Dataconomy

APRIL 14, 2025

By leveraging the similarity between instances, IBL provides a unique perspective on how algorithms can adapt and learn from previously encountered data. What is instance-based learning (IBL)? Unsupervised learning: Focuses on extracting patterns from data without pre-labeled responses, identifying inherent structures.

Machine Learning 91

Machine Learning Machine Learning Algorithm Supervised Learning 91

Dataconomy

MARCH 12, 2025

Support Vector Machines (SVM) are a type of supervised learning algorithm designed for classification and regression tasks. Definition of SVM SVMs operate on the principle of finding the hyperplane that maximizes the margin between different classes. What are Support Vector Machines (SVM)?

Support Vector Machines 91

Support Vector Machines Decision Trees Supervised Learning Machine Learning 91

Smart Data Collective

JUNE 7, 2019

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. Although there are many types of learning, Michalski defined the two most common types of learning: Supervised Learning.

Machine Learning 101

Machine Learning Machine Learning Supervised Learning Support Vector Machines 101

Dataconomy

MARCH 12, 2025

Machine teaching is redefining how we interact with artificial intelligence (AI) and machine learning (ML). As industries increasingly adopt AI solutions, professionals without a technical background can now step into the realm of machine learning, leveraging powerful algorithms to automate tasks and improve decision-making.

Machine Learning 91

Machine Learning Machine Learning Algorithm Supervised Learning 91

Dataconomy

MARCH 13, 2025

These sophisticated algorithms facilitate a deeper understanding of data, enabling applications from image recognition to natural language processing. What is deep learning? Deep learning is a subset of artificial intelligence that utilizes neural networks to process complex data and generate predictions.

Deep Learning 91

Deep Learning Deep Learning Natural Language Processing Machine Learning 91

ML @ CMU

APRIL 23, 2025

The paper analyzes two families of self-improvement algorithms: one based on supervised fine-tuning (SFT) and one on reinforcement learning (RLHF). Unlike prior benchmarks, miniCTX includes large repositories with relevant definitions, lemmas, and structures.

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Algorithm Machine Learning Machine Learning AI 170

Explosion

APRIL 1, 2017

Prodigy features many of the ideas and solutions for data collection and supervised learning outlined in this blog post. It’s a cloud-free, downloadable tool and comes with powerful active learning models. Sometimes the unsupervised algorithm will happen to produce the output you want, but other times it won’t.

Supervised Learning 40

Supervised Learning Machine Learning Machine Learning Algorithm 40

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.

Machine Learning 103

Machine Learning Machine Learning ML ML 103

Dataconomy

MARCH 27, 2023

Definition and purpose of RPA Robotic process automation refers to the use of software robots to automate rule-based business processes. Natural language processing (NLP):  ML algorithms can be used to understand and interpret human language, enabling organizations to automate tasks such as customer support and document processing.

ML 133

ML ML Machine Learning Machine Learning 133

The MLOps Blog

MARCH 21, 2023

Your data scientists develop models on this component, which stores all parameters, feature definitions, artifacts, and other experiment-related information they care about for every experiment they run. Building a Machine Learning platform (Lemonade). Design Patterns in Machine Learning for MLOps (by Pier Paolo Ippolito).

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

Applied Data Science

MARCH 11, 2022

Then, we will look at three recent research projects that gamified existing algorithms by converting them from single-agent to multi-agent: ?️‍♀️ Our internal agents are playing games until they learn how to cooperate and trick us into believing we are an individual. Gamification There are many definitions for what a game is.

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AI AI Data Science Machine Learning 130

AWS Machine Learning Blog

MARCH 27, 2025

In this post, we explore how you can use Amazon Bedrock to generate high-quality categorical ground truth data, which is crucial for training machine learning (ML) models in a cost-sensitive environment. When you evaluate a case, evaluate the definitions in order and label the case with the first definition that fits.

