Python Programming : Machine Learning & Data Science, Scikit-learn, TensorFlow, PyTorch, XGBoost, Statsmodels

Machine learning is no longer a distant frontier reserved for data scientists and engineers in elite labs—it has become an essential toolkit for anyone seeking to derive insights from data, build predictive systems, or explore artificial intelligence. The landscape of machine learning is both vast and rapidly evolving, and understanding it requires more than just learning a few algorithms or copying code from tutorials. It requires a deep comprehension of core principles, preprocessing strategies, model building, evaluation techniques, and the ability to connect theoretical foundations with practical implementations.

This book is designed to guide learners through the essential building blocks of machine learning, progressing from foundational preprocessing techniques to complex model evaluation and optimization strategies. Each section is crafted to demystify core concepts while grounding them in hands-on, real-world applications using Python libraries such as Scikit-learn. Whether you're a student, aspiring data scientist, or a professional seeking to strengthen your machine learning foundations, this book offers a structured and practical pathway.

The journey begins with a deep dive into data preprocessing, exploring critical topics such as zero mean and unit variance normalization, min-max scaling, and the importance of thoughtful data transformation in ensuring model performance. Feature engineering is covered in detail, emphasizing its pivotal role in enhancing model accuracy and interpretability.

Next, the book introduces Scikit-learn, the powerful Python library that simplifies many machine learning workflows. We present a clear overview of its structure, modules, and usage, ensuring that readers can effectively use it as a foundation for implementing models.