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Aldo Marzullo, Enrico Deusebio, Claudio Stamile
Graph Machine Learning, Second Edition builds on its predecessor’s success, delivering the latest tools and techniques for this rapidly evolving field. From basic graph theory to advanced ML models, you’ll learn how to represent data as graphs to uncover hidden patterns and relationships, with practical implementation emphasized through refreshed code examples. This thoroughly updated edition replaces outdated examples with modern alternatives such as PyTorch and DGL, available on GitHub to support enhanced learning.The book also introduces new chapters on large language models and temporal graph learning, along with deeper insights into modern graph ML frameworks. Rather than serving as a step-by-step tutorial, it focuses on equipping you with fundamental problem-solving approaches that remain valuable even as specific technologies evolve. You will have a clear framework for assessing and selecting the right tools.By the end of this book, you’ll gain both a solid understanding of graph machine learning theory and the skills to apply it to real-world challenges.*Email sign-up and proof of purchase required -
Tarek A. Atwan
To use time series data to your advantage, you need to master data preparation, analysis, and forecasting. This fully refreshed second edition helps you unlock insights from time series data with new chapters on probabilistic models, signal processing techniques, and new content on transformers. You’ll work with the latest releases of popular libraries like Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet through up-to-date examples.You'll hit the ground running by ingesting time series data from various sources and formats and learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods.Through detailed instructions, you'll explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR, and learn practical techniques for handling non-stationary data using power transforms, ACF and PACF plots, and decomposing time series data with seasonal patterns. The recipes then level up to cover more advanced topics such as building ML and DL models using TensorFlow and PyTorch and applying probabilistic modeling techniques. In this part, you’ll also be able to evaluate, compare, and optimize models, finishing with a strong command of wrangling data with Python.
Joshua N. Milligan
Tableau 2025 marks a new era in data visualization and analysis, bringing together advanced AI integrations and dynamic user experiences. This sixth edition, written by Tableau Visionary and Zen Master Joshua Miligan, is an end-to-end guide to mastering the latest innovations in Tableau that transform raw data into actionable insights.This edition introduces groundbreaking features like Tableau AI (including Tableau Pulse and Tableau Agent), enhancing your analytical capabilities with AI-driven data exploration and automated insights. With detailed walkthroughs, you’ll learn to build dynamic dashboards that respond to your data in real time and work with sophisticated AI functionalities that predict trends and model scenarios.Whether you're a seasoned data professional or new to Tableau, this book provides the tools you need to leverage Tableau’s full potential. From integrating diverse data sources using the enhanced data model to employing advanced geospatial functions for detailed mapping, every chapter is packed with expert knowledge and practical applications designed to put powerful analytics at your fingertips.*Email sign-up and proof of purchase required