Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed
Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
Linear regression is a linear system and the coefficients can be calculated analytically using linear algebra. ...
Linear regression does provide a useful exercise for learning stochastic gradient descent which is an important algorithm used for minimizing cost functions by machine learning algorithms.
Linear regression is a very simple approach for supervised learning. Though it may seem somewhat dull compared to some of the more modern algorithms, linear regression is still a useful and widely used statistical learning method. Linear regression is used to predict a quantitative response Y from the predictor variable X. Linear Regression is made with an assumption that there’s a linear relationship between X and Y.
Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).
When there is a single input variable (x), the method is referred to as simple linear regression. When there are multiple input variables, literature from statistics often refers to the method as multiple linear regression.
Accord.NET is a framework for scientific computing in .NET. The source code of the project is available under the terms of the Gnu Lesser Public License, version 2.1.
The framework comprises a set of libraries that are available in source code as well as via executable installers and NuGet packages.
The main areas covered include numerical linear algebra, numerical optimization, statistics, machine learning, artificial neural networks, signal and image processing, and support libraries (such as graph plotting and visualization). The project was originally created to extend the capabilities of the AForge.NET Framework, but has since incorporated AForge.NET inside itself. Newer releases have united both frameworks under the Accord.NET name.
The Accord.NET Framework has been featured in multiple books such as Mastering.NET Machine Learning by PACKT publishing and F# for Machine Learning Applications
After merging with the AForge.NET project, the framework now offers a unified API for learning/training machine learning models that is both easy to use and extensible. It is based on the following pattern:
Choose a learning algorithm that provides a Learn(x, y) or Learn(x) method;
Use the Learn(x, y) to create a machine learning model learned from the data;
Use the model's Transform, Decide, Scores, Probabilities or LogLikelihoods methods.
For Installing process check here: https://github.com/accord-net/framework
