Course Overview:

This course includes decision tree learning, artificial neural networks, Bayesian networks, instance-based learning, analytic learning, reinforcement, learning, genetic programming, deep learning, etc. This class also uses real life example applications on machine learning and methods for designing systems that iteratively learn from data and improve qualitatively with cumulative experience.

About this course:

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. This area is also concerned with issues both theoretical and practical. In this course, we will present algorithms and approaches in such a way that grounds them in larger systems as you learn about a variety of topics, including:

• Statistical Supervised and Unsupervised learning methods
• Randomized Search Algorithms
• Bayesian learning methods
• Reinforcement Learning

By the end of this course, you should have a strong understanding of machine learning so that you can pursue any further and more advanced learning.

• Reviewing the types of problems that can be solved
• Understanding building blocks
• Learning the fundamentals of building models in machine learning
• Exploring key algorithms
• Supervised learning algorithms
• Key concepts like under- and over-fitting, regularization, and cross-validation
• How to identify the type of problem to be solved, choose the right algorithm, tune parameters, and validate a model.