A major challenge in the K-means algorithm is choosing the optimal value of k; however, selecting the right value of k is quite tricky and is also crucial as it can impact the performance of the […]

Continue readingMore Tag# Author: Niranjan B Subramanian

## Introduction to K-means

K-means clustering is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. Before we venture into K-means, let’s first understand what clustering is? What is clustering? The idea behind clustering is […]

Continue readingMore Tag## Regression Evaluation Metrics

Once we build our regression model, how can we measure the goodness of fit? We have various regression evaluation metrics to measure how well our model fits the data. In this article, we will see some […]

Continue readingMore Tag## Introduction to Linear Regression

Before we venture into linear regression, let’s first try to understand what regression analysis is? What is Regression? Regression is a statistical approach used for predicting real values like the age, weight, salary, for example. In […]

Continue readingMore Tag## Data Science Life Cycle

The data science life cycle consists of 7 phases. In this post, we will go through each of them briefly. The following infographic depicts different phases in the data science life cycle. PROBLEM DEFINITION: This phase […]

Continue readingMore Tag## Performance Measure for Classification(Part-2)

INTRODUCTION: In the last part, we see what a confusion matrix is and some other metrics like TPR, TNR, FPR, and FNR, which are based on the confusion matrix. In this post, we will see about […]

Continue readingMore Tag## Performance Measures for Classification

Why do we need performance measures? Why do we need performance measures at all? After we developed our classification model, we need to asses the performance of the model. Obviously, we can use accuracy as a […]

Continue readingMore Tag## Dropout for Regularization

INTRODUCTION: When we have deep neural networks, the biggest problem is overfitting. We can say a neural network overfits when it has excellent performance in the training data and has a poor performance in unseen data. […]

Continue readingMore Tag## Introduction to activation functions

Why do we need activation functions? We use activation function to introduce non-linearity into the neural network. Activation functions convert independent variables of near infinite range into simple probabilities between 0 and 1. Now the question […]

Continue readingMore Tag## Introduction to Principal Component Analysis(PCA)

Principal Component Analysis is an unsupervised dimensionality reduction technique, which is extensively used in machine learning. It helps us to alleviate the problem of the curse of dimensionality by reducing the dimension of the data. PCA […]

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