regression modeling

Machine Learning Algorithms in Depth - by Vadim Smolyakov (Paperback)

JMP for Mixed Models - by Ruth Hummel & Elizabeth a Claassen & Russell D Wolfinger (Hardcover)

Data Science for Supply Chain Forecasting - by Nicolas Vandeput (Paperback)

Bayesian Inference of State Space Models - (Springer Texts in Statistics) by Kostas Triantafyllopoulos

Graph Neural Networks in Action - (In Action) by Keita Broadwater & Namid Stillman (Paperback)

Causal AI - by Robert Osazuwa Ness (Paperback)

Grokking Statistics - by Thomas Nield (Paperback)

Large Language Models: A Deep Dive - by Uday Kamath & Kevin Keenan & Garrett Somers & Sarah Sorenson (Hardcover)

Fundamentals of Deep Learning - 2nd Edition by Nithin Buduma & Nikhil Buduma & Joe Papa (Paperback)

Unsupervised Learning - (Computational Neuroscience) by Geoffrey Hinton & Terrence Sejnowski (Paperback)

Data Quality Fundamentals - by Barr Moses & Lior Gavish & Molly Vorwerck (Paperback)

Domain-Specific Small Language Models - by Guglielmo Iozzia (Paperback)

Rearchitecting Llms - by Pere Martra (Paperback)

Deep Learning for Time Series Cookbook - by Vitor Cerqueira & Luís Roque (Paperback)

Deep Generative Modeling - 2nd Edition by Jakub M Tomczak (Hardcover)

Statistics Every Programmer Needs - by Gary Sutton (Paperback)

Outlier Detection in Python - by Brett Kennedy (Paperback)

Interpretable Machine Learning with Python - Second Edition - 2nd Edition by Serg Masís (Paperback)

Option Pricing Models and Volatility Using Excel-VBA - (Wiley Finance) by Fabrice D Rouah & Gregory Vainberg (Paperback)

Extending Excel with Python and R - by Steven Sanderson & David Kun (Paperback)

Principles of Brain Dynamics - (Computational Neuroscience) by Mikhail I Rabinovich & Karl J Friston & Pablo Varona (Paperback)

Multiple Attribute Decision Making - (Lecture Notes in Economic and Mathematical Systems) by Ching-Lai Hwang & Kwangsun Yoon (Paperback)

Machine Learning in Protein Science - by Jinjin Li (Hardcover)