What are bias, variance and the bias-variance trade-off? The bias-variance trade-off is a fundamental concept in supervised machine learning that refers to the trade-off between the error due to...
![How To Guide To Bias-Variance Trade-Off [2 Examples In Python: Polynomial Regression & SVM]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/04/bias-varince-trade-off.jpg?fit=960%2C540&ssl=1)
The Natural Language Processing (NLP) Blog
Topic: Data Science
Data Quality In Machine Learning – Explained, Issues, How To Fix Them & Python Tools
What is data quality in machine learning? Data quality is a critical aspect of machine learning (ML). The quality of the data used to train a ML model directly impacts the accuracy and effectiveness...
Top 8 Most Useful Anomaly Detection Algorithms For Time Series And Common Libraries For Implementation
How does anomaly detection in time series work? What different algorithms are commonly used? How do they work, and what are the advantages and disadvantages of each method? Be able to choose the...
How To Implement Logistic Regression Text Classification In Python With Scikit-learn and PyTorch
Text classification is a fundamental problem in natural language processing (NLP) that involves categorising text data into predefined classes or categories. It can be used in many real-world...
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....
![Tutorial TF-IDF vs Word2Vec For Text Classification [How To In Python With And Without CNN]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/02/word2vec-text-classification.jpg?fit=1024%2C576&ssl=1)
Tutorial TF-IDF vs Word2Vec For Text Classification [How To In Python With And Without CNN]
Word2Vec for text classification Word2Vec is a popular algorithm used for natural language processing and text classification. It is a neural network-based approach that learns distributed...
Top 10 Natural Language Processing (NLP) Research Papers Worth Reading For Beginners
Reading research papers is integral to staying current and advancing in the field of NLP. Research papers are a way to share new ideas, discoveries, and innovations in NLP. They also give a more...
![How To Use Text Normalization Techniques In NLP With Python [9 Ways]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/01/keyword-normalisation.png?fit=1024%2C576&ssl=1)
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...
Top 5 Ways To Implement Question-Answering Systems In NLP & A List Of Python Libraries
What is a question-answering System? Question answering (QA) is a field of natural language processing (NLP) and artificial intelligence (AI) that aims to develop systems that can understand and...
The Curse Of Variability In Machine Learning And How To Overcome It
What is the curse of variability? The curse of variability refers to the idea that as the variability of a dataset increases, the difficulty of finding a good model that can accurately predict...
Top 6 Most Popular Text Clustering Algorithms And How They Work Explained
What exactly is text clustering? The process of grouping a collection of texts into clusters based on how similar their content is is known as text clustering. Text clustering combines related...
Opinion Mining — Top 8 Useful Tools More Than Sentiment Analysis
Opinion mining is a field that is growing quickly. It uses natural language processing and text analysis to gather subjective information from sources. The main goal of opinion mining is to find and...
Tutorial On How To Implement Document Clustering In Python With K-means
Introduction to document clustering and its importance Grouping similar documents together in Python based on their content is called document clustering, also known as text clustering. This...
How To Use One Hot Encoding In Python With 3 Tutorials
Categorical variables are variables that can take on one of a limited number of values. These variables are commonly found in datasets and can't be used directly in machine learning models as most...
![Hidden Markov Model (HMM) For NLP Made Easy [How To In Python]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/01/hmm-nlp.jpg?fit=1024%2C576&ssl=1)
Hidden Markov Model (HMM) For NLP Made Easy [How To In Python]
What is a Hidden Markov Model in NLP? A time series of observations, such as a Hidden Markov Model (HMM), can be represented statistically as a probabilistic model. Natural language processing (NLP)...
MinHash — How To Deal With Finding Similarity At Scale With Python Code To Get Started
What is MinHash? MinHash is a technique for estimating the similarity between two sets. It was first introduced in information retrieval to evaluate the similarity between documents quickly. The...
![How To Implement Fuzzy String Matching [4 Ways In Python]](https://i0.wp.com/spotintelligence.com/wp-content/uploads/2023/01/fuzzy-logic-hot-cold.jpg?fit=1024%2C1024&ssl=1)
How To Implement Fuzzy String Matching [4 Ways In Python]
This article discusses one of the most valuable tools when analysing textual data in natural language processing — fuzzy string matching. We first discuss what it is, its typical applications and...
