Machine Learning for Big Data and Text Processing: Foundations
Machine learning is a rapidly expanding area with a diverse collection of tools and approaches. Successfully applying such methods to real tasks may seem to require expertise that many do not possess. However, all these methods share the same basic concepts, use the same building blocks.
Understanding these basics, formulations, and when they are appropriate, is key to using machine learning techniques successfully in practice. This foundational course covers the essential concepts and methods in machine learning, providing participants with an entry level expertise they need to get started and quickly move ahead.
This course was previously titled Machine Learning for Big Data and Text Analysis.
- Understand the basic machine learning concepts and methods including neural networks
- Learn how to formulate/set up problems as machine learning tasks
- Assess which types of methods are likely to be useful for a given class of problems
- Understand strengths and weakness of learning algorithms
Who should attend
This course is appropriate to obtain a better understanding of machine learning basics. It is most suitable for those with an undergraduate degree in computer science or other related technical areas. A high-level understanding of programming (thinking in terms of programs) is helpful.
The foundational course describes key concepts, formulations, algorithms, and practical knowledge for people who are getting started or need to brush up in machine learning, and provides participants with core knowledge to succeed in the advanced level course.