Machine Learning for Data Science and Analytics
date_range Starts on April 15, 2016
event_note End date April 15, 2017
list 5 sequences
assignment Level : Introductive
chat_bubble_outline Language : English
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About the content

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.

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

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Syllabus

  • 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 
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Intructors

  • Ansaf Salleb-Aouissi
  • Cliff Stein
  • David Blei
  • Itsik Peer
  • Mihalis Yannakakis
  • Peter Orbanz
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Columbia University
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