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Top 10 blogs on NLP in Analytics Vidhya 2022

Analytics Vidhya

Introduction Natural language processing (NLP) is a field of computer science and artificial intelligence that focuses on the interaction between computers and human (natural) languages.

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Meet the finalists of the Pushback to the Future Challenge

DrivenData Labs

He is interested in researching human cognition and computational methods for modeling the brain. Nika Chuzhoy is a first-year undergraduate student at Caltech majoring in Computer Science. Her primary interests lie in theoretical machine learning. Dr. Martine De C**k is a Professor with expertise in machine learning.

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Scaling Kaggle Competitions Using XGBoost: Part 3

PyImageSearch

In this tutorial, you will learn about Gradient Boosting, the final precursor to XGBoost. Jump Right To The Downloads Section Scaling Kaggle Competitions Using XGBoost: Part 3 Gradient Boost at a Glance In the first blog post of this series, we went through basic concepts like ensemble learning and decision trees.

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From Pixels to Places: Harnessing Geospatial Data with Machine Learning.

Towards AI

Machine learning (ML) has proven that it is here with us for the long haul, everyone who had their doubts by calling it a phase should by now realize how wrong they are, ML has being used in various sector’s of society such as medicine, geospatial data, finance, statistics and robotics.

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Five machine learning types to know

IBM Journey to AI blog

What is machine learning? ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions. Here, we’ll discuss the five major types and their applications.

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. TensorFlow and Keras: TensorFlow is an open-source platform for machine learning.

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Scaling Kaggle Competitions Using XGBoost: Part 4

PyImageSearch

The reasoning behind that is simple; whatever we have learned till now, be it adaptive boosting, decision trees, or gradient boosting, have very distinct statistical foundations which require you to get your hands dirty with the math behind them. Or requires a degree in computer science? That’s not the case.