Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Online Course

CS190.1x: Scalable Machine Learning

University of California, Berkeley via edX

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

Tags

Related Courses

Reviews

4.5 rating, based on 31 reviews

Start your review of CS190.1x: Scalable Machine Learning

  • 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...
  • 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...
  • Anonymous

    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.
  • 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.
  • Maurits D.

    Maurits is taking this course right now.

  • Igor is taking this course right now.

  • Tabish is taking this course right now.

  • Vlad completed this course, spending 6 hours a week on it and found the course difficulty to be medium.

  • Anonymous

    Anonymous completed this course.

  • Lace is taking this course right now.

  • C C.

    C completed this course.

  • Prakhar completed this course.

  • Dmitry completed this course.

  • Liang is taking this course right now.

  • Sergiy completed this course.

  • V completed this course.

  • Shuang W.

    Shuang completed this course.

  • Peter completed this course.

  • Rogier W.

    Rogier completed this course.

  • Sauro completed this course.

Class Central

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free

Never stop learning Never Stop Learning!

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free