Analytics Vidhya

MAY 27, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Machine learning algorithms are classified into three types: supervised learning, The post K-Means Clustering Algorithm with R: A Beginner’s Guide. appeared first on Analytics Vidhya.

Clustering 304

Clustering Algorithm Supervised Learning Machine Learning 304

Analytics Vidhya

JANUARY 5, 2022

Types of Machine Learning Algorithms 3. Machine Learning […]. The post Machine Learning Algorithms appeared first on Analytics Vidhya. Introduction 2. Simple Linear Regression 4. Multilinear Regression 5. Logistic Regression 6. Decision Tree 7.

Machine Learning 382

Machine Learning Machine Learning Algorithm Decision Trees 382

Analytics Vidhya

NOVEMBER 23, 2021

Introduction to Classification Algorithms In this article, we shall analyze loan risk using 2 different supervised learning classification algorithms. These algorithms are decision trees and random forests. This article was published as a part of the Data Science Blogathon.

Machine Learning 399

Machine Learning Machine Learning Decision Trees Supervised Learning 399

KDnuggets

MARCH 21, 2022

Linear Regression and Logistic Regression are two well-used Machine Learning Algorithms that both branch off from Supervised Learning. Linear Regression is used to solve Regression problems whereas Logistic Regression is used to solve Classification problems. Read more here.

Supervised Learning 388

Supervised Learning Machine Learning Machine Learning Algorithm 388

KDnuggets

JULY 20, 2022

14 Essential Git Commands for Data Scientists • Statistics and Probability for Data Science • 20 Basic Linux Commands for Data Science Beginners • 3 Ways Understanding Bayes Theorem Will Improve Your Data Science • Learn MLOps with This Free Course • Primary Supervised Learning Algorithms Used in Machine Learning • Data Preparation with SQL Cheatsheet. (..)

Data Science 385

Data Science Supervised Learning Data Preparation Data Scientist 385

KDnuggets

JUNE 20, 2022

Also: Decision Tree Algorithm, Explained; 15 Python Coding Interview Questions You Must Know For Data Science; Naïve Bayes Algorithm: Everything You Need to Know; Primary Supervised Learning Algorithms Used in Machine Learning.

Decision Trees 323

Decision Trees Supervised Learning Data Scientist Machine Learning 323

Analytics Vidhya

SEPTEMBER 20, 2021

This article was published as a part of the Data Science Blogathon Introduction In this article, I am going to discuss the math intuition behind the Gradient boosting algorithm. The post Gradient Boosting Algorithm: A Complete Guide for Beginners appeared first on Analytics Vidhya.

Algorithm 333

Algorithm Data Science Analytics Analytics 333

Analytics Vidhya

MAY 20, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction This article aims to explain deep learning and some supervised. The post Introduction to Supervised Deep Learning Algorithms! appeared first on Analytics Vidhya.

Deep Learning 307

Deep Learning Deep Learning Algorithm Data Science 307

Dataconomy

APRIL 24, 2025

Just like chemical elements fall into predictable groups, the researchers claim that machine learning algorithms also form a pattern. A state-of-the-art image classification algorithm requiring zero human labels. This ballroom analogy extends to all of machine learning. It predicts new ones. One such prediction?

Machine Learning 177

Machine Learning Machine Learning Clustering Algorithm 177

Analytics Vidhya

MAY 15, 2021

The post K-Nearest Neighbour: The Distance-Based Machine Learning Algorithm. ArticleVideo Book This article was published as a part of the Data Science Blogathon. Introduction The abbreviation KNN stands for “K-Nearest Neighbour” It is. appeared first on Analytics Vidhya.

Machine Learning 309

Machine Learning Machine Learning Algorithm Data Science 309

Analytics Vidhya

JUNE 3, 2022

Introduction Classification problems are often solved using supervised learning algorithms such as Random Forest Classifier, Support Vector Machine, Logistic Regressor (for binary class classification) etc. This article was published as a part of the Data Science Blogathon.

Support Vector Machines 297

Support Vector Machines Supervised Learning Data Science Algorithm 297

Pickl AI

JANUARY 21, 2025

Summary: Machine Learning algorithms enable systems to learn from data and improve over time. These algorithms are integral to applications like recommendations and spam detection, shaping our interactions with technology daily. These intelligent predictions are powered by various Machine Learning algorithms.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Dataconomy

APRIL 29, 2025

Human-in-the-loop machine learning is a methodology that emphasizes the critical role of human feedback in the machine learning lifecycle. Instead of relying solely on automated algorithms, HITL processes involve human experts to validate, refine, and augment the learning models.

