As part of our interview cycle, candidates work with some data and build a simple model. After we talk through the modeling and data work, I ask them to come up with a business case for the model. Once they have done so, I follow up with: How would you measure the success of this … Continue reading Interview Question: What Machine Learning Metric to Use

# Category: Machine Learning

# 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

# Federated Learning

Introduction Federated Learning (FL) is a distributed machine learning approach which enables training on a large corpus of decentralised data residing on devices like mobile phones. FL is one instance of the more general approach of “brining the code to the data, instead of the data to the code” and addresses the fundamental problems of … Continue reading Federated Learning

# 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

# Linear Regression In Pictures

What is linear regression? Suppose you are thinking of selling your home. Different sized homes around you have sold for different amounts: Your home is 3000 square feet. How much should you sell it for? You have to look at the existing data and predict a price for your home. This is called linear regression. … Continue reading Linear Regression In Pictures

# 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