Today’s blog will give you a warm-up for mathematical concepts involved in machine learning. So, let’s relive those days in high-school where you absolutely “adored” mathematics and wondered how learning this stuff would be useful in life.

Why do we need to learn maths?

Firstly, what people need to understand is that knowing libraries required for machine learning algorithms is not enough. In order to improve your implementation and grip on machine learning, you need to understand how an algorithm works and that’s where mathematics comes in.

• It helps you understand how certain factors,parameters and features affect the results the way they do.
• It helps you to…

Today we will be learning about a probabilistic classification algorithm known as logistic regression and its implementation.

Introduction

Logistic regression comes under the supervised machine learning algorithms. Its name is a bit misleading as it is used for binary classification however, the regression part stems from its similarity to linear regression. As we have learnt, linear regression involves around finding out a regression line which “fits” our dataset. For this we find out the parameters for our line which gives the minimum value of loss function. Logistic regression is pretty similar to that. …

Today, we will learn about the competence of a very “naive” algorithm known as Naive Bayes. We would also be discussing its implementation (from scratch) on a dataset containing both categorical and continuous values.

Introduction

Naive Bayes is a supervised learning algorithm used for classification problems. It comes from the family of “probabilistic classifiers” and applies Bayes theorem for classifying objects with the assumption that all the features are independent of each other. …

“Support Vector Machine” seems like handful of a name, but it is pretty simple if you understand it nicely. Today’s blog will give you a run through regarding what SVM is, how useful it is and how to implement linear SVM from scratch.

Introduction

It is a supervised-learning algorithm used for classification and regression problems, primarily used for the former though. It can be used as a non-probabilistic linear classifier.

This means that unlike logistic regression, it doesn’t find the probability of where a data point lies. It is more straightforward than that and just gives the class in which the…

Today we will be learning together a very interesting algorithm that uses similarity among items to group them. The idea seems pretty similar to that of KNN algorithm but the algorithm is not. Let’s get started with the k-means algorithm

What is clustering and what are its types?

Clustering is a process of grouping up data points or items based on their similar features. It does not involve labelled data and it is left on the machine to find a pattern according to which we cluster. There are 3 types of clustering:

• Exclusive Clustering: The clusters are non-overlapping ( example: K-means)

Greetings, my fellow noobs. Today I have compiled a blog for decision trees, a concept which at first glance may seem like a “tangled mess” but is much simpler than that. Here, we will grasp the idea and working of the decision tree and how it makes your life a tad bit easier.

Introduction

It is a supervised learning algorithm used in classification and regression problems. It uses its tree-like representation to give visuals of all the decisions that can be made given the problem.

In very basic, noobish terms, a decision trees is a tree which represents all possible solutions…

Today, we will be learning about KNN algorithm, its applications, intuition behind it and its implementation with MNIST dataset.

Introduction

KNN or K-Nearest Neighbours is a supervised classification algorithm and is as straightforward of an algorithm as its name. This algorithm classifies a given instance based on the groups of its surrounding “K” number of neighbours. For instance,

Assume you are given 2 groups : food and vehicle. You were given the task of classifying apple in any one of the groups, then the distance between apple and the other food items would be less compared to the vehicle group. This…

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Introduction

Linear regression is an algorithm that models the relationship variables linearly. When we say relationship, it is between our dependent variable and the independent variable(s). In this algorithm, we create a “linear” regression line in such a way that it “fits” our dataset. This is a regression algorithm which means that it is used to find out continuous values for our dependent variable. This should not be confused with our classification problem.

For example,

if you are predicting whether it would rain today or not, your answer would be either “yes” or “no”. …