supervised learning

Instant Consumer Technology Team

Jun 17, 2025 · Artificial Intelligence

Mastering Fine‑Tuning Datasets: From Basics to Advanced LLM Techniques

This comprehensive guide explains the importance of fine‑tuning datasets for large language models, covering task classification, dataset formats, supervised and instruction tuning, domain adaptation, multimodal data, and practical code examples to help practitioners build effective training, validation, and test sets.

Fine-tuningdataset preparationinstruction tuning

0 likes · 33 min read

Mastering Fine‑Tuning Datasets: From Basics to Advanced LLM Techniques

Python Programming Learning Circle

Jan 22, 2025 · Artificial Intelligence

A Visual Introduction to Machine Learning: Concepts, Categories, and Techniques

This article provides a clear, illustrated overview of machine learning, explaining its place within artificial intelligence, the main sub‑fields such as supervised and unsupervised learning, classic algorithms, ensemble methods, and practical examples to help beginners grasp core concepts.

Artificial Intelligenceclassificationensemble methods

0 likes · 8 min read

A Visual Introduction to Machine Learning: Concepts, Categories, and Techniques

Model Perspective

Mar 8, 2024 · Artificial Intelligence

Master the Three Machine Learning Types and Model Paradigms

This article introduces the three core machine learning categories—supervised, unsupervised, and reinforcement learning—detailing their definitions, typical algorithms, and real‑world applications, and then compares generative and discriminative models, highlighting key examples, characteristics, and use‑case differences.

Generative Modelsdiscriminative modelsmachine learning

0 likes · 13 min read

Master the Three Machine Learning Types and Model Paradigms

Rare Earth Juejin Tech Community

Jul 29, 2023 · Artificial Intelligence

Introduction to Machine Learning: Concepts, Terminology, Algorithms, Evaluation Metrics, and Practical Code Examples

This article provides a comprehensive overview of machine learning, covering fundamental concepts, key terminology, common algorithms for supervised, unsupervised, and reinforcement learning, model evaluation metrics, loss functions, and practical code examples such as random forest and SVM implementations.

Algorithmsloss functionsmachine learning

0 likes · 35 min read

Introduction to Machine Learning: Concepts, Terminology, Algorithms, Evaluation Metrics, and Practical Code Examples

Python Programming Learning Circle

Mar 27, 2023 · Artificial Intelligence

Top 10 Machine Learning Algorithms: Concepts, Uses, and Key Characteristics

This article introduces the No‑Free‑Lunch principle in machine learning and provides concise explanations of ten fundamental supervised‑learning algorithms—including linear regression, logistic regression, LDA, decision trees, Naïve Bayes, K‑Nearest Neighbors, LVQ, SVM, random forest, and boosting—highlighting their mathematical basis, typical applications, advantages, and limitations.

AlgorithmsArtificial Intelligencedata science

0 likes · 12 min read

Top 10 Machine Learning Algorithms: Concepts, Uses, and Key Characteristics

DataFunSummit

Feb 4, 2023 · Artificial Intelligence

Overview of Deep Learning Algorithms: Supervised, Unsupervised, and Semi‑Supervised Methods

This article introduces deep learning as a powerful AI technique, explains its core algorithms—including supervised, unsupervised, and semi‑supervised approaches—and provides concrete examples such as CNN, RNN, autoencoders, GAN, self‑supervised and transfer learning, illustrated with visual demos.

AIGANdeep learning

0 likes · 6 min read

Overview of Deep Learning Algorithms: Supervised, Unsupervised, and Semi‑Supervised Methods

Model Perspective

Jan 7, 2023 · Artificial Intelligence

Mastering Supervised Learning: From Linear Models to SVMs and Beyond

An extensive overview of supervised learning introduces key concepts, model types, loss functions, optimization methods, linear and generalized linear models, support vector machines, generative approaches, tree and ensemble techniques, as well as foundational learning theory, providing a comprehensive foundation for AI practitioners.

AIGenerative ModelsSVM

0 likes · 9 min read

Mastering Supervised Learning: From Linear Models to SVMs and Beyond

Python Programming Learning Circle

Nov 21, 2022 · Artificial Intelligence

Transforming Time Series Data into Supervised Learning Datasets with Pandas

This tutorial explains how to convert single‑variable and multivariate time‑series data into supervised learning formats using pandas shift() and a custom series_to_supervised() function, covering one‑step, multi‑step, and sequence forecasting examples with complete Python code.

Pythonforecastingmachine learning

0 likes · 18 min read

Transforming Time Series Data into Supervised Learning Datasets with Pandas

Model Perspective

Nov 5, 2022 · Artificial Intelligence

Explore the Most Popular Machine Learning Algorithms and How They Work

This comprehensive guide walks you through the most popular machine learning algorithms, explaining how they are classified by learning style and problem type, and highlighting key examples from supervised, unsupervised, deep learning, ensemble, and many other algorithm families.

Algorithmsdeep learningmachine learning

0 likes · 11 min read

Explore the Most Popular Machine Learning Algorithms and How They Work

Model Perspective

Oct 26, 2022 · Artificial Intelligence

Master Machine Learning Algorithms: Types, Python Code & Real-World Examples

This article categorizes machine learning algorithms into supervised, unsupervised, and reinforcement learning, then details ten common algorithms—including linear regression, logistic regression, decision trees, SVM, Naive Bayes, K‑NN, K‑means, random forest, and dimensionality reduction—accompanied by clear Python code examples and illustrative diagrams.

