Columbia University


4950 ₺ + VAT

12 Weeks

Pearson Turkey

8-12 Hours per week

About This Course

Ever wondered about how the news trending topics are selected, or how the recommendation engines are built? Have you ever thought about how the paths of movie zombies are computed? All of these are the applications of machine learning, an exciting field, which serves as a base for the most sought-after careers in data analysis.

Would you like to know more about how to guide machine learning? Then this interactive and engaging course is definitely for you! Take part in this MicroMasters Program from Columbia University and achieve a strong, graduate-level foundation in AI that would take you closer to your dream job that is already highly demanded in the market.

This course entails several exercises based on Python programming language and elements of probability, for which we provide instructional support during our in-class sessions.

What You Will Learn

You will learn about regression, classification, clustering methods, sequential models, matrix factorization, topic modeling and model selection. All these exciting topics will be delivered in both a supervised learning environment, and through an individual study time. This content will help you gain confidence to apply the concepts in your work field.

The interactive in-class sessions are going to keep you engaged and will challenge you to think about the controversial topics on the course through peer debates and discussions.

You will be inspired by the most prominent regional success stories and receive a hands-on experience through several exercises prepared by our hand-picked facilitators.


Course Syllabus

This engaging and rigorous Machine Learning course from Columbia University will cover the models and methods of data analysis. You will learn about the following:

Week 1: Maximum likelihood estimation, linear regression, least squares

Week 2: Ridge regression, bias-variance, Bayes rule, maximum a posteriori inference

Week 3: Bayesian linear regression, sparsity, subset selection for linear regression

Week 4: Nearest neighbor classification, Bayes classifiers, linear classifiers, perceptron

Week 5: Logistic regression, Laplace approximation, kernel methods, Gaussian processes

Week 6: Maximum margin, support vector machines, trees, random forests, boosting

Week 7: Clustering, k-means, EM algorithm, missing data

Week 8: Mixtures of Gaussians, matrix factorization

Week 9: Non-negative matrix factorization, latent factor models, PCA and variations

Week 10: Markov models, hidden Markov models

Week 11: Continuous state-space models, association analysis

Week 12: Model selection, next steps

Job Outlook

  • Though Artificial Intelligence is one of the fastest-growing areas for high-tech professionals, there are too few qualified engineers, according to a recent Kiplinger report.
  • Robotics and artificial intelligence will impact wide segments of daily life by 2025, with huge implications for a range of industries such as health care, transport and logistics, customer service, and home maintenance. (Pew Internet)
  • The need for AI specialists exists in just about every field as companies seek to give computers the ability to think, learn, and adapt. (IEEE)
  • Exciting and rewarding career opportunities as a Machine Learning Software Engineer, Deep Learning Specialist, Data Scientist, Automation Engineer, 3D Artist, Computer Vision Engineer, and many more!

Who Will Support


Professor at the Department of Electrical Engineering in Columbia University.