In this blog I will be writing about simple, yet effective and commonly used, machine learning classifier, that is, Naive Bayes.

Here I will explain about What is Naive Bayes, Bayes theorem and its uses, Mathematical working of Naive Bayes, Step by Step implementation on it, Applications of Naive Bayes. Also i will provide link to my jupter notebook for references.

So without any further due lets get started.

It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. …

In this blog, I will be writing about a well known supervised ML algorithm used for for two-group classification problems, that is, Support Vector Machine(SVM).

Here I will explain about *What is SVM, How does SVM work, SVM kernels, SVM use cases* and finally I will provide link to my J*upyter notebook* where I have implemented SVM from scratch.

So without any further due lets get started.

Support Vector Machine (SVM) is a relatively simple **Supervised Machine Learning Algorithm** used for classification and/or regression. It is more preferred for classification but is sometimes very useful for regression as well. Basically…

In this Blog I will be sharing the explained implementation of image Segmentation using K-Means Clustering. Also I will be sharing my Jupyter Notebook of the implementation for references.

So before we jump into implementation lets learn about what is Image Segmentation and its uses.

In computer vision, image segmentation is the process of partitioning an image into multiple segments. The goal of segmenting an image is to change the representation of an image into something that is more meaningful and easier to analyze. It is usually used for locating objects and creating boundaries.

It is not a great idea…

In this Blog I will be writing about a well known unsupervised ML algorithm, that is, K-Means Clustering.

Here I will explain about *What is Clustering, Types of Clustering, types of clustering algorithm, K-Means Clustering, How does k-means algorithm works, applications of k-means clustering *and I will provide link to my *jupyter notebook* where i have implemented *K-means clustering from scratch*.

So without any further due lets get started.

Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points…

In this Blog I will be writing about a widely used classification ML algorithm, that is, Logistic Regression.

Here I will cover the topics like *What is Logistic Regression, Why we use it, How to get started with logistic Regression, Applications of Logistic regression, Advantages/Disadvantages* also I will provide my *Jupyter Notebook on implementation of Logistic regression from scratch*.

Also if you want know what is regression check below link. I have briefly explained about it here.

So without any further due lets get started.

Logistic regression is a statistical model that in its basic form uses a logistic function…

In this Blog I will be writing about a very famous classification as well as regression ML algorithm, that is, Random Forest

Here I will explain about what is random forest, why we use it, Introduction to ensemble method, Random Forest analogy, How to use Random Forest, Applications of Random Forest, Advantages/Disadvantages also I will provide link to my Jupyter notebook where I have implemented Random Forest algorithm, you check that for reference.

So without any further due lets get started.

A random forest consists of multiple random decision trees. Two types of randomnesses are built into the trees. First…

In this Blog I will be writing about a widely used classification (machine learning) algorithm, that is, Decision Tree.

Here I will explain about *what is Decision Tree, Types of Decision Tree Algorithm, How to create a Decision Tree, Applications of Decision Tree, Advantages/Disadvantages *and finally I will provide link to my briefly explained Jupyter Notebook on implementation of *decision tree algorithm from scratch*.

So without any further due lets get started.

- Decision tree algorithm falls under the category of supervised learning. They can be used to solve both regression and classification problems.
- Decision tree uses the tree representation to…

In this Blog I will be writing about a very famous supervised learning algorithm, that is, k-nearest neighbors or in short KNN.

Here I will explain about what is KNN algorithm, Industrial uses of KNN algorithm, How the KNN algorithm works, How to choose the value of K, Advantages/Disadvantages and finally I will provide link to my briefly explained Jupyter Notebook on implementation of KNN algorithm.

Also I will provide link to my** Digit classification (on mnsit dataset) **implemented using KNN algorithm. So without any further due lets get started.

KNN is a model that classifies data points based on…

In this blog I will be writing about Linear Regression, that is, what is linear regression, finding best fit regression line, checking goodness of fit etc.

In the end of the blog I’ll provide link to my jupyter notebook in which I have implemented a Linear Regression model from scratch with line by line explanation , so please do check that as well.

So without any further due lets get started.

**Before we dive into Linear regression lets understand what is regression and what are its use cases.**

Regression is a statistical method used in finance, investing, and other disciplines…

So this 3rd part of the blog as well as final part, as I will be covering the final topics for mathematics and statistics behind Machine Learning.

If you didn’t see my previous blog please do check it. Here’s the link to it.

**Part 1 —**

**Part 2 —**

So without any further delay lets get started with the final part of the series.

**For the final part we are left with two topics Multivariate Calculus | Algorithm and complexity. So in this Blog we will cover these topics.**

Before we move to definition, application and all the stuff about…