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.

Course Highlights

240+ Faculty Video Lectures

45 Quizzes / Assignments

18 Moderated Discussion Boards

20+ Q&A Sessions with Course Leaders

12 Application Projects

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.

1Part 1: Python for Data Analytics (Video content and delivery by Emeritus)

2Part 2: Applied Machine Learning (Video content from Columbia Engineering and delivery by Emeritus)

Application Projects

Movie Recommendation Engine

You will build a movie recommendation engine by applying collaborative filtering and topic modelling techniques. You use a dataset which contains 20 million viewer ratings of 27,000 movies.

House Price Prediction

You will write code to predict house prices based on several parameters available in the Ames City dataset compiled by Dean De Cock using least squares linear regression and Bayesian linear regression.

Human Activity Prediction

You will predict the human activity (walking, sitting, standing) that corresponds to the accelerometer and gyroscope measurements by applying the nearest neighbours technique.

Credit Card Fraud Detection

You will detect potential frauds using credit card transaction data. You will apply the random forest method to identify fraudulent transactions.

Marketing Segmentation

You will create market segments using the US Census dataset and by applying the k-means clustering method.

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Faculty

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

"The best part of this Applied Machine Learning program was its breadth, because it gives you a wide exposure to different ML principles."

Joshua Sampson,

Operations and Finance Leader

"I am thankful for the mathematical foundations in this program that helped me "understand" what we were doing and not just follow methods."

Xavier Gallez,

Technology Leader

"The “Python for Data Science” module of this program was extremely useful, because I learned how to use Python for Machine Learning."

Peter Abdelmalak,

Engineering Leader

"The Office Hours in this Applied Machine Learning program were the best for the real-world practical tutorials."

Michael Whittle,

Technology Leader

Certificate

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

Course FAQs

1 What is the process to enroll for the course?

2 Why should a Statistician/Mathematician enroll in this course?

3 Why should a Data Scientist/Data Engineer/Big Data Engineer enroll in this course?

4 Why should a Software Developer enroll in this course?

5 My current role in my company does not include exposure to Machine Learning. Does it make sense for me to enroll in this course?

6 How can I implement the concepts and ideas discussed in the course?

7 What is the relationship between Emeritus and Columbia Engineering Executive Education?

8 What software and versions will I need in this course?

9 What is the program refund and deferral policy?

10 Who will be collecting the payment for the program?

11 Are there any restrictions on the types of funding that can be used to pay for the program?

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.

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