Foundations of Supervised Machine Learning

Machine Learning is one of the most important technologies powering modern artificial intelligence systems. From recommendation systems to predictive analytics, machine learning algorithms are widely used across industries.
This course, Foundations of Supervised Machine Learning, is designed to help beginners understand the core concepts behind supervised learning algorithms. If you are new to machine learning and want to build a strong conceptual foundation, this course will guide you step by step through some of the most important supervised learning techniques.
In this course, you will start by learning the basic concepts of machine learning and the different types of learning approaches. You will then explore regression models and understand how regression algorithms are used to predict continuous values.
Next, the course introduces probabilistic generative models, including the Naive Bayes theorem and how it is applied in machine learning classification tasks. You will also learn about Support Vector Machines (SVM) and how they use the concept of maximum margin classification.
Finally, you will understand how Decision Tree classifiers work and how they are used to make predictions based on structured decision rules.
By the end of this course, you will have a clear understanding of several key supervised machine learning algorithms and the concepts behind them.
This course is ideal for students, beginners in artificial intelligence, and anyone interested in learning the foundations of machine learning.