.

For Organizations
Team-Based Learning

  • Upskill with a program from a top university

  • Leverage cohort-based learning, for high completion rates

Will Your Employer Sponsor Your Learning?

Many organizations have historically sponsored their employees for our executive education programs. Please check with your employer if they can cover your fee. We can assist you with the necessary documentation and support.

  • Preparing Your Pitch

    If you require company approval, we offer a customizable email template that you can use to show how the program will contribute to your growth.

  • Invoice Requirements

    An invoice will be issued to you after payment. If you require any customization, our advisory team can assist you.

  • Part/Full Sponsorship

    Our advisors are here to support you throughout the reimbursement process, whether your company covers the fee fully or partially.

  • Additional Questions

    Should your employer require specific information to approve your reimbursement, our advisory team is well-equipped to help you.

Course FAQs

Didn't find what you were looking for? Write to us at learner.success@emeritus.org or Schedule a call with one of our Program Advisors or call us at +1 315 840 3227 (US) / +44 203 838 0797 (UK) / +65 3129 4176 (SG)

Early registrations are encouraged. Seats fill up quickly!

Flexible payment options available.

Starts On TBD