Les meilleurs livres Intelligence artificielle
51 livres et 59 critiques, dernière mise à jour le 13 juillet 2026 , note moyenne : 4.3
Livres en français
- Gradient Boosting - Exploitez les arbres de décision pour le Machine Learning (XGBoost, CatBoost, LightGBM)
- Introduction au Machine Learning
- Intelligence artificielle, l'affaire de tous - De la science au business
- Spark - Valorisez vos données en temps réel avec Spark ML et Hadoop
- Data science - Cours et exercices
- Apprentissage artificiel - Deep learning, concepts et algorithmes
- Deep Learning avec TensorFlow - Mise en oeuvre et cas concrets
- Machine Learning avec Scikit-Learn - Mise en oeuvre et cas concrets
- Big Data et Machine Learning - Les concepts et les outils de la data science
- Data Scientist et langage R - Guide d'autoformation à l'exploitation des Big Data
- L'Intelligence Artificielle pour les développeurs - Concepts et implémentations en Java
- Apprentissage machine - De la théorie à la pratique - Concepts fondamentaux en Machine Learning
- Apprentissage artificiel - Concepts et algorithmes
- Intelligence artificielle
- Réseaux de neurones - Méthodologie et applications
- Intelligence Artificielle
- Apprentissage statistique - Réseaux de neurones - Cartes topologiques - Machines à vecteurs supports
- L'Intelligence Artificielle pour les développeurs - Concepts et implémentations en C#
Livres en anglais
- Ensemble Methods - Foundations and Algorithms
- Generative AI Design Patterns - Solutions to Common Challenges When Building GenAI Agents and Applications
- Building Applications with AI Agents - Designing and Implementing Multiagent Systems
- AI Engineering - Building Applications with Foundation Models
- LLMOps - Managing Large Language Models in Production
- Prompt Engineering for Generative AI - Future-Proof Inputs for Reliable AI Outputs
- Building LLMs for Production - Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG
- Hands-On Large Language Models - Language Understanding and Generation
- All-in On AI - How Smart Companies Win Big with Artificial Intelligence
- Natural Language Processing in the Real World - Text Processing, Analytics, and Classification
- Text Analytics - An Introduction to the Science and Applications of Unstructured Information Analysis
- Practical Simulations for Machine Learning - Using Synthetic Data for AI
- Natural Language Processing with Transformers - Building Language Applications with Hugging Face
- Deep Learning on Graphs
- Scientific Writing 3.0 - A Reader and Writer's Guide
- Reinforcement Learning and Stochastic Optimization - A Unified Framework for Sequential Decisions
- Interpretable Machine Learning with Python - Learn to build interpretable high-performance models with hands-on real-world examples
- Graph Machine Learning - Take graph data to the next level by applying machine learning techniques and algorithms
- Reinforcement Learning - Industrial Applications of Intelligent Agents
- The Art of Feature Engineering - Essentials for Machine Learning
- Bandit Algorithms
- Machine Learning Under a Modern Optimization Lens
- Ensemble Learning - Pattern Classification Using Ensemble Methods
- Handbook of Machine Learning - Volume 2: Optimization and Decision Making
- Handbook of Machine Learning - Volume 1: Foundation of Artificial Intelligence
- Fundamentals of Data Visualization - A Primer on Making Informative and Compelling Figures
- Practical Tableau - 100 Tips, Tutorials, and Strategies from a Tableau Zen Master
- Data Science from Scratch - First Principles with Python
- Generative Deep Learning - Teaching Machines to Paint, Write, Compose, and Play
- Practical Time Series Analysis - Prediction With Statistics and Machine Learning
- Hands-On Unsupervised Learning Using Python - How to Build Applied Machine Learning Solutions from Unlabeled Data
- Machine Learning for Data Streams - With Practical Examples in MOA
- Natural Language Processing with Python