Free Online Course on Machine Learning for Data Science and Analytics

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The University of Columbia is offering free online course on Machine Learning for Data Science and Analytics. This data science course is an introduction to machine learning and algorithms.

The overall objective of this course is to learn the principles of machine learning and the importance of algorithms.

Course At A Glance 

Length: 5 weeks
Effort: 7-10 hours pw
Subject: Machine Learning for Data Science and Analytics
Institution: Columbia University and edx
Languages: English
Price: Free
Certificate Available: Yes, Add a Verified Certificate for $99
Session: Self-Paced

Providers’ Details

Columbia University is one of the world’s most important centers of research and at the same time a distinctive and distinguished learning environment for undergraduates and graduate students in many scholarly and professional fields. The University recognizes the importance of its location in New York City and seeks to link its research and teaching to the vast resources of a great metropolis.

About This Course

Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications.

This data science course is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. We will also examine why algorithms play an essential role in Big Data analysis.

Why Take This Course?

This is the second course in the three-part Data Science and Analytics XSeries.

Learning Outcomes

  • What machine learning is and how it is related to statistics and data analysis
  • How machine learning uses computer algorithms to search for patterns in data
  • How to use data patterns to make decisions and predictions with real-world examples from healthcare involving genomics and preterm birth
  • How to uncover hidden themes in large collections of documents using topic modeling
  • How to prepare data, deal with missing data and create custom data analysis solutions for different industries
  • Basic and frequently used algorithmic techniques including sorting, searching, greedy algorithms and dynamic programming

Instructors

Professor Ansaf Salleb-Aouissi

Ansaf is a Lecturer in discipline of the Computer Science Department at the School of Engineering and Applied Science at Columbia University.

Cliff Stein

His research interests include the design and analysis of algorithms, combinatorial optimization, operations research, network algorithms, scheduling, algorithm engineering and computational biology.

David Blei

David Blei joined Columbia in Fall 2014 as a Professor of Computer Science and Statistics. His research involves probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference. He works on a variety of applications, including text, images, music, social networks, user behavior, and scientific data.

Itslk Peer

Itsik Pe’er is an associate professor in the Department of Computer Science.

Mihalis Yannakakis

He studied at the National Technical University of Athens (Diploma in Electrical Engineering, 1975), and at Princeton University (PhD in Computer Science, 1979).

Peter Orbanz

His main research interests are the statistics of discrete objects and structures: permutations, graphs, partitions, and binary sequences.

Requirements

High School Math. Some exposure to computer programming.

How To Join This Course

  • Go to the course website link
  • Create an edX account to SignUp
  • Choose “Register Now” to get started.
  • EdX offers honor code certificates of achievement, verified certificates of achievement, and XSeries certificates of achievement. Currently, verified certificates are only available in some courses.
  • Once applicant sign up for a course and activate their account, click on the Log In button on the edx.org homepage and type in their email address and edX password. This will take them to the dashboard, with access to each of their active courses. (Before a course begins, it will be listed on their dashboard but will not yet have a “view course” option.)

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