Machine Learning 91

Machine Learning Machine Learning Supervised Learning Natural Language Processing 91

Data Science Dojo

DECEMBER 8, 2023

Zero-shot, one-shot, and few-shot learning are redefining how machines adapt and learn, promising a future where adaptability and generalization reach unprecedented levels. Source: Photo by Hal Gatewood on Unsplash In this exploration, we navigate from the basics of supervised learning to the forefront of adaptive models.

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Supervised Learning Machine Learning Machine Learning Artificial Intelligence 446

Data Science Dojo

MAY 27, 2024

A visual representation of generative AI – Source: Analytics Vidhya Generative AI is a growing area in machine learning, involving algorithms that create new content on their own. These algorithms use existing data like text, images, and audio to generate content that looks like it comes from the real world.

K-nearest Neighbors 338

K-nearest Neighbors Supervised Learning AI AI 338

NYU Center for Data Science

JULY 5, 2024

Our study demonstrates that machine supervision significantly improves two crucial medical imaging tasks: classification and segmentation,” said Cirrone, who leads AI efforts at the Colton Center for Autoimmunity at NYU Langone. “The

Supervised Learning 98

Supervised Learning Deep Learning Deep Learning Algorithm 98

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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Supervised Learning Deep Learning Deep Learning Algorithm 98

Dataconomy

APRIL 29, 2025

Achieving such a model requires careful tuning of algorithms, feature engineering, and possibly employing ensembles of models to balance complexities. Goals of supervised learning In supervised learning tasks, managing the bias-variance tradeoff aligns with specific objectives.

Cross Validation 91

Cross Validation Supervised Learning Machine Learning Machine Learning 91

Analytics Vidhya

JUNE 13, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Speech Recognition is a supervised learning task. In the speech. The post MFCC Technique for Speech Recognition appeared first on Analytics Vidhya.

Supervised Learning 243

Supervised Learning Data Science Analytics Analytics 243

Machine Learning Research at Apple

DECEMBER 17, 2023

This paper was accepted at the workshop Self-Supervised Learning - Theory and Practice at NeurIPS 2023. We propose Bootstrap Your Own Variance (BYOV), combining Bootstrap Your Own Latent (BYOL), a negative-free Self-Supervised Learning (SSL) algorithm, with Bayes by Backprop (BBB), a Bayesian method for estimating model posteriors.

Supervised Learning 162

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Dataconomy

NOVEMBER 1, 2018

A clever algorithm that has digested seven decades’ worth of articles in China’s state-run media is now ready to predict its future policies. Supervised learning — the most developed form of Machine. The research design of this “crystal ball” can also be applied to tackling a variety of other problems.

Machine Learning 179

Machine Learning Machine Learning Supervised Learning Algorithm 179

Data Science Dojo

MAY 10, 2024

Unsupervised vs. supervised learning for embeddings While vector representation and contextual inference remain important factors in the evolution of LLM embeddings, the lens of comparative analysis also highlights another aspect for discussion. It involves the different approaches to train embeddings.

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Dataconomy

MARCH 11, 2025

Applications of linear regression in machine learning Linear regression plays a significant role in supervised learning, where it models relationships based on a labeled dataset. Understanding supervised learning In supervised learning, algorithms learn from training data that includes input-output pairs.

Supervised Learning 91

Supervised Learning Machine Learning Machine Learning Exploratory Data Analysis 91

Dataconomy

APRIL 29, 2025

Binary classification is a supervised learning method designed to categorize data into one of two possible outcomes. Overview of classification in machine learning Classification serves as a foundational method in machine learning, where algorithms are trained on labeled datasets to make predictions.

Support Vector Machines 91

Support Vector Machines Machine Learning Machine Learning Algorithm 91

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. I don’t think it will replace existing algorithms,” Shwartz-Ziv noted.

Supervised Learning 75

Supervised Learning Machine Learning Machine Learning Clustering 75

Data Science Dojo

APRIL 25, 2025

By basing decisions on data and algorithms rather than gut feelings, businesses can reduce the influence of bias in critical systems. You will need to implement algorithms that let it choose actions on its own. Reinforcement Learning (RL) is a popular choice because it mimics how humans learn: by trial and error.

