13 June 2019


3 Months, Online
6-8 hours per week


USD 1,400

info Flexible payment available

What is the Applied Machine Learning course about?

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

This course is appropriate for you, if you are looking to implementing or leading a machine learning project in your organization or developing custom ML functions to incorporate in your application.

Learning outcomes

At the end of this course, you will be able to


Your Learning Journey

Faculty Video Lectures

Quizzes / Assignments

Q&A Sessions with Course Leaders

Moderated Discussion Boards


Live Online Teaching

  • Supervised Learning
  • Module 1: Regression
    Maximum Likelihood, Least Squares, Regularization
  • Module 2: Bayesian Methods
    Bayes Rule, MAP Inference, Active Learning
  • Module 3: Foundational Classification Algorithms
    Nearest Neighbors, Perceptron, Logistic Regression
  • Module 4: Refinements to Classification
    Kernel Methods, Gaussian Process
  • Module 5: Intermediate Classification Algorithms
    SVM, Trees, Forests and Boosting
  • Unsupervised Learning
  • Module 6: Clustering Methods
    K-Means Clustering, E-M, Gaussian Mixtures
  • Module 7: Recommendation Systems
    Collaborative Filtering, Topic Modeling, PCA
  • Module 8: Sequential Data Models
    Markov and Hidden Markov Models, Kalman Filters
  • Module 9: Association Analysis
  • Module 10: Model Selection
    Model Comparisons, Analysis Considerations

● 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.

All assignments and application projects will be done using the Python programming language.


Application Assignments

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John W. Paisley
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.

Certificate of Completion


Certificate of Completion

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

Get Certified
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