A deep insight into logistic regression including the math involved in it.

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Hello Guys!!!

So far, we have discussed the Regression technique. Now, let us look at the second important Supervised learning algorithm, i.e. Classification.

Can classification problems be solved using Linear Regression? Why is Logistic Regression being a classification technique, still named regression? These questions along with a delineate on Logistic Regression will be addressed in this blog.

Introduction to Classification


A brief introduction to Stepwise Regression.

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So, you saw the name, and it says Stepwise. As the name stepwise regression suggests, this procedure selects variables in a step-by-step manner. Stepwise either adds the most significant variable or removes the least significant variable. It does not consider all possible models, and it produces a single regression model when the algorithm ends.

It is highly used to meet regression models with predictive models that are carried out naturally. With every forward step, the variable gets added or subtracted from a group of descriptive variables.

How Stepwise Regression Works

  • The Backward Method: Whenever a model is fully…

What if the simple linear regression model can’t find any relationship between the target and the predictor variable?🤔

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What if your linear regression model cannot establish the relationship between the target variable and the predictor variable? In other words, what if they don’t have a linear relationship at all. After this blog, you will definitely get all your answers. This is my third blog on Regression series. This blog requires prior knowledge of Linear Regression.

What is Polynomial Regression?


Solving overfitting and underfitting problems of the linear regression by using some new regression techniques.

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We already learned Linear Regression, so what’s new here? What was the need for Ridge & Lasso Regression technique? We will be answering all these questions right here in detail. First: let’s look at possible categories of results that can be obtained from training a model with linear regression.


Linear Regression is the beginner’s algorithm for a kick-start in Machine Learning. Let's take a deep dive into the Math behind this algorithm.

Source: Author

Whenever you come across linear regression, the first thing that should come to your mind is a scatter plot image somewhat like this.

To set up the possible relationship among different variables, various modes of statistical approaches are implemented, known as regression analysis. To understand how the variation in an independent variable can impact the dependent variable, regression analysis is specially molded out.

Basically, regression analysis sets up an equation to explain the significant relationship between one or more predictors and response variables and also to estimate current observations. …


A complete discussion on dataset types and ML algorithms for beginners. Processes involved in creating the ML model will also be covered.

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Hello everyone, as the title suggests, in this blog, we will be going through different types and processes involved in Machine Learning. But, let's first understand how machine learning works.

How does Machine Learning Work?

  • The Machine Learning algorithm is trained using a training dataset and a model is created.
  • Then the trained model works on a test dataset and generates predictions.
  • The prediction is evaluated for accuracy and if the accuracy is acceptable, the Machine Learning model is deployed. If the accuracy is not acceptable, then the Machine Learning model is trained with other techniques.
  • The type of technique (or algorithm) of ML used…


An important Python library for Machine Learning.

Python Pandas is an open-source library that provides high-performance data manipulation in Python. This tutorial is designed for both beginners and professionals.

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Key Features of Pandas

  • It’s fast and efficient DataFrame object indexing easy.
  • Used for reshaping and pivoting of the data sets.
  • Group by data for aggregations and transformations.
  • It is used for data alignment and integration of the missing data.
  • Provide the functionality of Time Series.
  • Process a variety of data sets in different formats like matrix data, tabular heterogeneous, time series.
  • Handle multiple operations of the data sets such as subsetting, slicing, filtering, groupBy, re-ordering, and re-shaping.
  • It integrates with the…


Python’s beginner libraries to start Machine Learning.

Credit: Author

In this blog, we will look at Python modules(NumPY & File Handling) necessary for Machine Learning. Please try to run the code by yourself for a better understanding.

Prerequisite; Basic knowledge of Python is necessary to understand this blog.

NumPy: NumPY is a Python library used for working with arrays. It also has functions for working in domain of linear algebra, Fourier transform (Fourier Transform is an important image processing tool which is used to decompose an image into its sine and cosine components), and matrices.


The History of AI

Step towards AI

The study of history is important because it allows one to make more sense of the current world. It let’s you know the efforts done by our scientists for creating AI. Its always interesting to start a topic to learn from its history.


Basics and Prerequisites of Machine Learning & Deep Learning for Beginners in Data Science

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Hello guys, I am sure that you are ready to dive deep into Data Science. I will cover the complete course with essential code from 0 to 100 in this series of blogs. So, let’s begin...

Topics covered in this blog:

1) Differencing these technologies (AI vs ML vs DL)

2) Defining these technologies

3) Prerequisite to become a Data Scientist

4) Life-cycle of Data Science Project

How AI & ML are different from each other?🤔

Deep Patel

Learning and exploring this beautiful world with amazing tech.

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