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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 (NaturalLanguageProcessing)? — YouTube YouTube Introduction to NaturalLanguageProcessing (NLP) NLP 2012 Dan Jurafsky and Chris Manning (1.1)
With advances in machine learning, deep learning, and naturallanguageprocessing, 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?
Deep Learning has been used to achieve state-of-the-art results in a variety of tasks, including image recognition, NaturalLanguageProcessing, and speech recognition. NaturalLanguageProcessing (NLP) This is a field of computer science that deals with the interaction between computers and human language.
NaturalLanguageProcessing (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.
SupportVectorMachines (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.
Naturallanguageprocessing ( 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.,
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