
Explore the basics of machine learning with a quick, visual, no-code overview of classification, regression, clustering, and association rules using drag-and-drop tools.
Apply naïve bayes in Orange to load data, visualize attributes from the Zoo dataset, train with an 80/20 split using cross-validation, and evaluate with accuracy and a confusion matrix.
Explore how linear regression predicts health plan cost from age using the line y = B0 + B1 x, and how training finds B0 and B1.
Explore clustering, a machine learning technique that groups data to reveal patterns across customers, text documents, products, and social networks, with examples from market segmentation and Netflix recommendations.
The area of Machine Learning is currently the most relevant field in Artificial Intelligence, being responsible for the use of intelligent algorithms that make computers learn through databases. The Machine Learning job market in various parts of the world is on the rise and the tendency is for this type of professional to be increasingly in demand! Some studies even indicate that knowledge in this area will soon be a prerequisite for Information Technology professionals!
To take you to this area, in this quick, basic and free course you will have a theoretical and practical overview of some machine learning algorithms using the Orange visual tool, which is one of the easiest tools for those starting learning since no computer programming skills are needed! The course is divided into four parts, which present the main areas of machine learning:
Classification: Naïve Bayes, decision trees, rules, and support vector machines (SVM) algorithms
Regression: linear regression algorithm
Clustering: k-means algorithm
Association rules: - apriori algorithm
This course aims to serve as a basic reference on the main machine learning techniques, especially for beginners in the area who do not have much time to take a longer and more complete course! I will see you in class!