1604152020

Logistic Regression is an algorithm that can be used for regression as well as classification tasks but it is widely used for classification tasks. The response variable that is binary belongs either to one of the classes. It is used to predict categorical variables with the help of dependent variables. Consider there are two classes and a new data point is to be checked which class it would belong to. Then algorithms compute probability values that range from 0 and 1. For example, whether it will rain today or not. In logistic regression weighted sum of input is passed through the sigmoid activation function and the curve which is obtained is called the sigmoid curve.

Logistic regression uses an equation as the representation, very much like linear regression. Input values (x) are combined linearly using weights or coefficient values (referred to as the Greek capital letter Beta) to predict an output value (y). A key difference from linear regression is that the output value being modeled is a binary value (0 or 1) rather than a numeric value.

Below is an example logistic regression equation:

**y = e^(b0 + b1 x) / (1 + e^(b0 + b1x))**

Where y is the predicted output, b0 is the bias or intercept term and b1 is the coefficient for the single input value (x). Each column in your input data has an associated b coefficient (a constant real value) that must be learned from your training data. The actual representation of the model that you would store in memory or in a file is the coefficients in the equation (the beta value or b’s).

using a given set of independent features whereas*Linear regression is used for predicting the continuous dependent variable*.*Logistic Regression is used to predict the categorical*- Linear regression is used to
whereas logistic regression is used to*solve regression problems*.*solve classification problems* - In Linear regression, the approach is to find the best fit line to predict the output whereas in the Logistic regression approach is to try for S curved graphs that classify between the two classes that are 0 and 1.
- The method for accuracy in linear regression is the
whereas for logistic regression it is*least square estimation*.*maximum likelihood estimation* - In Linear regression,
like price & age, whereas in Logistic regression*the output should be continuous*like either Yes / No or 0/1.*the output must be categorical* - There should be a
between the dependent and independent features in the case of Linear regression whereas it is*linear relationship*.*not in the case of Logistic regression* - There can be collinearity between independent features in the case of linear regression but it is not in the case of logistic regression.

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1625843760

When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

**revoscalepy**– This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.**microsoftml**– This is another Microsoft Python package which adds machine learning algorithms in Python.**Anaconda 4.2**– Anaconda is an opensource Python package

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1619518440

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

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1619643600

If you want to become a machine learning professional, you’d have to gain experience using its technologies. The best way to do so is by completing projects. That’s why in this article, we’re sharing multiple machine learning projects in Python so you can quickly start testing your skills and gain valuable experience.

However, before you begin, make sure that you’re familiar with machine learning and its algorithm. If you haven’t worked on a project before, don’t worry because we have also shared a detailed tutorial on one project:

#artificial intelligence #machine learning #machine learning in python #machine learning projects #machine learning projects in python #python

1620367500

If you want to become a machine learning professional, you’d have to gain experience using its technologies. The best way to do so is by completing projects. That’s why in this article, we’re sharing multiple machine learning projects in Python so you can quickly start testing your skills and gain valuable experience.

However, before you begin, make sure that you’re familiar with machine learning and its algorithm. If you haven’t worked on a project before, don’t worry because we have also shared a detailed tutorial on one project:

The Iris dataset is easily one of the most popular machine learning projects in Python. It is relatively small, but its simplicity and compact size make it perfect for beginners. If you haven’t worked on any machine learning projects in Python, you should start with it. The Iris dataset is a collection of flower sepal and petal sizes of the flower Iris. It has three classes, with 50 instances in every one of them.

We’ve provided sample code on various places, but you should only use it to understand how it works. Implementing the code without understanding it would fail the premise of doing the project. So be sure to understand the code well before implementing it.

#artificial intelligence #machine learning #machine learning in python #machine learning projects #machine learning projects in python #python

1620898103

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

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Visit Blog- https://www.xplace.com/article/8743

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