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IBM Online Courses
Spark MLliB

Spark MLliB

Build sought-after skills in Spark MLlib. Learn about its data types, algorithms, and parameters. Explore vectors, matrices, clustering, decision trees, and more. Plus, get hands-on experience through online labs.

Discover how to make machine learning scalable and easy and boost your data science career.

Spark MLliB Highlights

  Course duration

Duration

  • 1 week, online
    4-5 hours/week
   Course Fee

Fee

US$ 99 - US$ 199
Course duration

Duration

  • 1 week, online
    4-5 hours/week
Course Fee

Fee

US$ 99 - US$ 199

Spark MLlib enables data scientists to focus on their data problems and models instead of solving the complexities surrounding distributed data, such as infrastructure and configuration. Put simply, its goal is to make practical machine learning scalable and easy.

During this course, you will learn about the data types in Spark MLlib, including local vectors, labeled points, local matrices, and distributed matrices and their types. You will be introduced to the algorithms available with Spark MLlib, including clustering algorithms, decision trees, and random forest splitting. You will learn in detail about splitting features of decision trees and random forests. You will investigate the parameters - specifiable and stopping - required to create decision trees, plus you will explore k-means and Gaussian Mixture clustering. You will also have opportunities to enhance your understanding by working on various hands-on-labs on these topics.

Once you have completed this course, you will have the skills to make machine learning scalable and easy through algorithms, featurization, pipelines, persistence, and utilities. Thus, Spark MLlib provides essential learning for data scientists, machine learning engineers, and others working in this field seeking to boost their career.

This IBM certified course comprises five purposely designed modules that take you on a carefully defined learning journey.

It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules or submitting assignments. It is anticipated that it takes about 4-5 hours to complete. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And don’t worry, you’re not alone! You will be encouraged to stay connected with your learning community and mentors through the course discussion space.

The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include videos, reading material, and online exam questions.

As part of our mentoring service you will have access to valuable guidance and support throughout the course. We provide a dedicated discussion space where you can ask questions, chat with your peers, and resolve issues. Depending on the payment plan you have chosen, you may also have access to live classes and webinars, which are an excellent opportunity to discuss problems with your mentor and ask questions. Mentoring services will vary across packages.

Once you have successfully completed the course, you will earn your IBM Certificate.

Once you have successfully completed this course, you will understand:

  • Supervised learning
  • Unsupervised learning Reinforcement learning
  • ML algorithms
  • Basic statistics
  • Classification
  • Dimensionality reduction
  • Individuals seeking to learn about machine learning libraries with Spark.
  • Data scientists, machine learning engineers, and statisticians looking to improve their efficiency through tailored tools.

No experience required.

Course Outline

Course Certificate

Earn your certificate

Once you have completed this course, you will earn your certificate.

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Spark MLliB

Course details

Course Certificate

Certificate

  • IBM Certificate
About Course

About this course

  • 05 Modules
  • 03 Skills
Course Includes

Includes

  • Discussion space
  • 04 Labs
  • 04 Quiz
  • 01 Final exam
Course Learning

Create

  • Text analytics
  • Datatypes
  • Clustering
  • Decision trees and random forests
Course Exercises

Exercises to explore

  • Review of algorithms
  • Linear regression and classification
  • Introduction to Spark MLlib with Python
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Daniel Tran

FAQs

Constructed on top of a distributed computing platform called Spark, the MLlib machine-learning library provides a collection of common learning algorithms and utilities such as classification and regression, as well as clustering and collaborative filtering. It also includes dimensionality reduction and underlying optimization primitives.  

The most significant distinction between the two is the data types they offer, with MLlib supporting RDDs and ML supporting DataFrames and Datasets, respectively. 

Yes, the data types used by Spark MLlib will be explored in detail throughout the first module of this course. You will learn about local matrices, row indexed rows, coordinate distributed matrices, and block matrices.

This course is self-paced, which means that you can work at a pace that suits you. It does not follow a predetermined timetable, unlike scheduled live sessions. You are free to work at your own speed if you complete the modules and the course before the deadline.  You will also have access to the course discussion space where you can ask questions and discuss topics with fellow learners. 

Data analysts, application developers, and data engineers are the good candidates for this course. 

Yes, you will be issued an IBM Certificate once you have successfully completed this Spark MLlib course. You can also add this certificate to your LinkedIn profile and your resumé to demonstrate your knowledge to prospective employers. 

Yes. To earn your IBM Certificate, you must complete all knowledge checks and the final exam with an average score of 70%. 

Yes. Spark MLlib is 100% online. All you need is a good connection to the internet to access the course materials. 

As soon as you enroll in the Spark MLlib course, you will be able to access the course materials through your dashboard. The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include videos, reading material, and online exams questions.