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Practical Python AI Projects

Discover the art and science of solving artificial intelligence problems with Python using optimization modeling. This book covers the;

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Linear Models with Python

This version replaces R with Python to make it accessible to a greater number of users outside of statistics including those from Machine;

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Introduction to Computational Models with Python

Implementation of computational models with Python using Numpy, with examples and case studies The modeling of linear optimization problems, from;

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Introduction to Computational Models with Python

Introduction to Computational Models with Python explains how to implement computational models using the flexible and easy-to-use Python;

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Practical Explainable AI Using Python

using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers. You'll begin with an;

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Bayesian Analysis with Python

statistical problems using Python; * Tutorial guide that will take the you through the journey of Bayesian analysis with the help of sample problems;

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Pandas for Everyone

solving real problems with Pandas, even if you're new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical;

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Bayesian Analysis with Python

statistical modeling if you need to. What you will learn Build probabilistic models using the Python library PyMC3 Analyze probabilistic models with;

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Regression Analysis with Python

Learn the art of regression analysis with Python About This Book * Become competent at implementing regression analysis in Python;

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Practical Numerical and Scientific Computing with MATLAB (R) and Python

Practical Numerical and Scientific Computing with MATLAB (R) and Python concentrates on the practical aspects of numerical analysis and;

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Practical Numerical and Scientific Computing with MATLAB (R) and Python

Practical Numerical and Scientific Computing with MATLAB (R) and Python concentrates on the practical aspects of numerical analysis and;

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Deep Learning with Python

. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having;

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Deep Learning with Python

problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization;

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Linear Algebra and Its Applications with R

importance of linear algebra through working computational examples with R and Python, including tutorials on how to install R in the Appendix. If a;

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Data Structures using Python

finally ending with an appendix on Python built-in class functions.;

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Linear Model Theory

A precise and accessible presentation of linear model theory, illustrated with data examples Statisticians often use linear models for data;

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Pandas for Everyone

Manage and Automate Data Analysis with Pandas in Python Today, analysts must manage data characterized by extraordinary variety, velocity;

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Statistical Application Development with R and Python -

Software Implementation Illustrated with R and Python About This Book * Learn the nature of data through software which takes the;

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Mastering Python for Data Science

Science with Python - data mining, data analysis, data visualization, and machine learning Who This Book Is For If you are a Python;

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Building Web Applications with Python and Neo4j

About This Book Develop a set of common applications and solutions with Neo4j and Python Secure and deploy the Neo4j database;

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Explainable AI with Python

with Python fills the current gap in literature on this emerging topic by taking both a theoretical and a practical perspective, making the;

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Linear Model Methodology

, heteroscedastic linear models, response surface models with random effects, and linear multiresponse models. The final chapter introduces generalized;

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Linear Models

basic mathematical concepts in linear algebra. The author begins with a presentation of the classic fixed-effects linear model and goes on to;

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Python Machine Learning

Inside this book you will find all the basic notions to start with Python and all the programming concepts to build machine learning;

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Python for Probability, Statistics, and Machine Learning

is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python;

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Python for Probability Statistics and Machine Learning

is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python;

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Linear Models in Statistics

includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells;

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