Linear Regression is among mostly used Machine Learning algorithms. Univariate Linear Regression is the simpler form, while Multivariate Linear Regression is for more complicated problems.

This is my second paper about Linear Regression, first one is on Univariate Linear Regression. You can look through it for getting background knowledge on Linear Regression, basics of Machine Learning algorithms, and better understanding of this paper.

- Introduction to Multivariate Linear Regression;
- Hypothesis of the algorithm;
- Manipulation of the dataset and matrix multiplication;
- Cost function;
- Gradient Descent.

In ML problems, there are various datasets, they differ from one another with their dimensions (number of rows and columns). As known, columns refer to the features, rows refer to samples, and more features mean more complicated models. In Linear Regression if the number of columns is one, it is called Univariate Linear Regression, if it is more than one, the algorithm is called Multivariate Linear Regression (MLR). The key concepts of Univariate and Multivariate LR algorithms are similar, but there are some differences in the equations, which are caused by various dimensions of datasets. …

Linear Regression (LR) is one of the main algorithms in Supervised Machine Learning. It solves many regression problems and it is easy to implement. This paper is about Univariate Linear Regression(ULR) which is the simplest version of LR.

**The paper contains following topics:**

- The basics of datasets in Machine Learning;
- What is Univariate Linear Regression?
- How to represent the algorithm(hypothesis), Graphs of functions;
- Cost function (Loss function);
- Gradient Descent.

In ML problems, beforehand some data is provided to build the model upon. The datasets contain of rows and columns. …

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