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From prediction to prevention: Machines’ struggle to save our hearts

Dataconomy

The time has come for us to treat ML and AI algorithms as more than simple trends. This technological journey of humanity, which started with the slow integration of IoT systems such as Alexa into our lives, has peaked in the last quarter of 2022 with the increase in the prevalence and use of ChatGPT and other LLM models.

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How to build a Machine Learning Model?

Pickl AI

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. What is Machine Learning?

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Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

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. What is machine learning?

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Eager Learning and Lazy Learning in Machine Learning: A Comprehensive Comparison

Pickl AI

Support Vector Machines (SVM) : SVM is a powerful Eager Learning algorithm used for both classification and regression tasks. It constructs a hyperplane to separate different classes during training and uses it to make predictions on new data. What Are The Examples of Eager Learning Algorithms?

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7 Intriguing Artificial Intelligence Project Ideas for Beginners in 2023

How to Learn Machine Learning

Need inspiration building AI systems? As Artificial Intelligence (AI) continues to become more and more prevalent in our daily lives, it’s no surprise that more and more people are eager to learn how to work with the technology. Creating a virtual personal assistant starts with understanding the basics of AI and NLP.

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How To Use ML for Credit Scoring & Decisioning

phData

The model learns from these labels to predict the outcome of new, unseen data. Various machine learning algorithms can be used for credit scoring and decisioning, including logistic regression, decision trees, random forests, support vector machines, and neural networks. loan default or not).

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Embeddings in Machine Learning

Mlearning.ai

Hidden secret to empower semantic search This is the third article of building LLM-powered AI applications series. To enable semantic search, we need something called embedding/vector/vector embedding. What are Vector Embeddings? Pinecone Used a picture of phrase vector to explain vector embedding.