Free online courseMachine Learning

Course Description

Machine learning is no longer reserved for research labs; it is a practical skill used to build smarter products, automate decisions, and uncover patterns in data. This free online course gives you a clear starting point in Artificial Intelligence and Machine Learning, with a focus on understanding how models learn and how to choose the right approach for a real problem. If you want to move from curiosity to confidence, you will learn the essentials in a way that connects concepts to how they are applied in modern workflows.

Starting with machine learning using Python, you will build a strong mental model of what ML systems do and why they work. You will explore the difference between regression and classification and learn to recognize which type of problem you are facing based on the outcome you need to predict. This foundation matters because many beginners get stuck not on coding, but on framing the problem correctly and interpreting what a model is actually returning.

A central theme is logistic regression, one of the most useful baseline algorithms for classification. You will understand what logistic regression predicts, what category of learning it belongs to, and how it relates to classifiers in general. Rather than memorizing definitions, you will connect these ideas to practical decision-making, such as when you need probabilities, when you need labels, and how classification models differ from regression models in goals and evaluation.

You will also clarify the primary difference between supervised and unsupervised learning, helping you understand when you need labeled examples and when you are trying to discover structure in unlabeled data. By the end, you will be able to speak confidently about core machine learning terminology, avoid common misconceptions, and take the next step toward building and testing your own models in Python for everyday data-driven problems.