AI 342

AI AI Supervised Learning Algorithm 342

Towards AI

APRIL 24, 2025

Thats the motto of Unsupervised Learning a fascinating branch of machine learning where algorithms learn patterns from unlabeled data. Unsupervised learning helps you automatically discover patterns or groupings or clustering in the data, like identifying clusters of customers with similar behaviors or preferences.

Supervised Learning 81

Supervised Learning Clustering Machine Learning Machine Learning 81

Dataconomy

APRIL 24, 2025

Unsupervised learning is a fascinating area within machine learning that uncovers hidden patterns in data without the need for pre-labeled examples. By allowing algorithms to learn autonomously, it opens the door to various innovative applications across different fields. What is unsupervised learning?

Clustering 91

Clustering Machine Learning Machine Learning Algorithm 91

Google Research AI blog

FEBRUARY 10, 2023

Robust algorithm design is the backbone of systems across Google, particularly for our ML and AI models. Hence, developing algorithms with improved efficiency, performance and speed remains a high priority as it empowers services ranging from Search and Ads to Maps and YouTube. You can find other posts in the series here.)

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Algorithm Clustering ML ML 110

BAIR

JULY 9, 2023

Figure 1: stepwise behavior in self-supervised learning. When training common SSL algorithms, we find that the loss descends in a stepwise fashion (top left) and the learned embeddings iteratively increase in dimensionality (bottom left). Our work finds the analogous results for SSL.

Supervised Learning 75

Supervised Learning Deep Learning Deep Learning Algorithm 75

Towards AI

JULY 3, 2024

When it comes to the three best algorithms to use for spatial analysis, the debate is never-ending. The competition for best algorithms can be just as intense in machine learning and spatial analysis, but it is based more objectively on data, performance, and particular use cases. Also, what project are you working on?

K-nearest Neighbors 105

K-nearest Neighbors Machine Learning Machine Learning Algorithm 105

Machine Learning Research at Apple

FEBRUARY 27, 2023

Self-training (ST) and self-supervised learning (SSL) methods have demonstrated strong improvements in automatic speech recognition (ASR). In this work we aim to analyse the effect of number of speakers in the training data on a recent SSL algorithm (wav2vec 2.0), and a recent ST algorithm (slimIPL).

Supervised Learning 130

Supervised Learning Algorithm 130

Machine Learning Mastery

DECEMBER 31, 2022

Last Updated on January 1, 2023 While a logistic regression classifier is used for binary class classification, softmax classifier is a supervised learning algorithm which is mostly used when multiple classes are involved. Softmax classifier works by assigning a probability distribution to each class.

Supervised Learning 130

Supervised Learning Algorithm Deep Learning Deep Learning 130

Dataconomy

APRIL 17, 2025

Image-to-image translation is a fascinating area of generative AI that harnesses advanced algorithms to transform existing images into new forms while retaining essential characteristics. Understanding generative AI Generative AI encompasses a range of algorithms designed to create new content based on pre-existing data.

Deep Learning 91

Deep Learning Deep Learning Supervised Learning Algorithm 91

Data Science Dojo

MAY 10, 2024

Unsupervised vs. supervised learning for embeddings While vector representation and contextual inference remain important factors in the evolution of LLM embeddings, the lens of comparative analysis also highlights another aspect for discussion. It involves the different approaches to train embeddings.

Supervised Learning 195

Supervised Learning Clustering ML ML 195

Data Science Dojo

FEBRUARY 15, 2023

Ultimately, we can use two or three vital tools: 1) [either] a simple checklist, 2) [or,] the interdisciplinary field of project-management, and 3) algorithms and data structures. In addition to the mindful use of the above twelve elements, our Google-search might reveal that various authors suggest some vital algorithms for data science.

K-nearest Neighbors 195

K-nearest Neighbors Decision Trees Support Vector Machines Data Science 195

Data Science Dojo

AUGUST 19, 2024

They dive deep into artificial neural networks, algorithms, and data structures, creating groundbreaking solutions for complex issues. These professionals venture into new frontiers like machine learning, natural language processing, and computer vision, continually pushing the limits of AI’s potential.

AI 364

AI AI Machine Learning Machine Learning 364