In 1959 Arthur Samuel, an American pioneer in the field of computer gaming gave the idea of Machine Learning. He defined machine learning as — **“Field of study that gives computers the capability to learn without being explicitly programmed”**.

In 1997, Tom Mitchell gave a mathematical and relational definition that “**A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E**”

In Layman’s terms, Consider you are trying to toss a paper into a dustbin. In first…

AUR (Arch User Repository) is a community-driven repository for Arch Linux users, containing PKGBUILDs (package descriptions) that allow you to compile a package from source with makepkg and then install it via pacman.

Ensure the base-devel package group is installed. And also git should be installed to download packages. python-pip required to install setuptools

sudo pacman -S base-devel git python-pip

Install setuptools using pip.

pip install setuptools

Search and download PKGBUILDs from the AUR Web Interface

git clone https://aur.archlinux.org/aurman.git

- Change dir to aurman (downloaded package)

cd aurman

This `PKGBUILD`

can be built into installable packages using *makepkg*, then installed using…

Regularization is an important concept to avoid overfitting of the training data especially when the trained and tested data are much varying.

Regularization is calculated by adding a “penalty” term to the RSS to achieve a *lesser variance* with the tested data.

RSS modified by adding that sum of squares of the coefficients of B.

Suppose following equation is the Regression model.

How do I calculate accuracy for my regression model?

This is a common question by beginners when they make a regression predictive modeling project. But the fact is accuracy is a measure for classification, not regression. **We cannot calculate accuracy for a regression model**. The performance of a regression model measures by error in predictions.

For example if you are predicting value of house, you don’t want to know if the model is predicting the exact value or not. Now how you will know how close the predictions were to the expected values.

There are three error metrics that are…

In the previous article, I wrote about Linear Regression, optimization of error by taking such coefficient, Gradient Descend Method, Overdetermined System of the equation, etc. In this article, I am writing about Polynomial Regression and other things written in the title.

This is an image of Linear Regression

Polynomial regression is a form of regression in which the relationship between the independent variable and the dependent variable is an nᵗʰ degree polynomial function of x.

Suppose equation of an machine learning model is,

Where B0,B1…… are parameters and 1,x1,x2…… are features, and the curve is of n dimensions.

For example, suppose following table is training datasets of a machine learning model, where x0,x1,x2,x3 are features and y is result.

Gradient Descent is an optimizing algorithm used in Machine/ Deep Learning algorithms, to minimize the objective convex function f(x) using iteration. It find the global minimum of the objective function.

Before starting this you have to know following concept of mathematics.

A kind of statistical supervised learning technique for estimating the relationships between dependent & independent variables.

Here Y is dependent variable and X₁,X₂,X₃ …….,Xn are independent variable. Dependent variable is also called Outcome Variable, Response Variable and Independent Variable is also called Predictor Variable, Explanatory Variable.

Linear Regression is a statistical supervised learning technique to predict the dependent variable by forming a linear relationship with one or more independent variable.

- Simple Linear Regression
- Multiple Linear Regression

Simple Linear Regression find the linear relationship between two continuous variables,One independent and one dependent…

Ubuntu 18.04 user can skip this step.

Other operating system install Ubuntu 18.04 in docker, as shown bellow. Remember if you install Ubuntu in this process you don’t have to waste very much ram like virtual box.

In ubuntu Kivy is built from the packages `python-kivy`

, `python-kivy-examples`

.

So install these packages by run ..

`sudo apt-get install python-kivy python-kivy-examples debhelper python python-all-dev cython libgl1-mesa-dev libgles2-mesa-dev`

Buildozer is a tool that aim to package mobiles application easily. It automates the entire build process, download the prerequisites like python-for-android, Android SDK, NDK, etc.

First of all download buildozer.

`apt install git #…`

In this article, I am writing how to share files b/w two PCs.

Step 1: Download and Install Python3

Myself Ujjwal Kar, worked on various technology, interested to write in It. I write technical articles on medium without partner program