ml-fundamentals
Master machine learning foundations - algorithms, preprocessing, feature engineering, and evaluation
Découvrez les skills Données et IA, comparez leurs dépôts et consultez leurs instructions.
Master machine learning foundations - algorithms, preprocessing, feature engineering, and evaluation
Build production-ready classification and regression models with hyperparameter tuning
Process and analyze text using modern NLP techniques - preprocessing, embeddings, and transformers
Build computer vision solutions - image classification, object detection, and transfer learning
Deploy ML models to production - APIs, containerization, monitoring, and MLOps
Linux system management - packages, services, users
Linux networking fundamentals - configuration, troubleshooting, security
Linux programming languages - installation, configuration, development
Linux data science and AI/ML environment setup - TensorFlow, PyTorch, CUDA, Jupyter
Linux shell scripting mastery - Bash, automation, cron jobs
Linux system administration - installation, maintenance, troubleshooting
Linux DevOps tools and practices - CI/CD, automation, infrastructure
Linux specialized topics - kernel, security hardening, performance
Linux development environment - compilers, build tools, IDEs
Linux shell basics - navigation, commands, file operations
Linux system architecture - processes, memory, storage, kernel
Master UIKit - Views, controllers, navigation, Auto Layout, animations
Master iOS development foundations - Architecture, lifecycle, memory, concurrency
Master SwiftUI - Declarative UI, state management, animations, modern iOS development
Master iOS data persistence - SwiftData, Core Data, Keychain, CloudKit
Master iOS testing - XCTest, UI testing, mocking, debugging, performance
Master iOS networking - URLSession, async/await, REST APIs, authentication
Master App Store deployment - Submission, TestFlight, CI/CD, release management
Core HTML5 elements, document structure, and foundational markup patterns
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.