1. Machine Learning with R: Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data, 4th Edition
Authored by Brett Lantz, this book is an essential resource for those looking to grasp machine learning concepts using the R programming language. Released just recently on May 29, 2023, it provides an up-to-date, thorough explanation of how to build and optimize models–from basic data preparation to advanced tuning techniques. Readers will find practical insights into handling big data that resonate well with the evolving industry standards. This book bridges theory and practice, making it a strong recommendation for anyone serious about entering the field of machine learning.
2. Advanced Analytics for Business: Generative AI and machine learning for tabular data
Written by Mark Ryan and Luca Massaron, this book sets the stage for understanding generative AI and machine learning with tabular data. Launching on March 25, 2025, it prepares readers for advanced analytics applications in business environments. The book dives deep into the techniques that empower businesses to utilize data effectively, making it a required read for professionals looking to leverage these remarkable technologies. It’s not just about theory; it’s about actionable insights that can transform strategies and drive success.
3. Gradient Boosting – Exploitez les arbres de décision pour le Machine Learning
Guillaume Saupin’s book on Gradient Boosting caters to readers keen to exploit decision trees effectively in machine learning. Published on March 16, 2022, it offers a comprehensive guide on advanced techniques like XGBoost and CatBoost. This is a pivotal read for data scientists who want to improve their predictive modeling skills and understand its application in real-world scenarios. The depth of information it provides guarantees that readers will walk away with robust knowledge of one of the most powerful algorithms in machine learning.
4. Modern Time Series Forecasting with Python
Manu Joseph’s book is essential for those interested in mastering time series forecasting with Python. Released on November 24, 2022, it walks readers through industry-ready techniques, blending modern machine learning and deep learning approaches. With practical examples and case studies, the author highlights the application of these models in various industries, ensuring that readers can utilize the knowledge gained in their professional pursuits. This book embodies the expertise required to handle time-sensitive data effectively.
5. Practical Gradient Boosting: An Deep Dive into Gradient Boosting in Python
Dr. Guillaume Saupin delves into the intricacies of gradient boosting in this illuminating book, published on October 16, 2022. It serves as an advanced guide for professionals familiar with basic machine learning concepts, expanding their horizon through in-depth discussions and practical applications in Python. The book stands out for its clear explanations and thorough examples, making it an invaluable resource for those aiming to deepen their understanding of this complex yet rewarding technique.
6. Machine Learning for High-Risk Applications: Approaches to Responsible AI
Co-authored by Patrick Hall, James Curtis, and Parul Pandey, this book emphasizes the ethical aspects of machine learning applications in high-stakes environments. Released on May 23, 2023, it discusses frameworks and guidelines to ensure responsible AI deployment. The importance of ethics in technology cannot be overstated, making this book a must-read for professionals who aspire to create impactful systems while being mindful of potential societal implications. Its focus on responsible AI will resonate with those dedicated to ethical practices in technical fields.
7. Data Science Solutions with Python
Tshepo Chris Nokeri’s book offers a practical guide to building fast and scalable models using multiple powerful tools such as Keras and PySpark. Published on October 26, 2021, it is designed for both beginners and seasoned data scientists aiming to hone their skills in Python-based data science techniques. The wide range of topics covered provides a solid foundation for readers to understand and apply new methodologies, ensuring they stay competitive in a fast-evolving field.
8. Phishing Website Detection Using Machine Learning Algorithms
Hajara Musa’s book tackles a crucial aspect of cybersecurity, exploring the detection of phishing websites through comparative studies of various machine learning algorithms. Launched on September 20, 2019, it provides valuable insights into safeguarding online spaces. In today’s world, where online threats are rampant, understanding how to leverage technology to combat these risks is essential. Musa’s research and findings will benefit cybersecurity professionals and enthusiasts alike.
9. 深入理解XGBoost：高效机器学习算法与进阶 (Chinese Edition)
Focusing on the efficiency of machine learning with XGBoost, this Chinese edition by 何龙 is a valuable resource for Mandarin speakers looking to expand their knowledge. While it was published on January 1, 2020, the principles applied within remain relevant. The book discusses advanced machine learning techniques format to help readers master this powerful algorithm, which is essential for predictive analytics and many modern data science applications. It’s a unique offering for those who prefer materials in Chinese.
10. Decision Tree: Artificial Intelligence for Quick Learning 7
Dr. mint’s upcoming book set for release on June 10, 2024, promises to provide swift insights into decision trees and their application in artificial intelligence. This book is ideal for quick learners who want to grasp AI concepts efficiently. Although it’s forthcoming, its focus on practicality and accessible writing style indicates its potential to be a hit among beginners and industry professionals alike. This title seems set to add value to any data enthusiast’s library!
