Remove Azure Remove Natural Language Processing Remove Support Vector Machines
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A comprehensive guide to learning LLMs (Foundational Models)

Mlearning.ai

Learning LLMs (Foundational Models) Base Knowledge / Concepts: What is AI, ML and NLP Introduction to ML and AI — MFML Part 1 — YouTube What is NLP (Natural Language Processing)? — YouTube YouTube Introduction to Natural Language Processing (NLP) NLP 2012 Dan Jurafsky and Chris Manning (1.1)

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Creating an artificial intelligence 101

Dataconomy

With advances in machine learning, deep learning, and natural language processing, the possibilities of what we can create with AI are limitless. However, the process of creating AI can seem daunting to those who are unfamiliar with the technicalities involved. What is required to build an AI system?

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Deep Learning has been used to achieve state-of-the-art results in a variety of tasks, including image recognition, Natural Language Processing, and speech recognition. Natural Language Processing (NLP) This is a field of computer science that deals with the interaction between computers and human language.

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What Does the Modern Data Scientist Look Like? Insights from 30,000 Job Descriptions

ODSC - Open Data Science

Natural Language Processing (NLP) has emerged as a dominant area, with tasks like sentiment analysis, machine translation, and chatbot development leading the way. Core Machine Learning Algorithms Core machine learning algorithms remain foundational for data science workflows.

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Must-Have Skills for a Machine Learning Engineer

Pickl AI

Support Vector Machines (SVM) SVMs are powerful classifiers that separate data into distinct categories by finding an optimal hyperplane. Deep Learning Deep Learning is a specialised subset of Machine Learning involving multi-layered neural networks to solve complex problems. They are handy for high-dimensional data.

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Understanding and Building Machine Learning Models

Pickl AI

Natural language processing ( NLP ) allows machines to understand, interpret, and generate human language, which powers applications like chatbots and voice assistants. These real-world applications demonstrate how Machine Learning is transforming technology. For instance: For a classification problem (e.g.,