Claims Browser

The book 'Data Mining Techniques for Marketing, Sales and CRM' is impressively educational and demonstrates algorithms of data science and machine learning in real practical business application contexts.

"See the Algorithms of Data Science and Machine Learning in real practical business application contexts in this impressively educational classic book:"

The 4th edition of Artificial Intelligence: A Modern Approach covers deep learning, transfer learning, multi-agent systems, robotics, natural language processing, causality, and more.

"UPDATED 4th edition of this classic AI textbook [1166 pages] now covers Deep Learning, Transfer Learning, multi-agent systems, robotics, NLProc, causality & much more!"

Transfer learning allows knowledge to be reused at an enterprise level, so experience gained once can be repeatedly applied to real-world problems.

"At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world."

Transfer learning makes AI systems more reliable and robust, preventing them from deviating too much from expected performance under unforeseeable changes.

"This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance."

Transfer learning enables machine learning systems to leverage auxiliary data and models to solve target problems with small amounts of data.

"It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available."

The Art of Computer Programming by Donald Knuth is the classic book on programming.

"THE CLASSIC BOOK(s) on PROGRAMMING!"

Data Science for Business is a 5-star classic and essential reading for understanding data mining and data-analytic thinking.

"💥5-Star Classic! ➡️ Data Science for Business — What You Need to Know about Data Mining and Data-Analytic Thinking"

Neural Smithing is a classic Machine Learning book for learning neural network fundamentals.

"Learn Neural Networks fundamentals in this classic Machine Learning book: “Neural Smithing — Supervised Learning...”"

This book is a foundational resource for reinforcement learning.

"Reinforcement Learning foundational book"

The linked textbook is comprehensive on outlier analysis.

"Comprehensive textbook on Outlier Analysis"

The linked PDF is an extensive tutorial on outlier detection techniques.

"Extensive Tutorial on Outlier Detection Techniques"

The text covers temporal data streams from financial, business, medical, credit, sensor network (IoT/IIoT), spatiotemporal, and network domains.

"including financial, business, medical, credit, sensor network (IoT / IIoT) time series data streams, plus spatiotemporal data, network data, etc."

The text on outlier detection in temporal data is a classic reference.

"Classic text on Outlier Detection in Temporal Data"

The Python code in this cookbook is exceptionally well presented.

"Python code in a cookbook context has never looked so good."

This book is great for individual use and for training workshops and educational settings.

"Consequently, this is great not only for individual use but also for training workshops and other educational settings."

The Scikit-learn Cookbook is extraordinarily practical, useful, and reader-friendly.

"This is an extraordinarily practical, useful, and reader-friendly style, perfect for the target audience: anyone who needs to use (or is discovering how to use) specific Scikit-learn tools for their Python-based machine learning tasks."

The book covers mastering XGBoost, applying deep learning to tabular data, deploying models locally and in the cloud, and building pipelines to train and maintain models.

"• Master XGBoost • Apply deep learning to tabular data • Deploy models locally and in the cloud • Build pipelines to train and maintain models"

The book 'Machine Learning for Tabular Data' is excellent.

"Excellent book from @ManningBooks >> "Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI," by @MarkRyanMkm & @lucamassaron"

This resource provides a useful introduction to outlier detection in Python.

"Outlier Detection in Python — https://t.co/vWtHhru2DW from @ManningBooks"