Smart Data Collective

JUNE 4, 2021

Basics of Machine Learning. Machine learning is the science of building models automatically. It is a branch of artificial intelligence. Whereas in machine learning, the algorithm understands the data and creates the logic. Machine learning is broadly classified into three types – Supervised.

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PyImageSearch

MARCH 30, 2023

What is Machine Learning A Closer Look at Machine Learning Machine learning (ML) revolves around creating algorithms and related systems that learn behavior strategies in specific environments using instructions and training data sets.

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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. That is, is giving supervision to adjust via.

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IBM Journey to AI blog

DECEMBER 20, 2023

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

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Women in Big Data

DECEMBER 13, 2023

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that helps computers understand, interpret and manipulate human language. Computational Linguistics is rule based modeling of natural languages. Process of NLP: What is a language model and what are Large Language Models (LLMs)?

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Heartbeat

OCTOBER 25, 2023

The development of explainable and interpretable machine learning models will enhance the trustworthiness of predictions and enable researchers to gain deeper insights into the underlying biological mechanisms. Clustering algorithms can group similar biological samples or identify distinct subtypes within a disease.

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Pickl AI

MARCH 13, 2023

INTRODUCTION Machine Learning is a subfield of artificial intelligence that focuses on the development of algorithms and models that allow computers to learn and make predictions or decisions based on data, without being explicitly programmed. WHAT IS CLUSTERING? Those groups are referred to as clusters.

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ODSC - Open Data Science

APRIL 24, 2023

Botnet Detection at Scale — Lessons Learned From Clustering Billions of Web Attacks Into Botnets Editor’s note: Ori Nakar is a speaker for ODSC Europe this June. Be sure to check out his talk, “ Botnet detection at scale — Lesson learned from clustering billions of web attacks into botnets ,” there!

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Pickl AI

JULY 17, 2023

Machine Learning is a subset of artificial intelligence (AI) that focuses on developing models and algorithms that train the machine to think and work like a human. Unsupervised Learning Algorithms Unsupervised Learning Algorithms tend to perform more complex processing tasks in comparison to supervised learning.

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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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Pickl AI

APRIL 26, 2024

Machine learning is a subset of artificial intelligence that enables computers to learn from data and improve over time without being explicitly programmed. Explain the difference between supervised and unsupervised learning. In traditional programming, the programmer explicitly defines the rules and logic.

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Snorkel AI

SEPTEMBER 29, 2023

Some machine learning algorithms, such as clustering and self-supervised learning , do not require data labels, but their direct business applications are limited. Use cases for supervised machine learning models, on the other hand, cover many business needs.

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IBM Journey to AI blog

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. Artificial intelligence is the overarching system. Machine learning is a subset of AI.

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Dataconomy

APRIL 4, 2023

Artificial intelligence (AI) is a broad term that encompasses the ability of computers and machines to perform tasks that normally require human intelligence, such as reasoning, learning, decision-making, and problem-solving. An AI model is a crucial part of artificial intelligence. What is an AI model?

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Dataconomy

APRIL 4, 2023

Artificial intelligence (AI) is a broad term that encompasses the ability of computers and machines to perform tasks that normally require human intelligence, such as reasoning, learning, decision-making, and problem-solving. An AI model is a crucial part of artificial intelligence. What is an AI model?

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Towards AI

APRIL 4, 2024

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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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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IBM Journey to AI blog

SEPTEMBER 20, 2023

Foundational models (FMs) are marking the beginning of a new era in machine learning (ML) and artificial intelligence (AI) , which is leading to faster development of AI that can be adapted to a wide range of downstream tasks and fine-tuned for an array of applications.

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NYU Center for Data Science

NOVEMBER 15, 2023

Ferretti, Nana Liu, Animashree Anandkumar, Noah Brenowitz, Veronika Eyring, Nicholas Geneva, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Akshay Subramaniam, Carl Vondrick, Rose Yu, *Laure Zanna (affiliated professor), Tian Zheng, Ryan Abernathey, Fiaz Ahmed, David Bader, Pierre Baldi, Elizabeth Barnes, Christopher Bretherton, Peter Caldwell, Wayne (..)

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Towards AI

APRIL 18, 2023

Building disruptive Computer Vision applications with No Fine-Tuning Imagine a world where computer vision models could learn from any set of images without relying on labels or fine-tuning. Understanding DINOv2 DINOv2 is a cutting-edge method for training computer vision models using self-supervised learning.

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Becoming Human

MAY 15, 2023

Machine Learning Machine learning is a type of artificial intelligence that allows software applications to learn from the data and become more accurate over time. Unsupervised Learning In this type of learning, the algorithm is trained on an unlabeled dataset, where no correct output is provided.

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Dataconomy

MAY 22, 2023

By dividing the workload and data across multiple nodes, distributed learning enables parallel processing, leading to faster and more efficient training of machine learning models. There are various types of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning.

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Mlearning.ai

JANUARY 31, 2023

It turned out that a better solution was to annotate data by using a clustering algorithm, in particular, I chose the popular K-means. While SVM is a supervised machine learning classifier, this one belongs to the family of unsupervised learning algorithms. Machine learning would be a lot easier otherwise.

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Google Research AI blog

JULY 23, 2023

Elsayed , Aravindh Mahendran , Fisher Yu , Avital Oliver , Fantine Huot , Jasmijn Bastings , Mark Patrick Collier , Alexey Gritsenko , Vighnesh Birodkar , Cristina Vasconcelos , Yi Tay , Thomas Mensink , Alexander Kolesnikov , Filip Pavetić , Dustin Tran , Thomas Kipf , Mario Lučić , Xiaohua Zhai , Daniel Keysers , Jeremiah Harmsen , Neil Houlsby Fast (..)

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Mlearning.ai

DECEMBER 13, 2023

What are the key differences between supervised and unsupervised learning in the context of deep learning? Supervised Learning: You have labeled data, and the model learns to predict the label. For example, you have pictures of cats and dogs, and the model learns to tell which is which.

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AWS Machine Learning Blog

MAY 30, 2023

Gradient boosting is a supervised learning algorithm that attempts to accurately predict a target variable by combining an ensemble of estimates from a set of simpler and weaker models. He focuses on developing scalable machine learning algorithms. Tony Cruz

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Flipboard

DECEMBER 6, 2023

The model then uses a clustering algorithm to group the sentences into clusters. The sentences that are closest to the center of each cluster are selected to form the summary. For example, “You are given the following article about Artificial Intelligence and its role in Healthcare: [input text].”

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The MLOps Blog

MARCH 21, 2023

Orchestrators are concerned with lower-level abstractions like machines, instances, clusters, service-level grouping, replication, and so on. You can read this article to learn how to choose a data labeling tool. Leveraging Unlabeled Image Data With Self-Supervised Learning or Pseudo Labeling With Mateusz Opala.

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