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How to Learn Machine Learning

MAY 2, 2025

Learn more from the MLflow with Azure ML documentation. Automated Machine Learning (AutoML) : This feature automates time-consuming tasks like algorithm selection, hyperparameter tuning, and feature engineering. Simply prepare your data, define your target variable, and let AutoML explore various algorithms and hyperparameters.

Azure 52

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Pickl AI

NOVEMBER 18, 2024

Key Takeaways Machine Learning Models are vital for modern technology applications. Types include supervised, unsupervised, and reinforcement learning. Key steps involve problem definition, data preparation, and algorithm selection. Ethical considerations are crucial in developing fair Machine Learning solutions.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Data Science 101

OCTOBER 31, 2019

A definition from the book ‘Data Mining: Practical Machine Learning Tools and Techniques’, written by, Ian Witten and Eibe Frank describes Data mining as follows: “ Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Classification. Anomaly Detection.

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Data Mining Data Mining Data Mining Data Science 77

Pickl AI

JANUARY 2, 2025

Summary: Entropy in Machine Learning quantifies uncertainty, driving better decision-making in algorithms. It optimises decision trees, probabilistic models, clustering, and reinforcement learning. Key Takeaways Entropy measures randomness, guiding algorithms to make better decisions.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Clustering 52

Dataconomy

MARCH 27, 2023

Definition and purpose of RPA Robotic process automation refers to the use of software robots to automate rule-based business processes. Natural language processing (NLP):  ML algorithms can be used to understand and interpret human language, enabling organizations to automate tasks such as customer support and document processing.

ML 70

ML ML Machine Learning Machine Learning 70

Pickl AI

OCTOBER 24, 2024

Summary: This article compares Artificial Intelligence (AI) vs Machine Learning (ML), clarifying their definitions, applications, and key differences. While AI aims to replicate human intelligence across various domains, ML focuses on learning from data to improve performance.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Machine Learning Machine Learning 52

AWS Machine Learning Blog

NOVEMBER 10, 2023

We previously explored a single job optimization, visualized the outcomes for SageMaker built-in algorithm, and learned about the impact of particular hyperparameter values. In this post, we run multiple HPO jobs with a custom training algorithm and different HPO strategies such as Bayesian optimization and random search.

ML 122

ML ML Algorithm Python 122

Pickl AI

JULY 12, 2024

Their interactive nature makes them suitable for experimenting with AI algorithms and analysing data. This section delves into its foundational definitions, types, and critical concepts crucial for comprehending its vast landscape. AI algorithms may produce inaccurate or biased results without clean, relevant, and representative data.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Explosion

AUGUST 3, 2017

Why machine learning systems need annotated examples Most AI systems today rely on supervised learning : you provide labelled input and output pairs, and get a program that can perform analogous computation for new data. By definition, you can’t directly control what the process returns.

Supervised Learning 52

Supervised Learning Python Machine Learning Machine Learning 52

Chatbots Life

MAY 12, 2023

Reinforcement learning is a machine learning training method based on rewarding desired behaviours and punishing undesired ones. Here is a brief description of the algorithm: OpenAI collected prompts submitted by the users to the earlier versions of the model.

AI 90

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Mlearning.ai

NOVEMBER 29, 2023

All the previously, recently, and currently collected data is used as input for time series forecasting where future trends, seasonal changes, irregularities, and such are elaborated based on complex math-driven algorithms. And with machine learning, time series forecasting becomes faster, more precise, and more efficient in the long run.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

ODSC - Open Data Science

FEBRUARY 14, 2023

The model was fine-tuned to reduce false, harmful, or biased output using a combination of supervised learning in conjunction to what OpenAI calls Reinforcement Learning with Human Feedback (RLHF), where humans rank potential outputs and a reinforcement learning algorithm rewards the model for generating outputs like those that rank highly.

