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.



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.



At the end of the course you will be able to

  • Understand the underlying math and statistics of Machine Learning
  • Learn Supervised and Unsupervised techniques of Machine Learning
  • Apply these techniques to real-world business problems

EMERITUS and Columbia Engineering

Columbia Engineering is committed to pushing the frontiers of knowledge and shaping discoveries to meet the needs of society. Over the years, Columbia’s faculty and students have made remarkable contributions that have spurred technological and social progress. Today, Columbia carries the tradition of innovation as engineering transforms nearly every aspect of life. Faculty at Columbia Engineering have won 10 Nobel Prizes in physics, chemistry, medicine, and economics.


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  • Faculty Video Lectures
  • Peer Learning
    Moderated Discussion Boards
  • assignment icon
  • Real World Applications
    Application Projects
  • Office Hours with Course Leaders
  • webinar
    Live Online Teaching



Maximum Likelihood, Least Squares, Regularization

Bayes Rule, MAP Inference, Active Learning

Nearest Neighbors, Perceptron, Logistic Regression

Kernel Methods, Gaussian Process

SVM, Trees, Forests and Boosting


K-Means Clustering, E-M, Gaussian Mixtures

Collaborative Filtering, Topic Modeling, PCA

Markov and Hidden Markov Models, Kalman Filters

Model Comparisons, Analysis Considerations


Movie Recommendation Engine

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

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

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

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

Marketing Segmentation

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


  • John Paisley
    John Paisley
    Columbia University Associate Professor, Electrical Engineering
    Affiliated Member, Data Sciences Institute.
    Columbia Engineering Executive Education

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.


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

  • OrientationOrientation 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.
  • Goal SettingWeekly 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.
  • Video LecturesRecorded Video Lectures
    The recorded video lectures are by faculty from the collaborating university.
  • Live WebinarsLive 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 DoubtsClarifying 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.
  • Follow-UpFollow-Up
    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.
  • Continuous Course AccessContinued 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.

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.



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.



  • Starts 13 June 2019
  • 3 Months
  • 6–8 hours per week
  • Course Fees USD 1400


  • The course requires an undergraduate knowledge of statistics (descriptive statistics, regression, sampling distributions, hypothesis testing, interval estimation, etc.), calculus, linear algebra, and probability.
  • All assignments/application projects will be done using the Python programming language. You should have an intermediate knowledge of Python or you should have completed the Emeritus Python for Data Science course prior to joining this course.


  • You can pay for the course either with an international debit or credit card (unfortunately we are unable to accept Diners credit cards), or through a bank wire transfer. On clicking the apply now button below, you will be directed to the application form and the payment page.
  • We provide deferrals and refunds in specific cases. The deferrals and refund policy is available here.
  • You will be provided a course login within 48 hours of making a payment.


  • Please provide your work experience and your current employer via the application.
  • You can apply by clicking the Apply Now button


We have listed two type of FAQs:

  • FAQs common to all courses. These are available at COMMON FAQs
  • Course specific FAQs



  • Applied Machine Learning:
    Teaches you the essential statistical tools and methods, and algorithms that can help you create models that can analyse vast amount of data to predict outcomes that can be immensely useful for your personal and business ventures alike. By working on the real-life application projects, you also acquire the knowledge of how different algorithms are used in different kinds of industry scenarios.
  • Applied Data Science:
    Teaches you the essentials of data science – from extraction, visualization to analysis and insights. Via EDA, this course will let you discover the underlying patterns in the vast quantity of data, and let you answer the whys and whats about those data points using hypothesis testing. In this course you will learn to use foundational ML algorithms to derive sentiments from text, group data points or split datasets to find insights.
  • Applied Artificial Intelligence:
    Teaches you to the essentials of creating intelligent systems. Starting with the foundation of AI, this course teaches you the tools and techniques that make a system intelligent – search techniques, machine learning algorithms to group data points or split datasets to find insights, finding fast and optimal solutions to highly complex problems bound by real-world constraints, decide the best logical course of action to achieve its goal.

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.

The course familiarizes you with Machine learning algorithms and applications and provides a solid foundation in statistics/mathematics and problem-solving skills to help you solve enterprise-level problems. The Applied Machine Learning course augments your existing knowledge of various tools and expands your skill set as a Data Science or Machine Learning professional.

The course familiarizes you with Machine learning algorithms and applications while providing a solid foundation in statistics/mathematics and enhancing your business acumen. It augments your existing programming knowledge and expands the technologies you are familiar with, helping you further develop your skill set as a Data Science or Machine Learning professional.

Absolutely! Knowledge of Data Science and Machine Learning (ML) has quickly become a requisite across industries, and all businesses will eventually need to use these techniques to thrive. While your current role may not require Machine learning knowledge, it is almost certain that ML skills will be in high demand in most every industry in the future.

The course is a blend of theory, tools, and case studies (datasets) that are easy to assimilate and implement. For instance, students work on application projects that require them to apply the Machine Learning concepts they’ve learned to datasets and derive inferences. These application projects are intentionally made to be challenging, and students are expected to spend substantial time and effort solving them; likely eight to 10 hours per week. At the end of the course, students will be able to apply Machine Learning to solve many of the business problems they face in their workplace.

Columbia Engineering Executive Education is collaborating with online education provider EMERITUS Institute of Management to offer a portfolio of high-impact online courses. These courses leverage Columbia’s thought leadership in management practice developed over years of research, teaching, and practice.

Recommended System Requirements

  • Processors: 2.60 GHz
  • RAM: 8 GB of RAM
  • Disk space: 2 to 3 GB
  • Operating systems: Windows 10, MacOS and Linux
  • Python download link
  • Compatible tools: Any text editor, Command prompt

Minimum System Requirements

  • Processors: 1 GHz
  • RAM: 1 GB of RAM
  • Disk space: 1 GB
  • Operating systems: Windows 7 or later, MacOS and Linux
  • Python versions: 2.7.X, 3.6.X
  • Compatible tools: Any text editor, Command prompt


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