AWS Machine Learning Blog

FEBRUARY 25, 2025

Instead of relying on predefined, rigid definitions, our approach follows the principle of understanding a set. Its important to note that the learned definitions might differ from common expectations. Instead of relying solely on compressed definitions, we provide the model with a quasi-definition by extension.

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Smart Data Collective

JUNE 7, 2019

For centuries before the existence of computers, humans have imagined intelligent machines that were capable of making decisions autonomously. At the early era of Artificial Intelligence, programmers tried to teach machines from the definition of logical rules that the machine itself could extend during the execution of the program.

Machine Learning 101

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Towards AI

APRIL 14, 2023

In the second part, I will present and explain the four main categories of XML algorithms along with some of their limitations. However, typical algorithms do not produce a binary result but instead, provide a relevancy score for which labels are the most appropriate. Thus tail labels have an inflated score in the metric.

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How to Learn Machine Learning

MARCH 25, 2025

Let us now look at the key differences starting with their definitions and the type of data they use. Definition of Supervised Learning and Unsupervised Learning Supervised learning is a process where an ML model is trained using labeled data. After training, the machine learning model can predict outcomes for new, unseen data.

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Towards AI

FEBRUARY 20, 2024

Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction Everyone is using mobile or web applications which are based on one or other machine learning algorithms. You might be using machine learning algorithms from everything you see on OTT or everything you shop online.

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Chatbots Life

APRIL 16, 2024

In this article, we will delve into the concepts of generative and discriminative models, exploring their definitions, working principles, and applications. Examples of Generative Models Generative models encompass various algorithms that capture patterns in data to generate realistic new examples.

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PyImageSearch

NOVEMBER 13, 2023

One of the most popular deep learning-based object detection algorithms is the family of R-CNN algorithms, originally introduced by Girshick et al. Since then, the R-CNN algorithm has gone through numerous iterations, improving the algorithm with each new publication and outperforming traditional object detection algorithms (e.g.,

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How to Learn Machine Learning

DECEMBER 15, 2024

Machine Learning by Stanford University (Andrew Ng) This legendary program, taught by the AI pioneer Andrew Ng , is often considered the definitive introduction to machine learning. This professional certificate provides a holistic approach to machine learning, combining theoretical knowledge with practical skills.

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Mlearning.ai

APRIL 13, 2023

Before we discuss the above related to kernels in machine learning, let’s first go over a few basic concepts: Support Vector Machine , S upport Vectors and Linearly vs. Non-linearly Separable Data. Machine learning algorithms rely on mathematical functions called “kernels” to make predictions based on input data.

Machine Learning 52

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Pickl AI

JANUARY 29, 2024

Summary: In the tech landscape of 2024, the distinctions between Data Science and Machine Learning are pivotal. Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. Markets for each field are booming, offering diverse job roles, especially in Machine Learning for Data Analytics.

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How to Learn Machine Learning

JUNE 9, 2024

The thought of machine learning and AI will definitely pop into your mind when the conversation is about emerging technologies. Today, we see tools and systems with machine-learning capabilities in almost every industry. It dramatically shortens computing times for complex algorithms. Isn’t it so?

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Pickl AI

JULY 12, 2024

Their interactive nature makes them suitable for experimenting with AI algorithms and analysing data. Understanding the Basics of AI Artificial Intelligence (AI) represents the capability of machines to imitate intelligent human behaviour. Python offers a robust ecosystem for implementing various supervised learning algorithms.

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Pickl AI

NOVEMBER 18, 2024

Key Takeaways Machine Learning Models are vital for modern technology applications. Key steps involve problem definition, data preparation, and algorithm selection. Ethical considerations are crucial in developing fair Machine Learning solutions. Let’s break down the key components and types of Machine Learning.

Machine Learning 52

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Mlearning.ai

MAY 23, 2023

An interdisciplinary field that constitutes various scientific processes, algorithms, tools, and machine learning techniques working to help find common patterns and gather sensible insights from the given raw input data using statistical and mathematical analysis is called Data Science. What is Data Science?

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Pickl AI

JULY 26, 2023

Understanding these concepts is paramount for any data scientist, machine learning engineer, or researcher striving to build robust and accurate models. K-Nearest Neighbors with Small k I n the k-nearest neighbours algorithm, choosing a small value of k can lead to high variance.

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Pickl AI

MAY 31, 2023

Explore Machine Learning with Python: Become familiar with prominent Python artificial intelligence libraries such as sci-kit-learn and TensorFlow. Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines.

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