Data Science & Machine Learning

Category	Data Science & Machine Learning
Pages	489
Size	24 MB
Created at:	2025
Last Updates	April 2025
ISBN No
Doc Type	PDF (Portable Document Format)

“Data Science & Machine Learning” is a comprehensive and rigorous introduction to the principles and practices of modern data analysis. Blending mathematical foundations with practical implementation in Python, this book walks readers through the entire data science pipeline—from importing and structuring raw data, to visualizing patterns, and building predictive models.

Tailored for students, educators, and professionals, the book emphasizes statistical thinking, supervised and unsupervised learning, optimization, and probability theory. It introduces essential concepts such as regression, classification, cross-validation, overfitting, and the risk-loss framework, while also exploring tools like pandas, matplotlib, seaborn, and scikit-learn.

With hands-on code examples, real datasets, and theoretical insights, it equips readers to not only apply data science methods effectively but also understand the 'why' behind them. Whether you are aiming to become a machine learning practitioner or deepen your statistical learning understanding, this book provides both the mathematical clarity and coding fluency needed to succeed.