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Techniques for Data Scientists to Upskill with Large Language Models

Data Science Dojo

Data scientists are continuously advancing with AI tools and technologies to enhance their capabilities and drive innovation in 2024. The integration of AI into data science has revolutionized the way data is analyzed, interpreted, and utilized. Have you used voice assistants like Siri or Alexa?

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5 Error Handling Patterns in Python (Beyond Try-Except)

KDnuggets

By subscribing you accept KDnuggets Privacy Policy Leave this field empty if youre human: Get the FREE ebook The Great Big Natural Language Processing Primer and The Complete Collection of Data Science Cheat Sheets along with the leading newsletter on Data Science, Machine Learning, AI & Analytics straight to your inbox.

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Traditional vs Vector databases: Your guide to make the right choice

Data Science Dojo

It also facilitates integration with different applications to enhance their functionality with organized access to data. In data science, databases are important for data preprocessing, cleaning, and integration. Data scientists often rely on databases to perform complex queries and visualize data.

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t-SNE (t-distributed stochastic neighbor embedding)

Dataconomy

t-SNE (t-distributed stochastic neighbor embedding) has become an essential tool in the realm of data analytics, standing out for its ability to unravel the complexities inherent in high-dimensional data. This enables researchers to identify clusters and similarities among the data points more intuitively.

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Embedding projector

Dataconomy

The embedding projector is a powerful visualization tool that helps data scientists and researchers understand complex, high-dimensional data often encountered in machine learning (ML) and natural language processing (NLP). This awareness enables targeted interventions that foster model improvement.

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Bitcoin price outlook: How AI and data science are reshaping crypto market forecasting

Dataconomy

Clustering algorithms (K-Means) classify wallet activity to forecast shifts on a larger scale. These models usually combine on-chain data with social metrics and some macro variables to achieve a holistic view of market risk and momentum. Bayesian ModelsGreat during periods of heightened volatility for risk estimation.

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How Fastweb fine-tuned the Mistral model using Amazon SageMaker HyperPod as a first step to build an Italian large language model

AWS Machine Learning Blog

The dataset was stored in an Amazon Simple Storage Service (Amazon S3) bucket, which served as a centralized data repository. During the training process, our SageMaker HyperPod cluster was connected to this S3 bucket, enabling effortless retrieval of the dataset elements as needed.