×
Home Discussions Write at Opengenus IQ
×
  • Graph Algorithms
  • Dynamic Programming 💑
  • Greedy Algo 📔
  • Algo Book 🧠
  • String Algo 🧬
  • Join our Internship 🎓
  • Algorithms
  • Data Structures
  • Machine Learning 🤖
  • Home

Machine Learning (ML)

Machine Learning is the fastest growing and most potential field that enables a computer to perform specific tasks better than humans. It is actively used in companies like Apple, Tesla, Google and Facebook. We are covering the latest developments in the field

Machine Learning (ML)

John McCarthy, Man behind Garbage Collection

John McCarthy was one of the most influential Computer Scientists in 1950s and a Professor at Stanford University for nearly 38 years. He is best known for developing LISP Programming Language, inventing Garbage Collection, coining the word Artificial Intelligence and much more.
Benjamin QoChuk
Machine Learning (ML)

Advantages and Disadvantages of Logistic Regression

In this article, we have explored the various advantages and disadvantages of using logistic regression algorithm in depth.
Khushnuma Grover
Machine Learning (ML)

Native Language Identification (NLI)

Native language identification (NLI) is the task of determining an author's native language based only on their writings or speeches in a second language. This is an application of Machine Learning.
Zuhaib Akhtar
Machine Learning (ML)

Disadvantages of CNN models

CNN (Convolutional Neural Network) is the fundamental model in Machine Learning and is used in some of the most applications today. There are some drawbacks of CNN models which we have covered and attempts to fix it.
Sandeep Bhuiya
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.
Priyanshi Sharma Priyanshi Sharma
Machine Learning (ML)

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.
Aaliyah Ahmed
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.
Dishant Parikh
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.
Priyanshi Sharma Priyanshi Sharma
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.
NIKHIL PRATAP SINGH NIKHIL PRATAP SINGH
Machine Learning (ML)

Convolution Filters

Convolution filters are filters (multi-dimensional data) used in Convolution layer which helps in extracting specific features from input data. There are different types of Filters like Gaussian Blur, Prewitt Filter and many more which we have covered.
Priyanshi Sharma Priyanshi Sharma
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.
SUTHAR MUDRA BHAVIKKMUAR
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.
Aryanshu Verma
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.
Apoorva Kandpal Apoorva Kandpal
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.
Sachit Mishra
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.
Namita Mutha
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.
Shaurya Bhandari
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.
Sonali Gupta
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.
Simrann Arora Simrann Arora
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.
Ashish Kumar Sinha Ashish Kumar Sinha
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.
Divij Kulshrestha
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.
Aditya Mangla
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.
Zuhaib Akhtar
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.
Divij Kulshrestha
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.
Annie Lee Annie Lee
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
Anmol Saluja
×

Visit our discussion forum to ask any question and join our community

View Forum
OpenGenus IQ: Learn Computer Science © 2020 All rights reserved â„¢
Top Posts Facebook Twitter