Machine Learning with Apache SystemML
Build valuable skills for working with big data. Discover the strengths of Apache SystemML and learn how to create a scalable, extensible machine learning framework that will create machine learning algorithms. Plus, get hands on practice through online labs.
Build in-demand skills for the fast-growing field of big data and boost your career.
Earn your certificate
Once you have completed this course, you will earn your certificate.Preview digital certificate
- IBM Certificate
About this course
- 05 Modules
- 07 Skills
- Discussion space
- 04 Hands-on labs
- 17 Videos
- 05 Review questions
- 01 Final exam
- Recommendation system using PySpark
- A string that represents the content of DML or PyDML scripts
Exercises to explore
- Linear regression using SystemML and Spark MLContext
- Getting started with SystemML
- Flight delay prediction demo using SystemML
- Declaritive machine learning
Want to know more?Get a Free Consultation
You'll learn with these experts
Designed for large-scale machine learning, Apache SystemML is a declarative style language with a declarative syntax. It allows for the automatic creation of efficient runtime plans for a variety of workloads, including single-node, in-memory, and distributed computations on Apache Hadoop and Apache Spark. Syntaxes for SystemML algorithms are reminiscent of R or Python and include elements such as arithmetic operations, statistical functions, and ML-specific features.
SystemML is a high-level programming language that allows you to quickly implement and run machine learning algorithms on Spark in minutes. SystemML's cost-based optimizer takes care of the low-level decisions regarding employing Spark's parallelism, allowing users to concentrate on the algorithm and the real-world problem that the algorithm is attempting to solve instead of on the optimizations.
The material for each module is 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.
You do not need prior technical experience before taking this course. This course is suitable for learners with both technical and non-technical backgrounds.
Yes, this is a self-paced course. This means you can organize your study time according to a timetable that suits you. As long as you finish the course before the deadline, you can work through the course at a pace that works with your style of learning.
When you opt for a self-paced course, it 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.
There is only one final exam in this course.
Yes, you will be issued an IBM Certificate when you successfully complete the course. You can then upload it on your LinkedIn profile.
This course is divided into 5 modules, each of which covers a different aspect of SystemML, such as its algorithms, architecture, and optimization.