To give you an example of the impact of machine learning, Man group's AHL Dimension programme is a $5.1 billion dollar hedge fund which is partially managed by AI. After it started off, by the year 2015, its machine learning algorithms were contributing more than half of the profits of the fund even though the assets under its management were far less. After reading this blog, you would be able to understand the basic logic behind some popular and incredibly resourceful machine learning algorithms which have been used by the trading community as well as serve as the foundation stone on which you step on to create the best machine learning algorithm. Initially developed in statistics to study the relationship between input and output numerical variables, it was adopted by the machine learning community to make predictions based on the linear regression equation. The mathematical representation of linear regression is a linear equation that combines a specific set of input data (x) to predict the output value (y) for that set of input values.

In Previous topic we came across the first most machine learning algorithm which is Linear Regression. Now it's learn about one of the linear algorithm in this section. Logistic Regression is used to solve the classification problems, so it's called as Classification Algorithm that models the probability of output class. Consider a scenario where we need to classify whether a patient has diabetes or not. If we use linear regression for this problem, there is a need for setting up a threshold based on which classification can be done.

Linear and Logistic regressions are usually the first algorithms people learn in predictive modeling. Due to their popularity, a lot of analysts even end up thinking that they are the only form of regressions. The ones who are slightly more involved think that they are the most important amongst all forms of regression analysis. The truth is that there are innumerable forms of regressions, which can be performed. Each form has its own importance and a specific condition where they are best suited to apply.

Linear Regression is a very popular prediction method and most likely the first predictive algorithm most be people learn. To put it simply, in linear regression you try to place a line of best fit through a data set and then use that line to predict new data points. Now our data file contains a listing of Years a person has worked for company A and their Salary. With a 2 variable data set, often it is quickest just to graph the data to check for a possible linear relationship. Looking at the plot, there definitely appears to be a linear relationship.

Linear regression is a very simple method but has proven to be very useful for a large number of situations. In this post you will discover exactly how linear regression works step-by-step. This tutorial was written for developers and does not assume any prior background in mathematics or statistics. This tutorial was written with the intention that you will follow a long in your own spreadsheet, which will help to make the concepts stick. Simple Linear Regression Tutorial for Machine Learning Photo by Catface27, some rights reserved.