Remove AI Remove Decision Trees Remove Supervised Learning
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Generative vs Discriminative AI: Understanding the 5 Key Differences

Data Science Dojo

In the recent discussion and advancements surrounding artificial intelligence, there’s a notable dialogue between discriminative and generative AI approaches. These methodologies represent distinct paradigms in AI, each with unique capabilities and applications. What is Generative AI?

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Exploring All Types of Machine Learning Algorithms

Pickl AI

Summary: Machine Learning algorithms enable systems to learn from data and improve over time. Key examples include Linear Regression for predicting prices, Logistic Regression for classification tasks, and Decision Trees for decision-making. Linear Regression predicts continuous outcomes, like housing prices.

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Top 17 trending interview questions for AI Scientists

Data Science Dojo

The demand for AI scientist is projected to grow significantly in the coming years, with the U.S. AI researcher role is consistently ranked among the highest-paying jobs, attracting top talent and driving significant compensation packages. Python Explain the steps involved in training a decision tree.

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Classification and Regression in Machine Learning: Understanding the Difference

Towards AI

Last Updated on January 12, 2024 by Editorial Team Author(s): Davide Nardini Originally published on Towards AI. Arguably, one of the most important concepts in machine learning is classification. This article will illustrate the difference between classification and regression in machine learning. Published via Towards AI

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Supervised learning vs Unsupervised learning

Pickl AI

Therefore, Supervised Learning vs Unsupervised Learning is part of Machine Learning. Let’s learn more about supervised and Unsupervised Learning and evaluate their differences. What is Supervised Learning? What is Unsupervised Learning?

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Decision Tree Classification- A Guide to Supervised Machine Learning Algorithm

Pickl AI

One of the most popular algorithms in Machine Learning are the Decision Trees that are useful in regression and classification tasks. Decision trees are easy to understand, and implement therefore, making them ideal for beginners who want to explore the field of Machine Learning.

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Understanding Associative Classification in Data Mining

Pickl AI

It identifies hidden patterns in data, making it useful for decision-making across industries. Compared to decision trees and SVM, it provides interpretable rules but can be computationally intensive. Key applications include fraud detection, customer segmentation, and medical diagnosis.