Kickstart Artificial Intelligence Fundamentals

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

Key Features

? Hands-on AI guide with Python, TensorFlow, and Keras implementations.

? Step-by-step walkthroughs of Machine Learning, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) models.

? Bridges AI theory with real-world applications and coding exercises.

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.

Beyond technical expertise, this book explores emerging trends like Generative AI and ethical considerations in AI, preparing readers for the challenges and opportunities in the field. This book will provide you the essential knowledge and hands-on experience to stay competitive. Don't get left behind-embrace AI and future-proof your career today!

What you will learn

? Build and train machine learning models for real-world datasets.

? Apply neural networks to classification and regression tasks.

? Implement CNNs and LSTMs for vision and sequence modeling.

? Solve AI problems using Python, TensorFlow, and Keras.

? Fine-tune pre-trained models for domain-specific applications.

? Explore generative AI for creative and industrial use cases.

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