Artificial Intelligence is rapidly becoming the present and future of technology. Machine learning algorithms have been created to handle challenging real-world situations. These algorithms are highly efficient and self-modifying, as they improve over time with the addition of more data and minimal human involvement. Let's go over the top machine learning algorithms you should be familiar with to keep up with the latest ML advancements. The algorithm depicts the relationship between two variables, one independent and the other dependent. When the independent variable is changed, it affects the dependent variable.

Support Vector Machine (SVM) is a supervised machine learning algorithm capable of performing classification, regression and even outlier detection. The linear SVM classifier works by drawing a straight line between two classes. This type of algorithm classifies output data and makes predictions. The output of this model is a set of visualized scattered plots separated with a straight line. You will learn the fundamental theory and practical illustrations behind Support Vector Machines and learn to fit, examine, and utilize supervised Classification models using SVM to classify data, using Python.

Most of the beginners start by learning regression. It is simple to learn and use, but does that solve our purpose? Because, you can do so much more than just Regression! Think of machine learning algorithms as an armory packed with axes, sword, blades, bow, dagger etc. You have various tools, but you ought to learn to use them at the right time.

Most of the beginners start by learning regression. It is simple to learn and use, but does that solve our purpose? Because, you can do so much more than just Regression! Think of machine learning algorithms as an armory packed with axes, sword, blades, bow, dagger etc. You have various tools, but you ought to learn to use them at the right time.