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Columbia University

Machine Learning for Data Science and Analytics

Columbia University via edX

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This course will soon be retired. Last day to enroll is July 31st, 2023 at 00:00 UTC.

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.

Taught by

Ansaf Salleb-Aouissi, Cliff Stein, David Blei, Itsik Peer, Mihalis Yannakakis and Peter Orbanz

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Reviews

3.1 rating, based on 15 Class Central reviews

Start your review of Machine Learning for Data Science and Analytics

  • Anonymous
    DO NOT PAY FOR THIS COURSE!!! YOU WILL GET RIPPED OFF!!! 1) The series is basically an informal presentation of topics in statistics and machine learning. THEY DON'T ACTUALLY TEACH, but rather send you a links of a whole lot of articles and paper…
  • Ericdo1810
    This course is not about Machine Learning It is about algorithms, which is nice, they'll familiarise you (by their lectures) with all the most common types of algorithms. Now, the next question is, do you get to implement the algorithms? THE ANSWER…
  • Shoaib
    The course sure covers a wide range of topics but the the content is interesting and intriguing. It does help in learning lots of tools to be able to explore further. Although the proportion of algorithms is bit bigger than expected but the course does provide a good overview of the field. A nice learning experience overall. Can help to better understand the other machine learning and data science courses.
  • Anonymous
    I mostly enjoyed the experience as I found the course interesting. The content is so rich. Of course you can expect some differences from a speaker to another one given the large number of speakers and the many covered topics, however, despite this, this course has worked for me. It helped me understand ML little by little resolving lots of confusions about how ml and stats work together which were not removed in may other courses that I took online. It gives the basic ideas of important concepts of data science, algorithms and machine learning that I didn’t find elsewhere. It is of course not realistic to assume that it will make you a data scientist over night but it is a good platform to start with.
  • Allison Cooper
    The lectures are nice and the material is very interesting, but they assume you are already familiar with the material. There are not very many questions, and they're either very simplistic and not really useful or overly hard (especially if you have had no exposure to the material). It would be nice to have better exercises that help students to understand the lecture material. In this run of the course there were a number of errors in the wording of the questions. The discussion board is hardly monitored by staff, and questions and concerns are not dealt with. It is hard to understand who this course will help.
  • Ava
    There's no clear objective for this course and its syllabus ran like an extended introduction to everything and anything we can think off. The treatment of subjects being covered range from elementary (linear programming) or to downright abysmal for NP-completeness.

    Unless you have plenty of time and money to burn - no recommended at all.
  • Anonymous
    DS102X
    Quizz wording is confusing.
    You only have one chance to get the answer right. after a mediocre explanation.
    Sometimes answers are wrong.

    Take it to learn but dont pay for it. Not worth it.
  • Anonymous
    Many Machine Learning courses neglect explaining the various more basic and general algorithms used when creating ML ones. A very well explained class, especially chapters 4 and 5.
  • Dissipate
    Like an earlier reviewer said, it is unclear who this course is meant to be for, and the outcome they aim to help learners achieve. Week 1 consists general information and background on, and the future of, data science. Weeks 2 and 3 breeze throug…
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