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Clarifying DNNs: The book explains the core concepts of DNNs, their structure, and how they extract patterns from training data.
Data preparation: Understanding the importance of various datasets, including training, testing, and unseen data, in building robust AI models.
Machine Learning and Deep Learning: The book provides a clear overview of Machine Learning (ML) and Deep Learning (DL) as foundational concepts for AI development.
Python libraries: Learn about Python libraries commonly used for AI and DNN implementation.
Designing AI Management Dashboards: Discover how to create dashboards to visualize and monitor AI performance.
Real-world applications of AI: Explore the diverse domains where AI is making a significant impact, including finance, and healthcare.
AI Engine integration: Understand the benefits of integrating AI engines with existing systems like ERP.
Generative AI: Learn about this exciting subfield of AI focused on creating new data.
50+ video tutorials: The book's website offers video tutorials demonstrating AI forecasting in various domains.
Overall, "AI-Modelling and Process" provides a valuable roadmap for understanding and implementing AI in today's data-driven world.
