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Hands-On Machine Learning with Scikit-Learn Keras & TensorFlow 3rd Edition
Hands-On Machine Learning with Scikit-Learn Keras & TensorFlow 3rd Edition
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This third edition technical guide centers on deep learning, machine learning, and the development of artificial intelligence. Authored by Aurélien Géron, the publication investigates actionable strategies for constructing intelligent systems utilizing prominent open-source machine learning libraries.
The text introduces fundamental theories tied to model construction, data analysis, neural network deployment, and complete machine learning pipelines. The material is structured to guide individuals through the essential steps of training, assessing, and refining machine learning models for diverse practical scenarios.
Professionals and students will discover concepts concerning unsupervised learning, supervised learning, neural network frameworks, predictive modeling, and deep learning structures. Furthermore, the manual details applied execution methods leveraging TensorFlow, Keras, and Scikit-Learn, all while reviewing techniques prevalent in contemporary AI initiatives.
Ideal for data scientists, students, developers, and technology practitioners, this comprehensive resource delivers an in-depth exploration of deep learning and machine learning principles utilized within analytics, software engineering, and artificial intelligence deployments.
Topics Covered:
- Machine learning fundamentals
- Supervised learning concepts
- Unsupervised learning methods
- Deep learning techniques
- Neural network architectures
- Scikit-Learn applications
- Keras framework concepts
- TensorFlow development methods
- Predictive modeling approaches
- Intelligent system development
Specifications:
- Title: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- Author: Aurélien Géron
- Edition: 3rd Edition
- Category: Artificial Intelligence
- Subject: Machine Learning and Deep Learning
- Language: English
- ISBN-10: 1098125975
- ISBN-13: 978-1098125974
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