AlgorithmsPythonmachine learning

0 likes · 14 min read

Master Machine Learning Algorithms: Types, Python Code & Real-World Examples

Model Perspective

Aug 5, 2022 · Artificial Intelligence

What Are the Essential Steps and Types of Machine Learning?

Machine learning involves five core steps—from data collection and preparation to model training, evaluation, and improvement—while encompassing supervised, unsupervised, and reinforcement learning methods, each with distinct algorithms and real-world applications across finance, healthcare, and retail.

applicationsmachine learningreinforcement learning

0 likes · 7 min read

What Are the Essential Steps and Types of Machine Learning?

Model Perspective

Aug 4, 2022 · Artificial Intelligence

How Supervised Learning Predicts House Prices – A Hands‑On Guide

Using a real‑world housing example, this article explains supervised and unsupervised learning, walks through building a price‑prediction function, introduces gradient descent for optimizing weights, and highlights pitfalls like overfitting, offering a practical introduction to core machine‑learning concepts.

Pythongradient descentlinear regression

0 likes · 13 min read

How Supervised Learning Predicts House Prices – A Hands‑On Guide

Model Perspective

Aug 3, 2022 · Artificial Intelligence

Explore the Most Popular Machine Learning Algorithms: A Comprehensive Guide

This article provides a thorough overview of the most widely used machine learning algorithms, classifying them by learning style and problem type, and highlighting popular methods such as supervised, unsupervised, semi‑supervised, regression, instance‑based, regularization, decision‑tree, Bayesian, clustering, association rule, neural network, deep learning, dimensionality‑reduction, and ensemble techniques.

Algorithmsclassificationdeep learning

0 likes · 10 min read

Explore the Most Popular Machine Learning Algorithms: A Comprehensive Guide

DataFunTalk

Jul 9, 2022 · Artificial Intelligence

User Behavior Sequence Based Transaction Anti‑Fraud Detection

This presentation explains how leveraging user behavior sequences with supervised and unsupervised deep learning models, including end‑to‑end and two‑stage architectures, improves transaction fraud detection by identifying distinct patterns of account takeover and stolen‑card activities and outlines the engineering deployment pipeline.

deep learningembeddingfraud detection

0 likes · 12 min read

User Behavior Sequence Based Transaction Anti‑Fraud Detection

Model Perspective

Jun 18, 2022 · Artificial Intelligence

Understanding Naive Bayes: Theory, Example, and Practical Steps

This article introduces the Naive Bayes classifier, explains its independence assumptions, walks through a weather‑based example with detailed probability calculations, demonstrates how to build and apply the model, and highlights its strengths and limitations in real‑world tasks such as document classification and spam filtering.

Naive Bayesclassificationmachine learning

0 likes · 7 min read

Understanding Naive Bayes: Theory, Example, and Practical Steps

Model Perspective

Jun 17, 2022 · Artificial Intelligence

What Is Classification in Data Mining? Types, Models, and Key Applications

The article explains classification as a data‑analysis task that builds models to assign new observations to predefined categories, outlines its implementation steps, describes various data types (boolean, nominal, ordinal, continuous, discrete), presents common machine‑learning classifiers such as decision trees and neural networks, and highlights practical applications like crime detection, disease risk prediction, and credit assessment.

Data Miningclassificationmachine learning

0 likes · 5 min read

What Is Classification in Data Mining? Types, Models, and Key Applications

Model Perspective

Jun 17, 2022 · Artificial Intelligence

Understanding Supervised Learning: Regression vs Classification Explained

This article explains the fundamentals of supervised machine learning, distinguishing between regression and classification, describing how algorithms learn mappings from inputs to outputs, and outlining common models such as linear regression, logistic regression, decision trees, SVMs, random forests, and neural networks.

Artificial Intelligenceclassificationmachine learning

0 likes · 4 min read

Understanding Supervised Learning: Regression vs Classification Explained

Python Programming Learning Circle

Apr 5, 2022 · Artificial Intelligence

Transforming Time Series Data into Supervised Learning Datasets with Pandas shift() and series_to_supervised()

This tutorial explains how to convert single‑variable and multi‑variable time‑series data into a supervised‑learning format using Pandas' shift() function and a custom series_to_supervised() helper, covering one‑step, multi‑step, and multivariate forecasting examples with complete Python code.

Pythonforecastingpandas

0 likes · 20 min read

Transforming Time Series Data into Supervised Learning Datasets with Pandas shift() and series_to_supervised()

ByteFE

Aug 2, 2021 · Artificial Intelligence

An Overview of Artificial Intelligence, Machine Learning, and Neural Networks

This article provides a beginner‑friendly overview of artificial intelligence, its relationship with machine learning, the four major learning paradigms—supervised, unsupervised, semi‑supervised and reinforcement learning—along with a historical sketch of neural networks, their training workflow, loss functions, back‑propagation, and parameter‑update mechanisms, while also containing a brief recruitment notice.

Artificial Intelligencedeep learningmachine learning

0 likes · 18 min read

An Overview of Artificial Intelligence, Machine Learning, and Neural Networks

DataFunTalk

Jul 7, 2021 · Artificial Intelligence

Robust Graph Representation Learning via Neural Sparsification

NeuralSparse is a supervised graph sparsification framework that removes task-irrelevant edges to improve GNN generalization, combining a sparsification network with downstream GNN training, and demonstrates superior performance across multiple graph benchmarks compared to random edge dropping and other sparsification methods.

Edge PruningGraph Neural NetworksGraph Representation

0 likes · 8 min read

Robust Graph Representation Learning via Neural Sparsification