Typically, applied AI use cases are limited to employing off-the-shelf Machine Learning models, and they range anywhere from healthcare and finance to autonomous systems and agriculture. The journey through technicalities and innovation in the Machine Learning field is ongoing, and we hope this book serves as a compass, guiding the readers through the evolving landscape of Artificial Intelligence (AI). The algorithms and techniques may evolve, but the essence of AI remains timeless. For any innovation by leveraging Machine Learning models on diverse applications use cases, the focus (of innovation) typically includes model selection, parameter tuning and optimization, use of pre-trained models and transfer learning, right use of limited data, model interpretability and explainability, feature engineering and autoML robustness and security, and computational cost-efficiency and scalability. This book emphasizes the importance of moving beyond the constraints of pre-trained models and off-the-shelf basic building blocks to tackle real-world problems effectively. We navigate through three fundamental data types: numerical, textual, and image data, offering practical insights into their utilization across various domains. It is recommended that you become familiar with Python programming language as all the implementation is done with Python programming language. Firstly, we must start setting up an Interactive Development Environment (IDE) for coding Python and discuss some of the most important packages that are used in this book. Python is one of the most popular multi-purpose programming languages. The popularity comes from beginner-friendly syntax and a vast number of libraries and frameworks.

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