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ARTIFICIAL INTELLIGENCE
COURSE DESCRIPTION
Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.
WHAT WILL YOU LEARN?
- Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
- Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbours, K-means clustering and more.
- Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning.
- Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering, and recommendation system
PRE-REQUISITES
Some of the topics in Introduction to Artificial Intelligence will build on probability theory and linear algebra. You should have understanding of probability theory comparable to that covered in our Intro to Statistics course.
COURSE OUTLINE
- Introduction to Artificial Intelligence
- Getting started with Fuzzy Logic
- Machine Learning
- Artificial Neural Network (ANN)
- Support Vector Machine
For FULL COURSE OUTLINE, please contact us.
Please contact us for the schedules and for booking a private class.