Support Vector Machine is one of the most commonly used supervised machine learning algorithms for data classification. A binary classifier, the support vector machine algorithm works in vector space to sort data points by finding the best hyperplane separating them into two groups. Thanks to its reliance upon vectors, it finds frontiers between groups of data points even in nonlinear patterns and features spaces of high dimensions.
K Nearest Neighbors is a popular classification algorithm for supervised machine learning. It permits to divide data points into groups, defining a model that will then be able to classify an unknown data point in one group or another. The K parameter, defined during programming, allows the algorithm to classify unknown data points by examining the K closest known data points.
One of the simplest supervised machine learning tools used in data science, linear regression permits to find the best-fitting line that correlates data points in a two-dimensional space. Defining this line then enables the prediction of where other data points could be located in the space, if they have the same characteristics as the original data set or if they stand out of it.
Diving deeper in data science and the actual coding of data processing functions and machine learning algorithms with Python, this series of tutorial gives us a great taste of what can be done in finance and stock trading. Through a hands-on approach, it guides us through the programming needed to retrieve, manipulate and visualize data, and, more importantly, to extract actionable insights.
Reinforcement Learning is one of the most exciting parts of Machine Learning and AI, as it allows for the programming of agents taking decisions in both virtual and real-life environments. This MIT course presents the theoretical background as well as the actual Deep Q-Network algorithm, that power some of the best Reinforcement Learning applications.