*THIS IS A TWO-PART SERIES.**PART 1**WILL BE ALL ABOUT THE THEORETICAL UNDERSTANDING OF THE LOGISTIC REGRESSION ALGORITHM. PART 2 (this one) WILL BE COVERING THE CODING PART, WHERE WE WILL BE IMPLEMENTING LOGISTIC REGRESSION IN PYTHON**

If you are confused about the ** theoretical working of the logistic regression algorithm**, you can read part 1 here.

The entire code used in this tutorial can be found here.

Since we have closely related logistic regression and linear regression, I would advise you to read up about linear regression as well to get a better understanding of these algorithms. …

**THIS IS A TWO-PART SERIES. PART 1 (this one) WILL BE ALL ABOUT THE THEORETICAL UNDERSTANDING OF THE LOGISTIC REGRESSION ALGORITHM. PART 2 WILL BE COVERING THE CODING PART, WHERE WE WILL BE IMPLEMENTING LOGISTIC REGRESSION IN PYTHON**

I have discussed a lot of Machine Learning articles in my previous blogs but most of them were *regression algorithms* i.e. predicting continuous numbers. This time we shift gears a little and move over to predicting discrete classes.

This time we will discuss the working of the ** logistic regression **algorithm.

Where regression algorithms predict continuous data e.g. the price of a particular…

For beginners, the terminology “*Machine Learning*” seems something very complicated and difficult. There is no doubt that it is one of the most rapidly developing fields but that doesn’t mean it has to be too complex. In this tutorial, we will be looking at a very simple, yet useful algorithm called the “** K-Nearest Neighbor Algorithm**”.

We have all heard the quote:

“you are defined by the company you keep”

KNN takes this literally 😁. This will be clearer when we look at the algorithm.

The entire code used in this tutorial can be found here.

KNN is a supervised algorithm…

In a previous article, we saw how to implement K-means algorithm from scratch in python. We delved deep into the working of the algorithm and discussed some possible practical applications. In this tutorial we are going to see one such application at work. In this tutorial we will see how we can use K-means clustering to separate an image into segments based on its pixel values.

**If you are new to machine learning or K-means, you can read the original article ****here****.**

**The complete code used in this article can be found ****here****.**

Since we have discussed all the nitty…

**K-Means clustering** is an *unsupervised machine learning* algorithm. Being unsupervised means that it requires no label or categories with the data under observation. If you are interested in **supervised algorithms** then you can start here.

K-means clustering is a surprisingly simple algorithm which creates groups (clusters) of similar data points within our entire dataset.** **This algorithm proves to be a very handy tool when *looking for hidden patterns in scattered data.*

The entire code used in this tutorial can be found here.

This tutorial will cover the following elements:

· **A brief overview of the k-means algorithm.**

**· Implementing the…**

Machine Learning algorithms have gained massive popularity over the last decade. Today these algorithms are used in several work fields for all sorts of data manipulation and predictions.

In this tutorial we will be implementing the most basic machine learning algorithm called “** Linear Regression**”. If we were to describe linear regression in a single line, it would be something like:

“Fitting a straight line through your data points”

This is a supervised machine learning algorithm. If you wish to learn about unsupervised algorithms, click here.

This tutorial will consist of **3 main parts**:

· Understanding and implementing the algorithm step-by-step.

…

A programming enthusiast who enjoys coding and loves to learn new things. AI using Python is my major focus.