PyImageSearch

JANUARY 23, 2023

Applying XGBoost on a Problem Statement Applying XGBoost to Our Dataset Summary Citation Information Scaling Kaggle Competitions Using XGBoost: Part 4 Over the last few blog posts of this series, we have been steadily building up toward our grand finish: deciphering the mystery behind eXtreme Gradient Boosting (XGBoost) itself.

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IBM Journey to AI blog

JULY 6, 2023

Because the datasets are unstructured, though, it can be complicated and time-consuming to interpret the data for decision-making. That’s where data science comes in. The term data science was first used in the 1960s when it was interchangeable with the phrase “computer science.”

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

AWS Machine Learning Blog

OCTOBER 30, 2024

Solution overview In this post, we demonstrate how to fine-tune a sentence transformer with Amazon product data and how to use the resulting sentence transformer to improve classification accuracy of product categories using an XGBoost decision tree.

Machine Learning 109

Machine Learning Machine Learning AWS Data Scientist 109

PyImageSearch

DECEMBER 12, 2022

We went through the core essentials required to understand XGBoost, namely decision trees and ensemble learners. Since we have been dealing with trees, we will assume that our adaptive boosting technique is being applied to decision trees. Or requires a degree in computer science? That’s not the case.

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AWS Machine Learning Blog

SEPTEMBER 10, 2024

The remaining features are horizontally appended to the pathology features, and a gradient boosted decision tree classifier (LightGBM) is applied to achieve predictive analysis. Tamas helped customers in the Healthcare and Life Science vertical to innovate through the adoption of Machine Learning.

Supervised Learning 92

Supervised Learning Machine Learning Machine Learning AWS 92

Heartbeat

SEPTEMBER 13, 2023

Cynthia Rudin, a computer science professor at Duke University, emphasized the difference between interpretability and explainability. The scholar, in her work , opines that: Interpretability is about understanding how the model works, whereas explainability involves providing justifications for specific predictions or decisions.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

PyImageSearch

OCTOBER 21, 2024

On Lines 21-27 , we define a Node class, which represents a node in a decision tree. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Or requires a degree in computer science? We first start by defining the Node of an iTree. That’s not the case.

Algorithm 110

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Heartbeat

FEBRUARY 8, 2023

Source: Author The field of natural language processing (NLP), which studies how computer science and human communication interact, is rapidly growing. Natural Language Processing (NLP) plays a crucial role in advancing research in various fields, such as computational linguistics, computer science, and artificial intelligence.

Natural Language Processing 52

Natural Language Processing Machine Learning Machine Learning Deep Learning 52

Chatbots Life

SEPTEMBER 11, 2023

In this blog post, we will explore the development of a state-of-the-art spell and grammar checker utilising NLP techniques, highlighting its ability to surpass conventional rule-based systems and deliver a more seamless user experience in the digital age of communication. Please get in touch for more details here.

Natural Language Processing 59

Natural Language Processing Python ML ML 59

Pickl AI

SEPTEMBER 23, 2024

Summary: The blog explores the synergy between Artificial Intelligence (AI) and Data Science, highlighting their complementary roles in Data Analysis and intelligent decision-making. Introduction Artificial Intelligence (AI) and Data Science are revolutionising how we analyse data, make decisions, and solve complex problems.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Machine Learning 52

Towards AI

APRIL 24, 2025

We begin with a clear, approachable guide to Python and core computer science concepts ideal if youre just starting out or brushing up on the basics. It covered decision tree theory, including Gini impurity, and offered a practical guide using sklearn with a bank marketing dataset. But from there, things go deeper.

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