Kickstart Artificial Intelligence Fundamentals: Master Machine Learning, Neural Networks, and Deep Learning from Basics to Build Modern AI Solutions with Python and TensorFlow-Keras

Anand, Dr. S.Mahesh

ISBN 10: 9348107135 / ISBN 13: 9789348107138

Verlag: Orange Education Pvt Ltd, 2025

Beschreibung

Bestandsnummer des Verkäufers 26404081089

Bibliografische Details

• Titel: Kickstart Artificial Intelligence Fundamentals: Master Machine Learning, Neural Networks, and Deep Learning from Basics to Build Modern AI Solutions with Python and TensorFlow-Keras
• Verlag: Orange Education Pvt Ltd
• Erscheinungsjahr: 2025
• Sprache: Englisch
• ISBN-10: 9348107135
• ISBN-13: 9789348107138
• Einband: Softcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

Master AI Fundamentals and Build Real-World Machine Learning and Deep Learning Solutions.

Book Description
AI is transforming industries, driving innovation, and shaping the future of technology. A strong foundation in AI fundamentals is essential for anyone looking to stay ahead in this rapidly evolving field.

Kickstart Artificial Intelligence Fundamentals is a comprehensive companion designed to demystify core AI concepts, covering Machine Learning, Deep Learning, and Neural Networks. Tailored for all AI enthusiasts, this book provides hands-on Python implementation using the TensorFlow-Keras framework, ensuring a seamless learning experience from theory to practice.

Bridging the gap between concepts and real-world applications, this book offers intuitive explanations, mathematical foundations, and practical use cases. Readers will explore supervised and unsupervised Machine Learning models, master Convolutional Neural Networks for image classification, and leverage Long Short-Term Memory networks for time-series forecasting. Each chapter includes coding examples and guided exercises, making it an invaluable resource for both beginners and advanced learners.

Table of Contents
1. Introduction and Evolution of AI Technologies
2. Modern Approach to AI
3. Introduction to Machine Learning
4. Regression Versus Classification Model
5. Naive Bayes as a Linear Classifier
6. Tree-Based Machine Learning Models
7. Distance-Based Machine Learning Models
8. Support Vector Machines
9. Introduction to Artificial Neural Networks
10. Training Neural Networks
11. Introduction to Convolutional Neural Networks
12. Classification Using CNN
13. Pre-trained CNN Architectures
14. Introduction to Recurrent Neural Networks
15. Introduction to Long Short-Term Memory (LSTM)
16. Application of LSTM in NLP and TS Forecasting
17. Emerging Trends and Ethical Considerations in AI
Index