Data Science Dojo

AUGUST 19, 2024

They dive deep into artificial neural networks, algorithms, and data structures, creating groundbreaking solutions for complex issues. These professionals venture into new frontiers like machine learning, natural language processing, and computer vision, continually pushing the limits of AI’s potential.

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Pickl AI

AUGUST 1, 2023

Machine Learning models play a crucial role in this process, serving as the backbone for various applications, from image recognition to natural language processing. In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types.

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IBM Journey to AI blog

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

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Cambridge Intelligence

FEBRUARY 20, 2024

FREE: Managing fraud The ultimate guide to fraud detection, investigation and prevention using data visualization GET YOUR FREE GUIDE The role of new & existing technology For many years, credit card companies have relied on analytics, algorithms and decision trees to power their fraud strategy.

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Pickl AI

AUGUST 23, 2023

Ensure the data is cleaned, formatted, and free from inconsistencies, as accurate predictions heavily depend on the quality of input data. Model Selection and Evaluation Experiment with different Machine Learning algorithms for stock price prediction, such as linear regression, decision trees , random forests, and neural networks.

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Pickl AI

JULY 23, 2023

Text Analytics (Natural Language Processing) Text analytics, also known as natural language processing (NLP), involves extracting valuable information and insights from unstructured text data, such as customer reviews, social media posts, or survey responses. Key Features: i.

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Pickl AI

NOVEMBER 28, 2024

Decision Trees These trees split data into branches based on feature values, providing clear decision rules. These networks can learn from large volumes of data and are particularly effective in handling tasks such as image recognition and natural language processing.

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