The lessons in this course will give you insider information on many topics that are often overlooked. What kind of data is good quality? How much data is enough for machine learning? What is a hybrid solution and when do you need one? How do I know if I have enough labels or the right kind of labels? And where do I get the labels?
After each lesson, you should know enough to dive deeper into the topic on your own and start implementing the knowledge in your solutions.
Who is this course for?
Anyone that has basic experience working with data and developing software solutions, or has some experience applying ML approaches will benefit from these lessons.
About the instructors
Dr. Orna Raz is a researcher at IBM Research, Haifa. Her research combines machine learning with software engineering. She is especially interested in making machine learning solutions more predictable and trustworthy. Orna holds a Ph.D degree in Software Engineering and computer science from Carnegie Mellon University, Pittsburgh, PA (2004) and a B.Sc degree in Computer Science from the Technion, Israel Institute of Technology (1996). Orna has worked in areas of fault tolerance, developer tools, and software quality, with an emphasis on data analytics. She has previously worked at Intel R&D labs in Haifa, ChipExpress, and NASA Ames.
Dr. Eitan Farchi is a Distinguished Engineer at IBM Research. His work at the IBM Research lab in Haifa currently focuses on the reliability of systems and machine-learning based systems. With 30 years of experience in the development and verification of business critical systems, Eitan has published numerous papers and patents in the area. He received his PhD form Haifa University and his early research was in Game Theory. When he's not ensuring the reliability of various critical systems, Eitan is passionate about spending time with family and martial arts.