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CS190.1x: Scalable Machine Learning

University of California, Berkeley via edX

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Overview

Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability and optimization. Learning algorithms enable a wide range of applications, from everyday tasks such as product recommendations and spam filtering to bleeding edge applications like self-driving cars and personalized medicine. In the age of ‘Big Data,’ with datasets rapidly growing in size and complexity and cloud computing becoming more pervasive, machine learning techniques are fast becoming a core component of large-scale data processing pipelines.
 
This course introduces the underlying statistical and algorithmic principles required to develop scalable real-world machine learning pipelines. We present an integrated view of data processing by highlighting the various components of these pipelines, including exploratory data analysis, feature extraction, supervised learning, and model evaluation. You will gain hands-on experience applying these principles using Apache Spark, a cluster computing system well-suited for large-scale machine learning tasks. You will implement scalable algorithms for fundamental statistical models (linear regression, logistic regression, matrix factorization, principal component analysis) while tackling key problems from domains such as online advertising and cognitive neuroscience.
 
This self-assessment document provides a short quiz, as well as online resources that review the relevant background material. 

Taught by

Ameet Talwalkar

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Reviews for edX's CS190.1x: Scalable Machine Learning
4.5 Based on 31 reviews

  • 5 stars 61%
  • 4 stars 32%
  • 3 stars 6%
  • 2 star 0%
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Gregory S
4.0 3 years ago
by Gregory completed this course and found the course difficulty to be medium.


Scalable Machine Learning is a 5-week distributed machine learning course offered by UC Berkeley through the edX platform. It is a follow up to another UC Berkely course: Introduction to Big Data with Apache Spark. Although the first course is not a strict perquisite, Salable Machine Learning uses the same virtual machine and even has some overlap with the homework labs, so it is beneficial to take Introduction to Big Data first. Scalable Machine Learning teaches distributed machine learning basics using Pyspark, Apache Spark’s Python API. Basic proficiency with Python is necessa…
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Martin S
4.0 3 years ago
by Martin completed this course, spending 4 hours a week on it and found the course difficulty to be medium.
Overall a good course, that is worthwhile spending the time on, if you want to get a basic introduction to solving machine learning problems using Apache Spark.

As with the precursor, CS100.1x, the lecture videos and quizzes are pretty light on actual content and nothing spectacular. However, as with the precursor I found the assignments really well structured, interesting, and informative. They use IPython notebook which I found to be a really awesome format for this kind of course and assignments.

The course is not heavy on the mathematics of machine learning algori…
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Gaurabh G
4.0 3 years ago
by Gaurabh completed this course, spending 5 hours a week on it and found the course difficulty to be medium.
Very well explained machine learning using Spark from scratch. Therefore a good introductory course. Not too many details covered, probably due to time limitation. Hope they make a sequel.
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Anonymous
5.0 3 years ago
Anonymous is taking this course right now.
The machine learning algorithms are explained in reasonably granular level, and easy to follow. The labs are the highlight. I learnt a lot from doing. Thanks for putting this course together.
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