Applied Machine Learning

Columbia Engineering Executive Education

How long?

  • 5 months
  • online

Columbia Engineering Executive Education


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Who should attend

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.


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).

This course is designed for professionals who intend to transition to the role of a Data Scientist. This course is for you if you are Software developer or a Project manager or a Business analyst or a Data Scientist or a Data Engineer who wants to build a solid foundation in Machine Learning.

Previous batches have come from:

  • Industries: Banking, Software, Consulting, Retail, Consumer Packaged Goods, Healthcare and Energy industries.
  • Countries: United States, India, United Kingdom, Canada, Australia, Hong Kong, Mexico.

About the course

Machine Learning has become an entrenched part of everyday life. It is one of the most exciting fields of computing today. And Machine Learning practitioners are in high demand, with a shortfall of 250,000 data scientists forecast.

Machine Learning has become an entrenched part of everyday life. It is one of the most exciting fields of computing today. And Machine Learning practitioners are in high demand, with a shortfall of 250,000 data scientists forecast.


Master the models and methods of machine learning while acquiring the Python programming knowledge you need to find solutions to real-world data problems. At the end of the course, you will be able to:

  • 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.

Emeritus and Columbia Engineering Executive Education

Columbia Engineering Executive Education is collaborating with online education provider Emeritus to offer executive education courses.

An Emeritus Certificate course created in collaboration with Columbia Engineering Executive Education is based on syllabus approved by Columbia Engineering Executive Education, and contains video content created and recorded by Columbia Engineering Executive Education faculty, combined with assessments, assignments, projects, cases, and exercises delivered by Emeritus. Upon successful completion of the course, learners will be awarded a certificate jointly by Emeritus and Columbia Engineering Executive Education.



  • Module 1: Introduction to Data Science
  • Module 2: Working with Data Types & Operators in Python
  • Module 3: Writing Functions in Python
  • Module 4: Popular Data Science Packages in Python
  • Module 5: Advanced Functions
  • Module 6: Data Manipulation and Analysis with Pandas
  • Module 7: Data Visualization with Matplotlib
  • Module 8: Random Variables & Statistical Inferences
  • Module 9: Statistical Distributions & Hypothesis Testing
  • Module 10: Data Cleaning
  • Module 11: Exploratory Data Analysis
  • Module 12: Getting Started with Linear Algebra for Machine Learning


Supervised Learning

  • Module 1 - Regression
  • Module 2: Linear Regression
  • Module 3 - Bayesian Methods
  • Module 4 - Foundational Classification Algorithms – Part 1
  • Module 5 - Foundational Classification Algorithms – Part 2
  • Module 6 - Intermediate Classification Algorithms – Part 1
  • Module 7: Intermediate Classification Algorithms – Part 2
  • Module 8: Clustering Methods
  • Module 9: Recommendation Systems – Part 1
  • Module 10: Recommendation Systems – Part 2
  • Module 11: Sequential Data Models
  • Module 12: Association Analysis Clustering methods

Unsupervised Learning

  • Module 8 - Clustering Methods
  • Module 9 - Recommendation Systems – Part 1
  • Module 10 - Recommendation Systems – Part 2
  • Module 11 - Sequential Data Models
  • Module 12 - Association Analysis Clustering methods


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.

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.

Marketing Segmentation

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

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.


Emeritus follows a unique online model. This model has ensured that nearly 90 percent of our learners complete their course.

Orientation Week

The first week is orientation week. During this week you will be introduced to the other participants in the class from across the world. You will also learn how to use the learning platform and other learning tools provided.

Weekly Goals

On other weeks, you have learning goals set for the week. The goals would include watching the video lectures and completing the assignments. All assignments have weekly deadlines.
 Recorded Video Lectures

The recorded video lectures are by faculty from the collaborating university.

Live Webinars

Every few weeks, there are live webinars conducted by Emeritus course leaders. Course leaders are highly-experienced industry practitioners who contextualize the video lectures and assist with questions you may have regarding your assignments. Live webinars are usually conducted between 1 pm and 3 pm UTC on Tuesdays and Wednesdays.

Clarifying Doubts

In addition to the live webinars, for some courses, the course leaders conduct Office Hours, which are webinar sessions that are open to all learners. During Office Hours, learners ask questions and course leaders respond. These are usually conducted every alternate week to help participants clarify their doubts pertaining to the content.


The Emeritus Program Support team members will follow up and assist over email and via phone calls with learners who are unable to submit their assignments on time.

Continued Course Access

You will continue to have access to the course videos and learning material for up to 12 months from the course start date.   Assignments/Application Projects

Assignments are given out weekly and they are based on the lectures or tutorials provided. They need to be completed and submitted as per the deadline for grading purposes. Extensions may be provided based on a request sent to the support team.

Discussion Boards

It is an open forum where participants pin their opinions or thoughts regarding the topic under discussion.

  Emeritus Program Support Team

  • If at any point in the course you need tech, content or academic support, you can email program support and you will typically receive a response within 24 working hours or less.   Device Support

  • You can access Emeritus courses on tablets, phones and laptops. You will require a high-speed internet connection.   Emeritus Network

  • On completing the course you join a global community of 5000+ learners on the Emeritus Network. The Network allows you to connect with Emeritus past participants across the world.


Intellectual Capital

  • Global Business Education
  • Rigorous and experiential curriculum
  • World-renowned faculty
  • Globally connected classroom: peer to peer learning circles
  • Action learning: learning by doing

Brand Capital

  • Certificate from Emeritus in collaboration with Columbia Engineering Executive Education

Social Capital

  • Build new networks through peer interaction
  • Benefit from diverse class profiles

Career Capital

  • Professional acceleration through our enriched leadership toolkit
  • Learn while you earn
  • Get noticed. Get ahead.


John Paisley

I am an associate professor in the Department of Electrical Engineering at Columbia University. I am also a member of the Data Science Institute at Columbia. Here is my CV. (Some other info about me here.) I received my Ph.D. in Electrical and Computer Engineering from Duke University, where I ...

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Applied Machine Learning at Columbia Engineering Executive Education

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Coursalytics is an independent platform to find, compare, and book executive courses. Coursalytics is not endorsed by, sponsored by, or otherwise affiliated with any business school or university.

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