AWS Machine Learning Blog

FEBRUARY 25, 2025

Classification algorithms like support vector machines (SVMs) are especially well-suited to use this implicit geometry of the data. Diego Martn Montoro is an AI Expert and Machine Learning Engineer at Applus+ Idiada Datalab. This doesnt imply that clusters coudnt be highly separable in higher dimensions.

Algorithm 74

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Pickl AI

JANUARY 29, 2024

AI comprises Natural Language Processing, computer vision, and robotics. ML focuses on algorithms like decision trees, neural networks, and support vector machines for pattern recognition. Requires a blend of computer science, mathematics, and domain-specific knowledge, often involving complex algorithms.

Data Science 52

Data Science ML ML Machine Learning 52

Pickl AI

JULY 12, 2024

Natural Language Processing (NLP) This is a field of computer science that deals with the interaction between computers and human language. NLP tasks include machine translation, speech recognition, and sentiment analysis. Popular models include decision trees, support vector machines (SVM), and neural networks.

Artificial Intelligence 52

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Dataconomy

FEBRUARY 24, 2023

Several algorithms are available, including decision trees, neural networks, and support vector machines. The field of computer science known as “artificial intelligence” (AI) focuses on creating intelligent machines that can accomplish jobs that would normally need human intelligence.

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Artificial Intelligence Artificial Intelligence AI AI 91

Heartbeat

OCTOBER 25, 2023

Empowering Data Scientists and Machine Learning Engineers in Advancing Biological Research Image from European Bioinformatics Institute Introduction: In biological research, the fusion of biology, computer science, and statistics has given birth to an exciting field called bioinformatics.

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Dataconomy

MARCH 13, 2023

How to create an artificial intelligence: Building accurate and efficient AI systems requires selecting the right algorithms and models that can perform the desired tasks effectively Developing AI Developing AI involves a series of steps that require expertise in several fields, such as data science, computer science, and engineering.

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PyImageSearch

NOVEMBER 13, 2023

Step #3: Use transfer learning, specifically feature extraction, to compute features for each proposal (effectively an ROI) using the pre-trained CNN. Step #4: Classify each proposal using the extracted features with a Support Vector Machine (SVM). Or requires a degree in computer science?

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Pickl AI

SEPTEMBER 12, 2024

Artificial Intelligence (AI): A branch of computer science focused on creating systems that can perform tasks typically requiring human intelligence. Association Rule Learning: A rule-based Machine Learning method to discover interesting relationships between variables in large databases.

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Pickl AI

SEPTEMBER 23, 2024

Understanding Data Science Data Science is a multidisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines principles from statistics, mathematics, computer science, and domain-specific knowledge to analyse and interpret complex data.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Data Science Machine Learning 52

PyImageSearch

JANUARY 2, 2023

Deep neural networks and support vector machines are being explored in developing pre-diabetic screening tools. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Or requires a degree in computer science? Diabetic Retinopathy, see Figure 9 ).

Deep Learning 52

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Flipboard

JULY 31, 2023

In their debut paper, they used a support-vector machine and only messed up 0.8% Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Or requires a degree in computer science? of the time. Not too shabby, right? That’s not the case.

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PyImageSearch

SEPTEMBER 16, 2024

For example, in fraud detection, SVM (support vector machine) can classify transactions as fraudulent or non-fraudulent based on historically labeled data. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Or requires a degree in computer science?

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