Simple Guide to the confusion matrix

A confusion matrix is a table that is often used to describe the performance of the classification model (or "classifier") on a set of test data for which the true values are known. The confusion matrix itself is relatively simple to understand, but the related terminology can be confusing. Confusion matrix A classification problem can be evaluated … Continue reading Simple Guide to the confusion matrix

Overfitting in Machine Learning

In this guide, we’ll walk you through exactly what overfitting means, how to spot it in your models, and what to do if your model is overfitting. By the end, you’ll know how to deal with this tricky problem once and for all. Table of Contents Examples of Overfitting Signal vs. Noise Goodness of fit … Continue reading Overfitting in Machine Learning

Data Imputation Techniques in Machine Learning

Have you come across the problem of handling missing data/values for respective features in machine learning (ML) models during prediction time? This is different from handling missing data for features during training/testing phase of ML models. Data scientists are expected to come up with an appropriate strategy to handle missing data during, both, model training/testing phase and also model prediction time … Continue reading Data Imputation Techniques in Machine Learning

Difference between classification and association algorithms

The term data mining refers loosely to finding relevant information or discovering knowledge from a large volumes of data. Like knowledge discovery in artificial intelligence, data mining attempts to discover statistical rules and patterns automatically from data. Knowledge discovered from a database can be represented by a set of rules. The following is an example … Continue reading Difference between classification and association algorithms

Quantum Machine Learning

A curated list of quantum machine learning algorithms, study materials, libraries, and software (by language). Why Quantum Machine Learning? Machine Learning(ML) is just a term in recent days but the work effort start from 18th century. What is Machine Learning?, In Simple word the answer is making the computer or application to learn themselves . … Continue reading Quantum Machine Learning

Logistic Regression – A complete Tutorial

Logistic regression is a predictive modelling algorithm that is used when the Y variable is binary categorical. That is, it can take only two values like 1 or 0. The goal is to determine a mathematical equation that can be used to predict the probability of event 1. Once the equation is established, it can … Continue reading Logistic Regression – A complete Tutorial

Deep Learning Resources

Online Courses Andrew Ng’s Machine-Learning Class on Coursera Geoff Hinton’s Neural Networks Class on Coursera (2012) U. Toronto: Introduction to Neural Networks (2015) Yann LeCun’s NYU Couse Ng’s Lecture Notes for Stanford’s CS229 Machine Learning Nando de Freitas’s Deep Learning Class at Oxford (2015) Andrej Karpathy’s Convolutional Neural Networks Class at Stanford Patrick Winston’s Introduction … Continue reading Deep Learning Resources

Summarize whole paragraph to sentence by Extractive Approach​

To catch a quick idea of a long document, we will always to do a summarization when we read an article or book. In English, the first (or first two) sentence(s) of each article has a very high chance of representing the whole article. Of course, the topic sentence can be the last sentence in … Continue reading Summarize whole paragraph to sentence by Extractive Approach​