Spark MLliB
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Course
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.
Self-Paced
Mentored
Beginner
Duration
4-5 hours/week
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 dont worry, youre 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.
- Individuals seeking to learn about machine learning libraries with Spark.
No experience required.