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IBM Journey to AI blog

SEPTEMBER 20, 2023

They use self-supervised learning algorithms to perform a variety of natural language processing (NLP) tasks in ways that are similar to how humans use language (see Figure 1). Large language models (LLMs) have taken the field of AI by storm.

Clustering 95

Clustering AI AI Data Science 95

Pickl AI

SEPTEMBER 17, 2024

Key Terms and Definitions To fully grasp the concepts of dimensionality reduction, it’s essential to understand the key terms and definitions associated with this field. This efficiency is especially valuable when working with large datasets or complex algorithms.

Machine Learning 52

Machine Learning Machine Learning Data Analysis Data Analysis 52

Mlearning.ai

APRIL 16, 2023

It may seem simple linear regression is neglected in the machine learning world of today. It helps to understand higher and more complex algorithms. So, it is important to master this algorithm. In this tutorial, you will learn about the concepts behind simple linear regression. We will also show how to code it in Python.

Machine Learning 52

Machine Learning Machine Learning Python Data Science 52

Pickl AI

FEBRUARY 22, 2024

MicroMasters Program in Statistics and Data Science MIT – edX 1 year 2 months (INR 1,11,739) This program integrates Data Science, Statistics, and Machine Learning basics. The curriculum includes Machine Learning Algorithms and prepares students for roles like Data Scientist, Data Analyst, System Analyst, and Intelligence Analyst.

Data Science 52

Data Science Hypothesis Testing Data Analysis Data Analysis 52

Hacker News

FEBRUARY 14, 2023

And many of the practical challenges around neural nets—and machine learning in general—center on acquiring or preparing the necessary training data. In many cases (“supervised learning”) one wants to get explicit examples of inputs and the outputs one is expecting from them. First comes the embedding module.

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Defined.ai blog

SEPTEMBER 14, 2022

Currently, most models are trained via supervised learning, which relies on well-annotated data from humans to create training examples. Data annotation is especially important when considering the amount of unstructured data that exists in the form of text, images, video, and audio.

Machine Learning 52

Machine Learning Machine Learning Supervised Learning Data Preparation 52

Snorkel AI

FEBRUARY 17, 2023

In the beginning, we looked at the binary classification problem of how do you find label errors in data and how do you learn? How do you train machine learning algorithms generally for any data set? Then we generalized that for the entire field of supervised learning. And for any model for binary classification?

Machine Learning 52

Machine Learning Machine Learning AI AI 52

Snorkel AI

FEBRUARY 17, 2023

In the beginning, we looked at the binary classification problem of how do you find label errors in data and how do you learn? How do you train machine learning algorithms generally for any data set? Then we generalized that for the entire field of supervised learning. And for any model for binary classification?

Machine Learning 52

Machine Learning Machine Learning AI AI 52

Google Research AI blog

JANUARY 18, 2023

Over the next several weeks, we will discuss novel developments in research topics ranging from responsible AI to algorithms and computer systems to science, health and robotics. of high definition video. Let’s get started! The Pix2Seq framework for object detection.

ML 132

ML ML AI AI 132

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. Support Vector Machine Support Vector Machine ( SVM ) is a supervised learning algorithm used for classification and regression analysis.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Support Vector Machines 52

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.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

Snorkel AI

JANUARY 16, 2024

As humans, we learn a lot of general stuff through self-supervised learning by just experiencing the world. It’s not just about accuracy, and it’s definitely not just about one test set. We’re definitely getting there. DK: Absolutely, I think that’s a perfect metaphor. DK: For sure.

AI 52

AI AI Supervised Learning Algorithm 52

Snorkel AI

JANUARY 16, 2024

As humans, we learn a lot of general stuff through self-supervised learning by just experiencing the world. It’s not just about accuracy, and it’s definitely not just about one test set. We’re definitely getting there. DK: Absolutely, I think that’s a perfect metaphor. DK: For sure.

Supervised Learning 52

Supervised Learning AI AI Algorithm 52