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2024 Tech breakdown: Understanding Data Science vs ML vs AI

Pickl AI

Summary: In the tech landscape of 2024, the distinctions between Data Science and Machine Learning are pivotal. Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. The collective strength of both forms the groundwork for AI and Data Science, propelling innovation.

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Exploring the dynamic fusion of AI and the IoT

Dataconomy

On the other hand, artificial intelligence is the simulation of human intelligence in machines that are programmed to think and learn like humans. By leveraging advanced algorithms and machine learning techniques, IoT devices can analyze and interpret data in real-time, enabling them to make informed decisions and take autonomous actions.

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From Rulesets to Transformers: A Journey Through the Evolution of SOTA in NLP

Mlearning.ai

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

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NLP-Powered Data Extraction for SLRs and Meta-Analyses

Towards AI

Last Updated on July 20, 2023 by Editorial Team Author(s): Gaugarin Oliver Originally published on Towards AI. It’s also an area that stands to benefit most from automated or semi-automated machine learning (ML) and natural language processing (NLP) techniques.

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Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction

Towards AI

Last Updated on February 20, 2024 by Editorial Team Author(s): Vaishnavi Seetharama Originally published on Towards AI. Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction Everyone is using mobile or web applications which are based on one or other machine learning algorithms.

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How Machines Learn: The Power of Gradient Descent

Towards AI

Last Updated on May 3, 2023 by Editorial Team Author(s): Ulrik Thyge Pedersen Originally published on Towards AI. Gradient descent is widely used in various machine learning models, such as linear regression, logistic regression, and neural networks.

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Creating an artificial intelligence 101

Dataconomy

The creation of artificial intelligence (AI) has long been a dream of scientists, engineers, and innovators. With advances in machine learning, deep learning, and natural language processing, the possibilities of what we can create with AI are limitless. How to create an artificial intelligence?