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.
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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
Certificate Available: Yes, Add a Verified Certificate for $99
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.
- 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
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.
His research interests include the design and analysis of algorithms, combinatorial optimization, operations research, network algorithms, scheduling, algorithm engineering and computational biology.
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.
Itsik Pe’er is an associate professor in the Department of Computer Science.
He studied at the National Technical University of Athens (Diploma in Electrical Engineering, 1975), and at Princeton University (PhD in Computer Science, 1979).
His main research interests are the statistics of discrete objects and structures: permutations, graphs, partitions, and binary sequences.
High School Math. Some exposure to computer programming.
How To Join This Course
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