Machine Learning (ML)

Advancing AIML to build a chatbot

In this article, we have explored some advanced concepts in AIML (Artificial Intelligence Markup Language) such as sets, maps, the '$' wildcard, loops, Rich Media Elements, buttons, hyperlinks, formatting and much more.

Differences between CNN and RNN

CNN (Convolution Neural Network) and RNN (Recurrent Neural Network) are two core Machine Learning models and are based on different fundamental ideas. In this article, we have explored the differences between CNN and RNN in depth.

Machine Learning (ML)

Edge Detection using Laplacian Filter

Laplacian Filter (also known as Laplacian over Gaussian Filter (LoG)), in Machine Learning, is a convolution filter used in the convolution layer to detect edges in input.

Machine Learning (ML)

Getting started with AIML to create Chatbots

AIML stands for Artificial Intelligence Markup Language and is used to create Chatbots. This article contains some basic utilities which can equip you to write a fulling functioning AIML bot.

Machine Learning (ML)

Sharpening Filters

Sharpening filters makes transition between features more recognizable and obvious as compared to smooth and blurry pictures. We have explored it in detail.

Machine Learning (ML)

Advantages of Support Vector Machines (SVM)

In this article, we have explored the Advantages of SVM in depth and compared Support Vector machine (SVM) with other approaches like Naive Bayes Algorithm and Logistic Regression as well.

Machine Learning (ML)

An Introduction to BERT

In this article, we have go through some of the basic concept related BERT architecture in general and Try to find the intuition behind using it . We also tried to explore similar models.

Machine Learning (ML)

Adversarial Machine Learning

Adversarial Machine learning is the technique which involves applying different methods in order to construct or generate examples that are meant to fool the machine learning model.

Machine Learning (ML)

K means vs K means++

The main difference between K Means and K Means++ lies in the selection of the centroids around which the clustering takes place and k means++ removes the drawback of K means which is it is dependent on initialization of centroid.

Machine Learning (ML)

Bernoulli Naive Bayes

Bernoulli Naive Bayes is used for discrete data and it works on Bernoulli distribution. The main feature of Bernoulli Naive Bayes is that it accepts features only as binary values like true or false, yes or no, success or failure, 0 or 1 and so on.

Machine Learning (ML)

How I mastered Machine Learning as a Fresher?

This is a complete guide by Shaurya Bhandari who went through the basics of Machine Learning as a fresher in College on his own. He explains the path he had taken so that you can follow it as well.

Machine Learning (ML)

VGG-11 Architecture

In this article, we have explored VGG-11 model architecture which has 11 layers namely 9 Convolution layers (with 5 MaxPool layers), 2 Fully connected layers and an output layer with softmax activation.

Machine Learning (ML)

The Inception Pre-Trained CNN Model

This is an overview of the Inception pre-trained CNN model along with a detailed description about its versions and network architectures including Inception V1, V2, V3, V4 and Inception-ResNet.

Machine Learning (ML)

Complete Guide on different Spell Correction techniques in NLP

This is the complete Guide on different Spell Correction techniques in Natural Language Processing (NLP) where we have explored approximate string matching techniques, coarse search, fine search, symspell, Seq2Seq along with code demonstration.

Machine Learning (ML)

Normalization in Machine Learning: A Breakdown in detail

In this article, we have explored Normalization in detail and presented the algorithmic steps. We have covered all types like Batch normalization, Weight normalization and Layer normalization.

Machine Learning (ML)

Techniques for Time Series Prediction

We have covered different techniques for Time series prediction which involves using Artificial Neural Networks like Single Layer NN, RNN, LSTM, using Stochastic models like ARMA, ARIMA, SAIMA and using Support Vector Machines.

Machine Learning (ML)

An Introduction to Recommendation System

This is the introduction to recommendation systems, how it works and more. We have different approaches to it like Content-based systems, Collaborative filtering systems and Hybrid systems.

Machine Learning (ML)

Activation Functions in Machine Learning: A Breakdown

We have covered the basics of Activation functions intuitively, its significance/ importance and its different types like Sigmoid Function, tanh Function and ReLU function.

Machine Learning (ML)

Gaussian blur (filter to blur images)

Gaussian blur is a type of image processing that applies a convolution filter on an image. This filter takes the surrounding pixels and returns a single number calculated with a weighted average based on the normal distribution.

Machine Learning (ML)

Text Preprocessing in Python using spaCy library

In this article, we have explored Text Preprocessing in Python using spaCy library in detail. Some techniques we have covered are Tokenization, Lemmatization, Removing Punctuations and Stopwords, Part of Speech Tagging and Entity Recognition

Machine Learning (ML)

Understand Support Vector Machine (SVM) by improving a simple classifier model

In this tutorial, we will start off with a simple classifier model and extend and improve it to ultimately arrive at what is referred to a support vector machine (SVM) which is a powerful Machine Learning model.

Machine Learning (ML)

Applications of Support Vector Machines (SVM)

Support Vector Machine (SVM) is a important ML model with several applications like Image-based analysis and classification tasks, Geo-spatial data-based applications, Text-based applications, Computational biology, Security-based applications and Chaotic systems control.

Machine Learning (ML)

Basics of Time Series Prediction

Time series prediction is the task where the initial set of elements in a series is given and we have to predict the next few elements. These are significant as it can be used to predict video frames as well when provided with initial frames.

Machine Learning (ML)

Sobel Filter/ Operator for Edge Detection

Sobel Filter/ Operator is a filter used in Convolution that is used to detect edges in an image. This is one of the fundamental approaches in Image Processing/ Machine Learning to detect edges.

Machine Learning (ML)

Everything about Pooling layers and different types of Pooling

We have explored the idea and computation details behind pooling layers in Machine Learning models and different types of pooling operations as well. In short, the different types of pooling operations are Maximum Pool, Minimum Pool, Average Pool and Adaptive Pool.