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University of California, San Diego

Machine Learning With Big Data

University of California, San Diego via Coursera


Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.

At the end of the course, you will be able to:
• Design an approach to leverage data using the steps in the machine learning process.
• Apply machine learning techniques to explore and prepare data for modeling.
• Identify the type of machine learning problem in order to apply the appropriate set of techniques.
• Construct models that learn from data using widely available open source tools.
• Analyze big data problems using scalable machine learning algorithms on Spark.

Software Requirements:
Cloudera VM, KNIME, Spark


  • Welcome
  • Introduction to Machine Learning with Big Data
  • Data Exploration
  • Data Preparation
  • Classification
  • Evaluation of Machine Learning Models
  • Regression, Cluster Analysis, and Association Analysis

Taught by

Paul Rodriguez, Natasha Balac and Andrea Zonca


1.9 rating, based on 14 Class Central reviews

4.6 rating at Coursera based on 2441 ratings

Start your review of Machine Learning With Big Data

  • Anonymous
    Spent 1 hour on the entire course. No practical assignments whatsoever. No new skills learnt. Lecturers read information which are freely available on wikipedia without explaining in-depth anything.
    Quizzes are extremely lazy, in the form of true-false format. Some questions even repeat. Extremely easy learning experience which is not at all worth the time.
  • too easy too short if you want ML stuff you have to look elsewhere if you very new to ML you will find this has something to learn but it's not informative enough Andrews ng's machine learning course is recommended for anyone who wants to know ma…
  • Anonymous
    This course makes a joke out of its topic name. Some examples:

    --Quizzes are too trivial. Some even repeat questions. Some questions are wrong.

    --Videos are too much ridden with facts all over the place, yet offer little evaluation and explanation. For example, why do we use this tool, what purpose is this concept good for, ...

    --Hands-on assignments either lack purpose and meaning or the designer never explains those.

    I hope other learners who share similar experience with me will take their time and write feedbacks in Coursera itself so this specialization can be improved.
  • Anonymous
    took me about 2 hours to complete the whole course, content is so general and high level that there is basically no added value. nothing is explained in depth, no examples are given, assignments do not force real understanding of material
  • Anonymous
    It looks like a course that can be found on udemy.
    The coolest courses can be found at Datacamp. It was the most productive and fast training I've seen. From the latter, it was interesting to take a few practical courses at - there were several interesting practical cases.
    On Udemy you can also find inexpensive but on average the same quality as on the Coursera course.
  • Stephane Mysona

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