Regression predicts a continuous value: for example, the return on an asset. Classification predicts a discrete value: for example, will a stock outperform next period? This is a binary classification problem, predicting a yes/no response. Another example: Which quartile will a stock's performance fall into next month? This is multinomial classification, predicting a categorical variable with 4 possible outcomes.
Algorithms are the smart and powerful soldier of a complex machine learning model. In other words, machine learning algorithms are the core foundation when we play with data or when it's come to training the model. In this article, you and I are going on a tour called "7 major machine learning algorithms and their application " The purpose of this tour is to either brush up the mind or to gain an essential understanding of machine learning algorithm. We will find the major answer in this tour like for what purpose machine learning algorithms works, where to use them, when to use them and how to use them. Before getting deeper let's have a brief introduction. Machine learning algorithms are mainly classified into 3 broad categories i.e supervised learning, unsupervised learning, and reinforcement learning. In supervised learning machine learning algorithms, the machine is taught by example. Here the operator provides the machine learning algorithm with the dataset. This dataset includes desired inputs and outputs variables. By the use of these set of variables, we generate a function that map inputs to desired outputs.
Data science, also known as data-driven decision, is an interdisciplinery field about scientific methods, process and systems to extract knowledge from data in various forms, and take descision based on this knowledge. A data scientist should not only be evaluated only on his/her knowledge on mahine learning, but he/she should also have good expertise on statistics. I will try to start from very basics of data science and then slowly move to expert level.