1. Practical Gradient Boosting: A deep dive into Gradient Boosting in Python
Authored by Guillaume Saupin, “Practical Gradient Boosting” is a comprehensive guide that delves into the intricate details of Gradient Boosting techniques in Python. This book is for anyone looking to strengthen their machine learning toolkit with practical insights and applicable knowledge. It incorporates a wealth of examples that demonstrate how Gradient Boosting can significantly improve prediction accuracy. A great blend of both theory and practice makes this a must-read for data scientists aiming to up their game. 
2. Machine Learning Approaches on the Bankruptcy Modeling
Murat Yazici’s “Machine Learning Approaches on the Bankruptcy Modeling” provides a nuanced look into using machine learning for predicting bankruptcy through various models, including Altman z-score and Neural Networks. This text is crucial for analysts and researchers in finance who are interested in applying machine learning methodologies to real-world problems. The in-depth discussion and comparative analyses present invaluable insights into financial modeling, making it a necessary addition to your collection. 
3. Practical Data Science with Jupyter
“Practical Data Science with Jupyter” by Prateek Gupta is a gem for beginners and seasoned practitioners alike. This book outlines the essential steps of data cleaning, pre-processing, and feature engineering using Jupyter’s innovative platform. With its hands-on approach, it empowers you to embrace the entire data science lifecycle effectively. The extensive coverage of elements from data wrangling to machine learning makes it an invaluable resource. 
4. Supervised Learning in Biological Applications
For those in biological sciences, Jamie Flux’s “Supervised Learning in Biological Applications” is essential reading. This book focuses on applying supervised learning techniques in biological contexts, providing a detailed overview of how data science intersects with life sciences. It’s filled with practical examples and insights that can help researchers harness machine learning to advance biological research, making it a vital resource for professionals in this interdisciplinary field. 
5. Gradient Boosting – Exploitez les arbres de décision pour le Machine Learning
This French text by Guillaume Saupin covers the practical aspects and strategies of employing decision trees for machine learning tasks. “Gradient Boosting” provides a broad overview of XGBoost and CatBoost, ensuring readers can master these essential techniques. This book is particularly beneficial for French-speaking data scientists eager to deepen their understanding of gradient boosting methods. 
6. A Day in the Life of a Data Scientist and AI Researcher
Ravindra Nayak’s concise exploration, “A Day in the Life of a Data Scientist and AI Researcher,” offers readers an engaging glimpse into the daily routines and challenges faced by professionals in the field. This book highlights essential skills and gives practical advice on navigating the complexities of data science careers. Ideal for newcomers to data science, it serves as inspiration for career progression in AI and data analytics fields. 
