This course is intended for data scientists and software engineers. It maintains an optimal balance of theory and practice. For each machine learning concept, we first discuss the foundations, its applicability and limitations. Then we explain the implementation and use, and specific use cases. This is achieved through a combination of about 50% lecture, 50% lab work. Amazon SageMaker is a fully managed machine learning service. The course combines overview and understanding of Machine Learning concepts with specific implementation in SageMaker. In addition, it brings in other tools outside of SageMaker when required. Machine Learning (ML) is the killer app for Big Data. Amazon Machine Learning brings the power of ML to a regular programmer and provides ML as a service. However, to use ML effectively, one needs to understand the models used and how to utilize them on Amazon.
- Familiarity with programming in at least one language
- Be able to navigate Linux command line
- Basic knowledge of command line Linux editors (VI / nano)•basic familiarity with AWS (optionally may be Provided in the first day on the course
3 Days/Lecture & Lab
This course is designed or Data Scientists and Software Engineers.
- Introductions and overviews
- Supervised Learning
- Unsupervised learning
- Data visualization