What are ROC and AUC Curves in Machine Learning? The ROC Curve The ROC (Receiver Operating Characteristic) curve is a graphical representation used to evaluate the performance of binary...

# The Natural Language Processing (NLP) Blog

## Naive Bayes Classification Made Simple & How To Tutorial In Python

What is Naive Bayes? Naive Bayes classifiers are a group of supervised learning algorithms based on applying Bayes' Theorem with a strong (naive) assumption that every feature in the dataset is...

## How To Implement Anomaly Detection With One-Class SVM In Python

What is One-Class SVM? One-class SVM (Support Vector Machine) is a specialised form of the standard SVM tailored for unsupervised learning tasks, particularly anomaly detection. Unlike traditional...

## Decision Trees In ML Complete Guide [How To Tutorial, Examples, 5 Types & Alternatives]

What are Decision Trees? Decision trees are versatile and intuitive machine learning models for classification and regression tasks. It represents decisions and their possible consequences,...

## Isolation Forest For Anomaly Detection Made Easy & How To Tutorial

What is an Isolation Forest? Isolation Forest, often abbreviated as iForest, is a powerful and efficient algorithm designed explicitly for anomaly detection. Introduced by Fei Tony Liu, Kai Ming...

## Batch Gradient Descent In Machine Learning Made Simple & How To Tutorial In Python

What is Batch Gradient Descent? Batch gradient descent is a fundamental optimization algorithm in machine learning and numerical optimisation tasks. It is a variation of the gradient descent...

## Full-Text Search Explained, How To Implement & 6 Powerful Tools

What is Full-Text Search? Full-text search is a technique for efficiently and accurately retrieving textual data from large datasets. Unlike traditional search methods that rely on simple string...

## Support Vector Regression (SVR) Simplified & How To Tutorial In Python

What is Support Vector Regression (SVR)? Support Vector Regression (SVR) is a machine learning technique for regression tasks. It extends the principles of Support Vector Machines (SVM) from...

## Weight Decay In Machine Learning And Deep Learning Explained & How To Tutorial

What is Weight Decay in Machine Learning? Weight decay is a pivotal technique in machine learning, serving as a cornerstone for model regularisation. As algorithms become increasingly complex and...

## Collaborative Filtering In Machine Learning Made Simple [6 Different Approaches]

What is Collaborative Filtering? In today's digital era, where we are inundated with overwhelming information and choices, recommendation systems have become indispensable. From suggesting movies to...

## Data Drift In Machine Learning Explained: How To Detect & Mitigate It

What is Data Drift Machine Learning? In machine learning, the accuracy and effectiveness of models heavily rely on the quality and consistency of the data on which they are trained. However, in...

## Classification Metrics In Machine Learning Explained & How To Tutorial In Python

What are Classification Metrics in Machine Learning? In machine learning, classification tasks are omnipresent. From spam detection in emails to medical diagnosis and sentiment analysis in social...

## Co-occurrence Matrices Explained: How To Use Them In NLP, Computer Vision & Recommendation Systems [6 Tools]

What are Co-occurrence Matrices? Co-occurrence matrices serve as a fundamental tool across various disciplines, unveiling intricate statistical relationships hidden within data. Whether in natural...

## 10 Regression Metrics For Machine Learning & Practical How To Guide

What are Evaluation Metrics for Regression Models? Regression analysis is a fundamental tool in statistics and machine learning used to model the relationship between a dependent variable and one or...

## Bagging, Boosting & Stacking Made Simple [3 How To Tutorials In Python]

What is Bagging, Boosting and Stacking? Bagging, boosting and stacking represent three distinct ensemble learning techniques used to enhance the performance of machine learning models. Bagging,...

## Top 9 Performance Metrics In Machine Learning & How To Use Them

Why Do We Need Performance Metrics In Machine Learning? In machine learning, the ultimate goal is to develop models that can accurately generalize to unseen data and make reliable predictions or...

## Stochastic Gradient Descent (SGD) In Machine Learning Explained & How To Implement

Understanding Stochastic Gradient Descent (SGD) In Machine Learning Stochastic Gradient Descent (SGD) is a pivotal optimization algorithm widely utilized in machine learning for training models....

## The Cold-Start Problem In Machine Learning Explained & 6 Mitigating Strategies

What is the Cold-Start Problem in Machine Learning? The cold-start problem refers to a common challenge encountered in machine learning systems, particularly in recommendation systems, where the...