What is text labelling? Text labelling, or text annotation or tagging, assigns labels or categories to text data to make it more understandable and usable for various natural language processing...
The Natural Language Processing (NLP) Blog
Topic: Pre-Processing
![Language Identification Complete How To Guide In Python [With & Without Libraries]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/09/language-identification.jpg?fit=960%2C540&ssl=1)
Language Identification Complete How To Guide In Python [With & Without Libraries]
What is language identification? Language identification is a critical component of Natural Language Processing (NLP), a field dedicated to interacting with computers and human languages. At its...
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Top 20 Essential Text Cleaning Techniques [Practical How To Guide In Python]
What is text cleaning in NLP? Text cleaning, also known as text preprocessing or text data cleansing, is preparing and transforming raw text data into a cleaner, more structured format for analysis,...
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Imputation Of Missing Values Comprehensive & Practical Guide [Python]
What is Imputation? Imputation is a statistical and data analysis technique to fill in or estimate missing values within a dataset. Data may not be complete in real-world situations for multiple...
Practical Guide And Tutorial To Label Encoding In Python
What is label encoding machine learning? Label encoding is a technique used in machine learning and data preprocessing to convert categorical data (data that consists of categories or labels) into...
Principal Component Analysis Made Easy & How To Python Tutorial With Scikit-Learn
What is the meaning of PCA in machine learning? PCA stands for Principal Component Analysis. It is a statistical technique used in data analysis and machine learning to simplify the complexity of...
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...
How To Combine Numerical & Text Features: 10 Ways In Machine Learning And Deep Learning
Why Combine Numerical Features And Text Features? Combining numerical and text features in machine learning models has become increasingly important in various applications, particularly natural...
CountVectorizer Tutorial: How To Easily Turn Text Into Features For Any NLP Task
What is CountVectorizer in NLP? CountVectorizer is a text preprocessing technique commonly used in natural language processing (NLP) tasks for converting a collection of text documents into a...
Difference Between Structured And Unstructured Data
Unstructured data has become increasingly prevalent in today's digital age and differs from the more traditional structured data. With the exponential growth of information on the internet, the vast...
Endogenous vs Exogenous Variables Explained With Examples & Why It’s Important For Machine Learning
Endogenous and exogenous variables are two important concepts. In machine learning, endogenous variables are the variables that are directly influenced by other variables within the system being...
N-grams Made Simple & How To Implement In Python (NLTK)
In natural language processing, n-grams are a contiguous sequence of n items from a given sample of text or speech. These items can be characters, words, or other units of text, and they are used to...
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How To Implement Natural Language Processing (NLP) Feature Engineering In Python [8 Techniques]
Natural Language Processing (NLP) feature engineering involves transforming raw textual data into numerical features that can be input into machine learning models. Feature engineering is a crucial...
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How To Implement Data Augmentation In Python [Image & Text (NLP)]
Top 7 ways of implementing data augmentation for both images and text. With the top 3 libraries in Python to use for image processing and NLP. What is data augmentation? Data augmentation is a...
SMOTE Oversampling & Tutorial On How To Implement In Python And R
How does the algorithm work? What are the disadvantages and alternatives? And how do we use it in machine learning? How does SMOTE work? SMOTE stands for Synthetic Minority Over-sampling Technique....
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How To Use Text Normalization Techniques In NLP With Python [9 Ways]
Text normalization is a key step in natural language processing (NLP). It involves cleaning and preprocessing text data to make it consistent and usable for different NLP tasks. The process includes...
Part-of-speech (POS) Tagging In NLP: 4 Python How To Tutorials
What is Part-of-speech (POS) tagging? Part-of-speech (POS) tagging is fundamental in natural language processing (NLP) and can be done in Python. It involves labelling words in a sentence with their...
