Machine Learning with Apache SystemML
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Course
Machine Learning with Apache SystemML
Learn to use Apache SystemML, a scalable language for large-scale machine learning. Master automatic optimization across Hadoop, Spark, and single-node systems with hands-on examples.
Self-Paced
Duration
Apache SystemML is a declarative language built for scalable machine learning. It automatically generates optimized runtime plans for distributed and in-memory computations across Hadoop and Spark.
In this course, youll explore SystemMLs core architecture, syntax, and optimizers. Youll gain practical experience expressing machine learning algorithms in R-like or Python-like syntax using linear algebra, statistics, and ML constructs.
The course helps data scientists and engineers write scalable analytics workflows efficiently while relying on SystemMLs automatic optimization. Hands-on examples demonstrate how to leverage MLContext, DML, and BigSheets for real-world applications.
This 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. It is anticipated that you will complete the course in 3 hours. 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 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 discussion space, videos, reading material, quizzes, hands-on lab, and final assignment.
Once you have successfully completed the course, you will earn your IBM Certificate.
You will be able to:
Course Outline
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FAQs
This course introduces Apache SystemML for machine learning, a declarative style language designed for large-scale computations. Youll learn how SystemML automatically generates optimized runtime plans for single-node, in-memory, and distributed environments on Hadoop and Spark.
The Apache SystemML course is ideal for data scientists, engineers, and machine learning practitioners who want to enhance productivity by expressing custom analytics in R-like or Python-like syntax while letting the optimization engine handle scalability.
A basic understanding of Apache Hadoop and Big Data concepts is recommended but not strictly required. The Apache SystemML tutorial included in this training course provides enough context to get started, even if youre new to distributed frameworks.
Unlike traditional ML frameworks, Apache SystemML for machine learning uses a declarative approach. You write algorithms in simple syntax, and SystemMLs optimizer automatically determines whether execution should happen on one machine or scale out to a cluster.
Yes. Youll learn about SystemMLs declarative machine learning (DML) language, its built-in functions, and how to express ML algorithms using its linear algebra primitives and statistical operations.
Yes. The course explains how to run SystemML programs in distributed environments using Spark MLContext and Hadoop, providing practical exposure to Apache machine learning in big data ecosystems.
Youll explore how MLContext allows seamless integration of SystemML with Spark. The course demonstrates how to execute DML scripts within Scala workflows for real-world machine learning tasks.
Youll work with SystemML algorithms that cover areas such as classification, regression, clustering, and statistical analysis, while understanding how they scale from single-node to distributed modes.
SystemML includes an optimizer stack that automatically decides whether to run a task on one machine, in memory, or across a distributed cluster, ensuring efficient execution regardless of dataset size.
Yes. The Apache SystemML Course includes practical labs where youll test runtime plans, optimizers, and experience first-hand how SystemML outperforms single-node R with large datasets.
SystemML algorithms can be expressed in R-like and Python-like syntax, making it beginner-friendly while offering superior scalability and automatic optimization compared to standalone R or Python implementations.
Yes. Through practical examples, youll see how Apache SystemML for machine learning is applied to distributed analytics in industries that need scalable solutions, such as finance, healthcare, and retail.
Yes. Youll receive an IBM Certificate upon successful completion of the course, validating your ability to apply SystemML for distributed ML and big data projects.
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