Towards AI

DECEMBER 18, 2024

Originally published on Towards AI. In Part 2, we compared Contextual Bandits to Supervised Learning, highlighting the advantages of adaptive optimization over static learning. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI.

Machine Learning 105

Machine Learning Machine Learning Supervised Learning AI 105

Data Science Dojo

MAY 27, 2024

In the recent discussion and advancements surrounding artificial intelligence, there’s a notable dialogue between discriminative and generative AI approaches. These methodologies represent distinct paradigms in AI, each with unique capabilities and applications. What is Generative AI?

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K-nearest Neighbors Supervised Learning AI AI 338

Dataconomy

APRIL 27, 2022

Contrary to popular belief, the history of machine learning, which enables machines to learn tasks for which they are not specifically programmed, and train themselves in unfamiliar environments, goes back to 17th century. Machine learning is a powerful tool for implementing artificial intelligence technologies.

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Towards AI

FEBRUARY 26, 2025

Last Updated on February 26, 2025 by Editorial Team Author(s): Aleti Adarsh Originally published on Towards AI. Ever Wondered How AI Learns? Have you ever looked at AI models and thought, How the heck does this thing actually learn? Join thousands of data leaders on the AI newsletter. Lets Break It Down!

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Data Science Dojo

JANUARY 30, 2025

Artificial intelligence (AI) has transformed industries, but its large and complex models often require significant computational resources. Traditionally, AI models have relied on cloud-based infrastructure, but this approach often comes with challenges such as latency, privacy concerns, and reliance on a stable internet connection.

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Towards AI

MAY 15, 2025

Author(s): Mehul Ligade Originally published on Towards AI. Regression vs Classification in Machine Learning Why Most Beginners Get This Wrong | M004 If youre learning Machine Learning and think supervised learning is straightforward, think again. Understand Problems. More than once.

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Dataconomy

MAY 4, 2023

Machine learning applications in healthcare are rapidly advancing, transforming the way medical professionals diagnose, treat, and prevent diseases. In this rapidly evolving field, machine learning is poised to drive significant advancements in healthcare, improving patient outcomes and enhancing the overall healthcare experience.

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Data Science Dojo

JUNE 1, 2023

Machine learning courses are not just a buzzword anymore; they are reshaping the careers of many people who want their breakthrough in tech. From revolutionizing healthcare and finance to propelling us towards autonomous systems and intelligent robots, the transformative impact of machine learning knows no bounds.

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Analytics Vidhya

APRIL 19, 2023

Meta AI has announced the launch of DinoV2, an open-source, self-supervised learning model. It is a vision transformer model for computer vision tasks, built upon the success of its predecessor, DINO.

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Pickl AI

FEBRUARY 17, 2025

Summary: Machine Learning’s key features include automation, which reduces human involvement, and scalability, which handles massive data. Introduction: The Reality of Machine Learning Consider a healthcare organisation that implemented a Machine Learning model to predict patient outcomes based on historical data.

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Analytics Vidhya

SEPTEMBER 27, 2023

Introduction In recent years, the integration of Artificial Intelligence (AI), specifically Natural Language Processing (NLP) and Machine Learning (ML), has fundamentally transformed the landscape of text-based communication in businesses.

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Data Science Dojo

AUGUST 19, 2024

The demand for AI scientist is projected to grow significantly in the coming years, with the U.S. AI researcher role is consistently ranked among the highest-paying jobs, attracting top talent and driving significant compensation packages. What is the bias-variance trade-off, and how do you address it in machine learning models?

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Pickl AI

JANUARY 21, 2025

Summary: Machine Learning algorithms enable systems to learn from data and improve over time. Introduction Machine Learning algorithms are transforming the way we interact with technology, making it possible for systems to learn from data and improve over time without explicit programming.

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Dataconomy

AUGUST 31, 2023

Classification in machine learning involves the intriguing process of assigning labels to new data based on patterns learned from training examples. Machine learning models have already started to take up a lot of space in our lives, even if we are not consciously aware of it. Of course not.

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Data Science Blog

FEBRUARY 23, 2023

1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves.

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Pickl AI

APRIL 13, 2025

Summary: Classifier in Machine Learning involves categorizing data into predefined classes using algorithms like Logistic Regression and Decision Trees. Introduction Machine Learning has revolutionized how we process and analyse data, enabling systems to learn patterns and make predictions.

Machine Learning 52

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Towards AI

JANUARY 11, 2024

Last Updated on January 12, 2024 by Editorial Team Author(s): Davide Nardini Originally published on Towards AI. Arguably, one of the most important concepts in machine learning is classification. This article will illustrate the difference between classification and regression in machine learning.

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Towards AI

FEBRUARY 3, 2025

Author(s): Shenggang Li Originally published on Towards AI. Photo by Agence Olloweb on Unsplash Machine learning model selection has always been a challenge. Inspired by its reinforcement learning (RL)-based optimization, I wondered: can we apply a similar RL-driven strategy to supervised learning?

