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Bayesian Analysis with Python. A practical guide to probabilistic modeling - Third Edition
Osvaldo Martin, Christopher Fonnesbeck, Thomas Wiecki
The third edition of Bayesian Analysis with Python serves as an introduction to the main concepts of applied Bayesian modeling using PyMC, a state-of-the-art probabilistic programming library, and other libraries that support and facilitate modeling like ArviZ, for exploratory analysis of Bayesian models; Bambi, for flexible and easy hierarchical linear modeling; PreliZ, for prior elicitation; PyMC-BART, for flexible non-parametric regression; and Kulprit, for variable selection.In this updated edition, a brief and conceptual introduction to probability theory enhances your learning journey by introducing new topics like Bayesian additive regression trees (BART), featuring updated examples. Refined explanations, informed by feedback and experience from previous editions, underscore the book's emphasis on Bayesian statistics. You will explore various models, including hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes, and BART, using synthetic and real datasets.By the end of this book, you’ll understand probabilistic modeling and be able to design and implement Bayesian models for data science, with a strong foundation for more advanced study.*Email sign-up and proof of purchase required
Osvaldo Martin, Eric Ma, Austin Rochford
The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models.The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to.
Bayesian Analysis with Python. Unleash the power and flexibility of the Bayesian framework
Osvaldo Martin
The purpose of this book is to teach the main concepts of Bayesian data analysis. We will learn how to effectively use PyMC3, a Python library for probabilistic programming, to perform Bayesian parameter estimation, to check models and validate them. This book begins presenting the key concepts of the Bayesian framework and the main advantages of this approach from a practical point of view. Moving on, we will explore the power and flexibility of generalized linear models and how to adapt them to a wide array of problems, including regression and classification. We will also look into mixture models and clustering data, and we will finish with advanced topics like non-parametrics models and Gaussian processes. With the help of Python and PyMC3 you will learn to implement, check and expand Bayesian models to solve data analysis problems.
Alvaro Fuentes
Python is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations.Become a Python Data Analyst introduces Python’s most essential tools and libraries necessary to work with the data analysis process, right from preparing data to performing simple statistical analyses and creating meaningful data visualizations.In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques.By the end of this book, you will have hands-on experience performing data analysis with Python.
Becoming a Data Analyst. A Beginner's Guide to Kickstarting Your Data Analysis Career
Remsey Mailjard, Maaike van Putten
This guide is designed to take you from novice to confident data analyst. Starting with the fundamentals of data analytics, you will explore what data analysis entails and why it's crucial in today's data-driven industries. You'll develop a data analyst mindset, honing your problem-solving and critical-thinking skills through practical exercises. You'll be introduced to different types of data, data sources and key concepts like KPIs and data warehouses. Hands-on chapters will guide you through Excel for basic data analysis, teaching you vital functions, pivot tables, and visualization techniques. You'll dive into SQL to query and manipulate data as well as data cleaning and exploration to prepare datasets for meaningful analysis. More advanced chapters will introduce you to Power BI, so you can build interactive dashboards and use DAX for advanced calculations. You'll work on major projects that will form a professional portfolio showcasing your skills in sales analysis, HR analytics, and customer insights. Finally, the book will teach you the art of communicating your findings through data storytelling to different audiences. You'll also find guidance on continuing education and career growth, ensuring you're well-prepared to launch a successful career in data analytics.
Jorge Brasil
In this book, you'll embark on a comprehensive journey through the fundamentals of linear algebra, a critical component for any aspiring machine learning expert. Starting with an introductory overview, the course explains why linear algebra is indispensable for machine learning, setting the stage for deeper exploration. You'll then dive into the concepts of vectors and matrices, understanding their definitions, properties, and practical applications in the field.As you progress, the course takes a closer look at matrix decomposition, breaking down complex matrices into simpler, more manageable forms. This section emphasizes the importance of decomposition techniques in simplifying computations and enhancing data analysis. The final chapter focuses on principal component analysis, a powerful technique for dimensionality reduction that is widely used in machine learning and data science. By the end of the course, you will have a solid grasp of how PCA can be applied to streamline data and improve model performance.This course is designed to provide technical professionals with a thorough understanding of linear algebra's role in machine learning. By the end, you'll be well-equipped with the knowledge and skills needed to apply linear algebra in practical machine learning scenarios.
Jorge Brasil
This book takes readers on a structured journey through calculus fundamentals essential for AI. Starting with “Why Calculus?” it introduces key concepts like functions, limits, and derivatives, providing a solid foundation for understanding machine learning.As readers progress, they will encounter practical applications such as Taylor Series for curve fitting, gradient descent for optimization, and L'Hôpital’s Rule for managing undefined expressions. Each chapter builds up from core calculus to multidimensional topics, making complex ideas accessible and applicable to AI.The final chapters guide readers through multivariable calculus, including advanced concepts like the gradient, Hessian, and backpropagation, crucial for neural networks. From optimizing models to understanding cost functions, this book equips readers with the calculus skills needed to confidently tackle AI challenges, offering insights that make complex calculus both manageable and deeply relevant to machine learning.
Jorge Brasil
Delve into the importance of probability and statistics in AI, beginning with fundamental measures like mean, median, and variance. This book takes you on a journey through the basics of probability theory, introducing key concepts such as central tendency, variance, and probability distributions. It emphasizes the role of statistical measures in understanding and analyzing data.Building on these foundations, the book explores hypothesis testing, Bayesian inference, and statistical distributions in-depth. Readers will gain practical insights into essential techniques for model evaluation, maximum likelihood estimation, and the interpretation of data in the context of AI applications. Each concept is illustrated with practical examples and case studies to ensure clarity and application.Finally, advanced topics like Markov processes, hierarchical Bayesian models, and multivariate distributions are introduced. The book addresses critical areas like variance, correlation, and hypothesis testing, equipping readers with the skills to tackle real-world challenges in AI and machine learning. Whether you're a student, professional, or AI enthusiast, this book offers the essential statistical tools and knowledge to excel in the field.