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75.4 MB
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April 20, 2026, 7:43 p.m.
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(Last updated: April 20, 2026, 7:49 p.m.)
| File | Size |
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| Anand S. Kickstart Artificial Intelligence Fundamentals_.2025.pdf | 24.8 MB |
| Code.zip | 50.6 MB |
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| Uploaded by XXXClub | Size 445.6 MB | Health [ 19 /44 ] | Added 2024-01-27 |
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SOURCE: Anand S. Kickstart Artificial Intelligence Fundamentals...2025
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COVER
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MEDIAINFO
Textbook in PDF format Preface Artificial Intelligence (AI) has revolutionized the way we interact with technology, shaping innovations across industries and redefining what machines are capable of achieving. From its theoretical roots to practical applications, AI continues to be a field that sparks curiosity and drives innovation. Kickstart Artificial Intelligence Fundamentals is crafted to provide a structured and comprehensive introduction to this fascinating domain. This book is designed for students, professionals, and enthusiasts who are keen to delve into the world of AI, starting from foundational concepts to the implementation of cutting-edge technologies. The book begins with the basics of Machine Learning models and their evolution into Artificial Neural Networks. It then ventures into advanced architectures such as Convolutional Neural Networks and Recurrent Neural Networks, equipping readers with the theoretical knowledge and practical insights needed to navigate the AI landscape. Each chapter is enriched with Python-based examples using TensorFlow and Keras frameworks, enabling hands-on learning and fostering a deeper understanding of the subject. This book not only serves as an educational resource but also inspires its readers to embark on their own journeys of discovery in the ever-evolving world of AI. May it provide the readers with the confidence and foundational knowledge to explore the limitless possibilities of Artificial Intelligence. Happy learning, and welcome to the future of technology! This book is thoughtfully divided into 17 chapters, each designed to progressively build your understanding of Artificial Intelligence (AI) and its foundational concepts. Chapter 1. Introduction and Evolution of AI Technologies: This chapter provides a historical overview of Artificial Intelligence (AI), tracing its evolution from symbolic reasoning and rule-based systems to modern-day neural networks. Key milestones, significant breakthroughs, and the growth of AI as a field are discussed to set the stage for exploring its transformative potential. Chapter 2. Modern Approach to AI: This chapter delves into the shift from traditional AI approaches to data-driven machine learning models. It introduces the role of data, algorithms, and computational power in enabling modern AI, emphasizing their convergence in creating advanced intelligent systems. Chapter 3. Introduction to Machine Learning: This chapter presents Machine Learning (ML) as the backbone of modern AI. This chapter introduces the core concepts of ML, including supervised, unsupervised, and reinforcement learning, alongside practical steps such as data preparation, feature engineering, and model evaluation. Chapter 4. Regression Versus Classification Model: This chapter contrasts regression and classification tasks, exploring their significance in predicting continuous values and categorizing data points. Examples and hands-on Python implementations provide a practical understanding of these fundamental ML paradigms. Chapter 5. Naive Bayes as a Linear Classifier: This chapter explores the Naive Bayes algorithm, with an emphasis on its assumptions, applications, and effectiveness as a linear classifier. Real-world use cases, such as spam detection and sentiment analysis, illustrate its utility. Chapter 6. Tree-Based Machine Learning Models: This chapter examines tree-based ML models, including Decision Trees, Random Forest, and Gradient Boosting. These models are highlighted for their interpretability and effectiveness in handling complex, non-linear data. Chapter 7. Distance-Based Machine Learning Models: This chapter focuses on algorithms such as K-Nearest Neighbors (KNN) and distance metrics and their application in classification and regression tasks. It emphasizes the simplicity and adaptability of distance-based learning. Chapter 8. Support Vector Machines: This chapter introduces Support Vector Machines, detailing their mathematical foundations, kernel functions, and effectiveness in classification tasks. Examples demonstrate SVM’s ability to handle high-dimensional data and complex decision boundaries. Chapter 9. Introduction to Artificial Neural Networks: This chapter transitions from classical ML to Neural Networks, explaining their structure, activation functions, and layers. The foundational concepts of Artificial Neural Networks (ANNs) set the stage for deep learning models. Chapter 10. Training Neural Networks: This chapter shifts to the mechanics of training neural networks, including gradient descent, backpropagation, and optimization techniques. Practical Python implementations illustrate the training process. Chapter 11. Introduction to Convolutional Neural Networks: This chapter introduces Convolutional Neural Networks (CNNs) as a powerful tool for image-related tasks. The chapter covers convolutional layers, pooling, and activation functions, building the foundation for deeper exploration. Chapter 12. Classification Using CNN: This chapter expands on the basics of CNNs. It delves into image classification, demonstrating step-by-step model implementation using TensorFlow-Keras. Practical insights and results are presented for hands-on learning. Chapter 13. Pre-trained CNN Architectures: This chapter explores popular pre-trained CNN models such as VGG, ResNet, and YOLO, showcasing their applications in object detection, semantic segmentation, and transfer learning. Readers are encouraged to experiment with these models for various tasks. Chapter 14. Introduction to Recurrent Neural Networks: This chapter introduces RNNs for handling sequential data such as time series and text. This chapter explains their architecture, working principles, and challenges, paving the way for more advanced models such as LSTMs. Chapter 15. Introduction to Long Short-Term Memory (LSTM): The chapter focuses on LSTM networks, designed to overcome the long-term dependency problem in RNNs. It explains their architecture and advantages, providing insights into how they manage temporal dependencies effectively. Chapter 16. Application of LSTM in NLP and TS Forecasting: This chapter applies LSTMs and GRUs to real-world tasks such as time-series forecasting, sentiment analysis, language translation, and chatbot development. It emphasizes hands-on implementations with Python. Chapter 17. Emerging Trends and Ethical Considerations in AI: This chapter navigates advanced applications in AI, including multimodal models and generative AI. It also addresses ethical considerations such as bias, privacy, and societal impacts, urging readers to approach AI responsibly and innovatively
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