What is ensemble learning in machine learning? Ensemble learning is a machine learning technique that combines the predictions of multiple individual models to improve a machine learning algorithm's...
The Natural Language Processing (NLP) Blog
Machine Learning – Deep Learning – Data Science
![Deep Dive into Active Learning in Machine Learning [How To In Python & PyTorch]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/08/active-learning-try-fail.jpg?fit=1200%2C675&ssl=1)
Deep Dive into Active Learning in Machine Learning [How To In Python & PyTorch]
What is active learning in machine learning? Active learning is a machine learning technique that involves iteratively selecting and labelling the most informative examples from an unlabeled dataset...
Comparative Guide: Top 7 Outlier Detection Algorithms & How To Tutorials In Python
Outlier detection in machine learning Outlier detection is a task in machine learning and data analysis involving identifying points that deviate significantly from the rest of the data. These data...
Unveiling the Hidden Peril: Understanding Data Leakage in Machine Learning
Welcome to our blog post, where we delve into a critical aspect of machine learning that often goes unnoticed but can significantly impact the reliability of our models - data leakage. As...
What Is A Large Language Model? Use Cases, Benefits, Limitations & What Does The Future Hold?
What is a large language model? A large language model (LLM) is a type of artificial intelligence (AI) trained on massive text and code datasets. This allows them to learn the patterns and...
![Zero-shot Classification [Top 6 Models, How To Tutorial In Python & Alternatives]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/08/zero-shot-classification.jpg?fit=1200%2C675&ssl=1)
Zero-shot Classification [Top 6 Models, How To Tutorial In Python & Alternatives]
What is zero-shot classification? Zero-shot classification is a machine learning approach in which a model can classify data into multiple classes without any specific training examples for those...
![How To Apply Feature Scaling In Machine Learning [Top Methods & Tutorials In Python]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/07/min-max-feature-scaling.jpg?fit=1200%2C675&ssl=1)
How To Apply Feature Scaling In Machine Learning [Top Methods & Tutorials In Python]
What is feature scaling in machine learning? Feature scaling is a preprocessing technique used in machine learning and data analysis to bring all the input features to a similar scale. It is...
K-Fold Cross-validation Complete Guide — How To Tutorial In Machine Learning & Deep Learning
What is k-fold cross-validation? K-fold cross-validation is a popular technique used to evaluate the performance of machine learning models. It is advantageous when you have limited data and want to...
Understanding & Implementing The Continuous Bag-of-Words (CBOW) Model
Introduction to word embeddings Word embeddings have become a cornerstone of Natural Language Processing (NLP), transforming how machines process and understand human language. These vector...
Skip-gram Models Explained & How To Create Embeddings In Word2Vec
What is skip-gram? Skip-gram is a popular algorithm used in natural language processing (NLP), specifically in word embedding techniques. It is a method for learning word representations in a vector...
Top 6 Name Matching Algorithm, How To Scale Your Solution & Tutorial In Python
What is fuzzy name matching? A fuzzy name matching algorithm, or approximate name matching, is a technique used to compare and match names with slight differences, variations, or errors. It is...
Graph Neural Network Explained & How To Tutorial In Python With PyTorch
Graph Neural Network (GNN) is revolutionizing the field of machine learning by enabling effective modelling and analysis of structured data. Originally designed for graph-based data, GNNs have found...
Few-shot Learning Explained & Step-by-step How To Python Tutorial
What is few-shot learning? Few-shot learning is a machine learning technique that aims to train models to learn new tasks or recognise new classes of objects using only a small amount of labelled...
![The History Of Natural Language Processing & Potential Future Breakthroughs [With Infographic Timeline]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/06/history-natural-language-processing.jpg?fit=960%2C540&ssl=1)
The History Of Natural Language Processing & Potential Future Breakthroughs [With Infographic Timeline]
History of natural language processing Natural Language Processing (NLP) has a fascinating history that spans several decades. Let's journey through time to explore the key milestones and...
Activation Function: Top 9 Most Popular Explained & When To Use Them
What is an activation function? In artificial neural networks, an activation function is a mathematical function that introduces non-linearity to the output of a neuron or a neural network layer. It...
Top 10 Ways Of Combining Numerical And Text Features – With How To Tutorial In Python
Combining numerical and text features in machine learning models has become increasingly important in various applications, particularly natural language processing (NLP) and text analytics. By...
Open Source Large Language Models (LLM) – Top 10 Most Powerful To Consider In 2023
What are open-source large language models? Open-source large language models, such as GPT-3.5, are advanced AI systems designed to understand and generate human-like text based on the patterns and...
L1 And L2 Regularization Explained, When To Use Them & Practical Examples
L1 and L2 regularization are techniques commonly used in machine learning and statistical modelling to prevent overfitting and improve the generalization ability of a model. They are regularization...
