Dataconomy

JULY 24, 2023

To put it another way, a data scientist turns raw data into meaningful information using various techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. Machine learning Machine learning is a key part of data science.

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IBM Journey to AI blog

JULY 6, 2023

Machine learning works on a known problem with tools and techniques, creating algorithms that let a machine learn from data through experience and with minimal human intervention. Because the datasets are unstructured, though, it can be complicated and time-consuming to interpret the data for decision-making.

Machine Learning 59

Machine Learning Machine Learning Data Science Big Data 59

Dataconomy

MARCH 13, 2023

Algorithms:  AI algorithms are used to process the data and extract insights from it. There are several types of AI algorithms, including supervised learning, unsupervised learning, and reinforcement learning. Develop AI models using machine learning or deep learning algorithms.

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Heartbeat

SEPTEMBER 13, 2023

Algorithmic Accountability: Explainability ensures accountability in machine learning and AI systems. It allows developers, auditors, and regulators to examine the decision-making processes of the models, identify potential biases or errors, and assess their compliance with ethical guidelines and legal requirements.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

Towards AI

APRIL 4, 2024

Created by the author with DALL E-3 Machine learning algorithms are the “cool kids” of the tech industry; everyone is talking about them as if they were the newest, greatest meme. Shall we unravel the true meaning of machine learning algorithms and their practicability?

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K-nearest Neighbors Machine Learning Machine Learning Decision Trees 70

IBM Journey to AI blog

DECEMBER 20, 2023

Each type and sub-type of ML algorithm has unique benefits and capabilities that teams can leverage for different tasks. 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. What is machine learning?

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Machine Learning Machine Learning Supervised Learning Clustering 81

PyImageSearch

JANUARY 16, 2023

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. To recap: ensemble learners are normally a group of weak algorithms working together to produce quality output.

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Decision Trees Deep Learning Deep Learning Algorithm 102

Pickl AI

MAY 10, 2023

Machine Learning is a subset of Artificial Intelligence and Computer Science that makes use of data and algorithms to imitate human learning and improving accuracy. Being an important component of Data Science, the use of statistical methods are crucial in training algorithms in order to make classification.

Clustering 52

Clustering Decision Trees Machine Learning Machine Learning 52

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.

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Natural Language Processing Machine Learning Machine Learning Deep Learning 52

Dataconomy

FEBRUARY 24, 2023

Choose the appropriate algorithm: Select the AI algorithm that best suits the problem you want to solve. Several algorithms are available, including decision trees, neural networks, and support vector machines. This involves feeding the algorithm with data and tweaking it to improve its accuracy. What is AI?

Artificial Intelligence 91

Artificial Intelligence Artificial Intelligence AI AI 91

PyImageSearch

JANUARY 23, 2023

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. We use 500 trees, with a value of 0 and a maximum depth of each tree of 5.

Deep Learning 107

Deep Learning Deep Learning Algorithm Decision Trees 107

Chatbots Life

SEPTEMBER 11, 2023

Natural Language Processing (NLP) is an interdisciplinary field that combines the expertise of linguistics, computer science, and artificial intelligence to enable computers to process and comprehend human language. This post will delve into the core components and algorithms that drive our NLP-based spell checker.

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Natural Language Processing Python ML ML 59

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.

Decision Trees 52

Decision Trees Deep Learning Deep Learning Exploratory Data Analysis 52