Big Data University

Spark MLlIB

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  • Course Number
  • Classes Start
    Any time, Self-paced
  • Estimated Effort
    3 hours
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Spark provides a machine learning library known as MLlib. Spark MLlib provides various machine learning algorithms such as classification, regression, clustering, and collaborative filtering. It also provides tools such as featurization, pipelines, persistence, and utilities for handling linear algebra operations, statistics and data handling. This course will start you off on your journey and walk you through some of the machine learning libraries and how to use them. 


  • Module 1 - Spark MLlib Datatypes
    1. Understand the difference between Dense and Sparse Data Types, and how they apply to LabeledPoints and matrices.

    2. Understand how to create and use the different matrices that are available in Spark MLlib.

  • Module 2 - Review of Algorithms
    1. Have a general understanding of each of the algorithm that will be discussed in the course and how they work.

    2. Learn how to instantiate simple Linear Regression and Classification models, including Linear Regression, Support Vector Machines, and Logistic Regression.

  • Module 3 - Spark MLlib Decision Trees and Random Forests
    1. Learn about the different input parameters used to create Decision Trees and Random Forests. 

    2. Understand the effects of tuning specific parameters for Decision Trees and Random Forests. 

  • Module 4 - Spark MLlib Clustering
    1. Learn about the parameters involved in creating K-Means Clustering models and Gaussian Mixture Clustering models.

    2. Describe how outputs and uses of the functions available to each clustering model.


  • This course is free.
  • It is self-paced.
  • It can be taken at any time.
  • It can be audited as many times as you wish.


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Daniel Tran, Spark MLlib Course Instructor

Daniel Tran

Daniel Tran is an IBM Co-op Student working as a Technical Curriculum Developer in Toronto, Ontario. He develops courses to improve the education of customers who seek knowledge in the Big Data field. He has also reworked previously developed courses, updating them to be compatible with the newest software releases, as well as work at the forefront of recreating courses on a newly developed cloud environment. He has worked with various components that deal with Big Data, including Hadoop, Pig, Hive, HBase, MapReduce & YARN, Sqoop, Oozie, and Phoenix. He has also worked on separate courses involving Machine Learning. Daniel is from the University of Alberta, where he has completed his third year of traditional Computer Engineering Co-op.