Machine Learning with Apache SystemML
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Course
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.
Self-Paced
Mentored
BEGINNER
Duration
3-4 hours/week
Fee
Apache SystemML is an important machine learning platform that offers the valuable strengths of scalability and flexibility to data engineers and data scientists working with big data. It customizes algorithms with the help of R-like and Python-like programming languages and does optimization automatically based on the characteristics of both the data and cluster. As a tool, therefore, it offers unique characteristics to those working in the field of big data, including algorithm customization, multiple execution modes, and automatic optimization.
During this course, you will learn how the optimizers function is handled by Apache SystemML. You will discover how to create a commercial friendly, scalable, and extensible machine learning framework that will create or extend machine learning algorithms using SystemML. Plus, you will get hands-on practice using examples of ML algorithms and investigate how to run them.
For data scientists and data engineers keen to build their competencies, this Machine Learning with Apache SystemML course will give you valuable skills to boost your 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. To give you an idea of how long the course takes to complete, it is anticipated that if you work 3-4 hours per week, you will complete the course in 2 weeks. 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.
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Course Outline
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We believe every learner is an individual and every course is an opportunity to build job-ready skills. Through our human-centered approach to learning, we will empower you to fulfil your professional and personal goals and enjoy career success.
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Personalized Mentoring & Support
1-on-1 mentoring, live classes, webinars, weekly feedback, peer discussion, and much more.
Practical Experience
Hands-on labs and projects tackling real-world challenges. Great for your resumé and LinkedIn profile.
Best-in-Class Course Content
Designed by the industry for the industry so you can build job-ready skills.
Job-Ready Skills Focus
Competency building and global certifications employers are actively looking for.
Course Offering
Type of certificate
IBM Certificate
About this course
05 Modules
07 Skills
Includes
Discussion space
04 Hands-on labs
17 Videos
05 Review questions
01 Final exam
Create
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
This course has been created by
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FAQs
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.
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.
Course Offering
Type of certificate
IBM Certificate
About this course
05 Modules
07 Skills
Includes
Discussion space
04 Hands-on labs
17 Videos
05 Review questions
01 Final exam
Create
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
This course has been created by
Newsletters & Updates
Subscribe to get the latest tech career trends, guidance, and tips in your inbox.