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How to Learn Machine Learning

MAY 2, 2025

Welcome to this comprehensive guide on Azure Machine Learning , Microsoft’s powerful cloud-based platform that’s revolutionizing how organizations build, deploy, and manage machine learning models. This is where Azure Machine Learning shines by democratizing access to advanced AI capabilities.

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How to Learn Machine Learning

MAY 14, 2025

Welcome to another exciting tutorial on building your machine learning skills! Just like how a great machine learning model needs key features to perform well, Y2Mate comes packed with everything you need: Lightning-Fast Processing : Y2Mate converts videos quicker than backpropagation in a simple neural network!

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AWS Machine Learning Blog

JANUARY 31, 2025

Increasingly, FMs are completing tasks that were previously solved by supervised learning, which is a subset of machine learning (ML) that involves training algorithms using a labeled dataset. George Lee is AVP, Data Science & Generative AI Lead for International at Travelers Insurance.

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IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. What is machine learning? temperature, salary).

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Dataconomy

SEPTEMBER 13, 2023

AI annotation jobs are on the rise; naturally, people started asking what exactly is data annotation. AI annotation jobs: What is data annotation? These labels provide crucial context for machine learning models, enabling them to make informed decisions and predictions. Image Credit ) Why does data annotation matter?

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Dataconomy

MAY 29, 2023

In the field of AI and ML, QR codes are incredibly helpful for improving predictive analytics and gaining insightful knowledge from massive data sets. So let’s start with the understanding of QR Codes, Artificial intelligence, and Machine Learning.

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Towards AI

JANUARY 20, 2025

Last Updated on January 20, 2025 by Editorial Team Author(s): Shenggang Li Originally published on Towards AI. By integrating Bayesian probability, state-space modeling, and neural network structures, BSSNN provides a flexible and insightful approach to machine learning. Join thousands of data leaders on the AI newsletter.

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Towards AI

APRIL 7, 2024

Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI. You just want to create and analyze simple maps not to learn algebra all over again. This function can be improved by AI and ML, which allow GIS to produce insights, automate procedures, and learn from data. Types of Machine Learning for GIS 1.

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NYU Center for Data Science

NOVEMBER 3, 2023

Self-supervised learning (SSL) has emerged as a powerful technique for training deep neural networks without extensive labeled data. However, unlike supervised learning, where labels help identify relevant information, the optimal SSL representation heavily depends on assumptions made about the input data and desired downstream task.

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Data Science Dojo

FEBRUARY 19, 2024

As part of the generative AI world, LLMs have led to innovation in machine-learning tasks. Roadmap to understanding an LLM project lifecycle Within the realm of generative AI, a project involving large language models can be a daunting task. It requires machine-learning expertise, computational resources, and time.

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Pickl AI

APRIL 22, 2025

Summary: Multilayer Perceptron in machine learning (MLP) is a powerful neural network model used for solving complex problems through multiple layers of neurons and nonlinear activation functions. Activation Function: The activation function introduces nonlinearity, enabling the network to learn complex mappings.

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AWS Machine Learning Blog

SEPTEMBER 8, 2023

This post is co-written with Travis Bronson, and Brian L Wilkerson from Duke Energy Machine learning (ML) is transforming every industry, process, and business, but the path to success is not always straightforward. Finally, there is no labeled data available for training a supervised machine learning model.

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Dataconomy

MARCH 12, 2025

Machine teaching is redefining how we interact with artificial intelligence (AI) and machine learning (ML). Machine teaching is an innovative approach that empowers users to create and refine machine learning models by providing the necessary context and guidance.

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Towards AI

AUGUST 1, 2023

Last Updated on August 2, 2023 by Editorial Team Author(s): Boris Meinardus Originally published on Towards AI. How the DINO framework achieved the new SOTA for Self-Supervised Learning! Transformers and Self-Supervised Learning. This way, the model will learn about different objects in a balanced way.

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Towards AI

FEBRUARY 20, 2024

Last Updated on February 20, 2024 by Editorial Team Author(s): Vaishnavi Seetharama Originally published on Towards AI. 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.

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Pickl AI

OCTOBER 9, 2023

However, with the emergence of Machine Learning algorithms, the retail industry has seen a revolutionary shift in demand forecasting capabilities. This technology allows computers to learn from historical data, identify patterns, and make data-driven decisions without explicit programming.

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Towards AI

MAY 1, 2024

Last Updated on May 1, 2024 by Editorial Team Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI. Created by the author with DALL E-3 R has become very ideal for GIS, especially for GIS machine learning as it has topnotch libraries that can perform geospatial computation.

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NYU Center for Data Science

SEPTEMBER 13, 2024

The world of multi-view self-supervised learning (SSL) can be loosely grouped into four families of methods: contrastive learning, clustering, distillation/momentum, and redundancy reduction. The exploration of MMCR is far from over.

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Dataconomy

MAY 22, 2023

But what exactly is distributed learning in machine learning? In this article, we will explore the concept of distributed learning and its significance in the realm of machine learning. Why is it so important? This process is often referred to as training or model optimization.

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