Kickstart Artificial Intelligence Fundamentals
Master Machine Learning, Neural Networks, and Deep Learning from Basics to Build Modern AI Solutions with Python and TensorFlow-Keras (English Edition)
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Kickstart Artificial Intelligence Fundamentals
Master Machine Learning, Neural Networks, and Deep Learning from Basics to Build Modern AI Solutions with Python and TensorFlow-Keras (English Edition)
About this book
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 Technologies2. Modern Approach to AI3. Introduction to Machine Learning4. Regression Versus Classification Model5. Naive Bayes as a Linear Classifier6. Tree-Based Machine Learning Models7. Distance-Based Machine Learning Models8. Support Vector Machines9. Introduction to Artificial Neural Networks10. Training Neural Networks11. Introduction to Convolutional Neural Networks12. Classification Using CNN13. Pre-trained CNN Architectures14. Introduction to Recurrent Neural Networks15. Introduction to Long Short-Term Memory (LSTM)16. Application of LSTM in NLP and TS Forecasting17. Emerging Trends and Ethical Considerations in AI
Index
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Information
Table of contents
- Cover Page
- Title Page
- Copyright Page
- Dedication Page
- About the Author
- About the Technical Reviewer
- Acknowledgements
- Preface
- Get a Free eBook
- Errata
- 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