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Predicting Race from Twitter: Unveiling Insights with pyCaret and Machine Learning

Mlearning.ai

One such intriguing aspect is the potential to predict a user’s race based on their tweets, a task that merges the realms of Natural Language Processing (NLP), machine learning, and sociolinguistics.

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Healthcare revolution: Vector databases for patient similarity search and precision diagnosis

Data Science Dojo

In vec t o r d a ta b a s e s , this process of querying is more optimized and efficient with the use of a sim i l a r i ty metric for searching the most sim i l a r vec t o r to our query. This may involve techniques like natural language processing for medical records or dimensionality reduction for complex biomolecular data.

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A Guide to Unsupervised Machine Learning Models | Types | Applications

Pickl AI

Machine Learning is a subset of artificial intelligence (AI) that focuses on developing models and algorithms that train the machine to think and work like a human. The following blog will focus on Unsupervised Machine Learning Models focusing on the algorithms and types with examples. What is Unsupervised Machine Learning?

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Five machine learning types to know

IBM Journey to AI blog

Each type and sub-type of ML algorithm has unique benefits and capabilities that teams can leverage for different tasks. Instead of using explicit instructions for performance optimization, ML models rely on algorithms and statistical models that deploy tasks based on data patterns and inferences. What is machine learning?

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Bias and Variance in Machine Learning

Pickl AI

Gender Bias in Natural Language Processing (NLP) NLP models can develop biases based on the data they are trained on. K-Nearest Neighbors with Small k I n the k-nearest neighbours algorithm, choosing a small value of k can lead to high variance.

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Semantic image search for articles using Amazon Rekognition, Amazon SageMaker foundation models, and Amazon OpenSearch Service

AWS Machine Learning Blog

You also generate an embedding of this newly written article, so that you can search OpenSearch Service for the nearest images to the article in this vector space. Using the k-nearest neighbors (k-NN) algorithm, you define how many images to return in your results. For this example, we use cosine similarity.

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Everything you should know about AI models

Dataconomy

Artificial Intelligence (AI) models are the building blocks of modern machine learning algorithms that enable machines to learn and perform complex tasks. K-nearest Neighbors For both regression and classification tasks, the K-nearest Neighbors (kNN) model provides a straightforward supervised ML solution.