Machine learning enables computers to learn from large amounts of data without being explicitly programmed to do so. We can already see how machine learning gives rise to new intelligent applications, from self-driving cars to intelligent assistants on our smartphones.
Increasingly, businesses recognize the importance of using machine learning to transform their data assets into business value. However, many companies are unsure how machine learning can be applied to solve problems in an enterprise context. As the world’s most relevant enterprise data is part of SAP’s system and business network, SAP aspires to make all its enterprise solutions intelligent and help customers to leverage their data.
The objective of this course is to help business decision makers understand the significance of machine learning for enterprise computing. After taking this course, participants should be aware of recent advances in machine learning, have an understanding of the basic concepts involved and how business problems can be solved with machine learning. In particular, the course gives guidelines on how to formulate a business problem as a machine learning problem. This course is mainly aimed at a business audience. In the meantime, we are working on another machine learning course for a developer audience, and expect this course to be available next year.
Here is what some participants are saying about the course:
"I loved this course because of following reasons:
a) It gave an overview of what machine learning is. b) It explained concept of machine learning in simple language c) The examples used were easy to relate and understand d) It also explained how ML can be used in Enterprise space." read the original post
" I am relatively new to the concepts of ML and I enjoyed this course as a good overview of ML." read the original post
• Starting from: November 14, 2016, 09:00 UTC. (What does this mean?)
• Duration: The course is open for 5 weeks
• Effort: 2-3 hours in total
• Course assignment: You can take the course assignment at any time whilst the course is open.
• Course closure: December 20, 2016, 09:00 UTC
• Course language: English
• How is an openSAP course structured?
Unit 1: Intelligent Applications Powered by Machine Learning
Unit 2: What Is Machine Learning?
Unit 3: From Business Problem to Machine Learning: A Recipe
Unit 4: Machine Learning in Enterprise Computing
Unit 5: Application Example: Natural Language Processing
Unit 6: Application Example: Computer Vision
Unit 7: Key Takeaways
• Solution managers
• Executive managers
This course was held from Nov 14, 2016 through Dec 20, 2016.
5374 learners were enrolled on day 1 of the course.
When the final exam ended, this number had increased to 8774.
21347 learners enrolled.
This course was rated with 4.24 stars in average from 2764 votes.
Find out more in the certificate guidelines.
Dr. Daniel Dahlmeier is a development manager at the SAP Innovation Center Network and head of Machine Learning for Sales and Service at SAP. Daniel leads teams building machine learning services for SAP’s Customer Engagement and Commerce applications.
Before joining SAP, Daniel was doing research in natural language processing at the National University of Singapore. He enjoys building state-of-the-art technology to solve real-world problems.
Dr. Markus Noga is head of SAP Leonardo Machine Learning, where he leads teams building machine learning, conversational AI, intelligent robotic process automation, and data intelligence capabilities for the SAP portfolio of products.
Prior to this, Markus was VP for New Business & Portfolio, where he drove the launch of SAP HANA Enterprise Cloud and the continuous renewal of SAP’s global R&D portfolio. He has also worked as a director in the Corporate Strategy Group.
Before joining SAP, Markus was a principal with management consultancy Booz & Company. He holds a PhD in Computer Science from University of Karlsruhe, where he focused on the optimization of document processing.