-Titulo Original : Causal Inference And Discovery In Python Unlock The Secrets Of Modern Causal Machine Learning With Dowhy, Econml, Pytorch And More-Fabricante : Packt Publishing-Descripcion Original: Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data Purchase of the print or Kindle book includes a free Key Features Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more Discover modern causal inference techniques for average and heterogenous treatment effect estimation Explore and leverage traditional and modern causal discovery methods Book Description Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. Youll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, youll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, youll discover Python causal ecosystem and harness the power of cutting-edge algorithms. Youll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more. What you will learn Master the fundamental concepts of causal inference Decipher the mysteries of structural causal models Unleash the power of the 4-step causal inference process in Python Explore advanced uplift modeling techniques Unlock the secrets of modern causal discovery using Python Use causal inference for social impact and community benefit Who this book is for This book is for machine learning engineers, data scientists, and machine learning researchers looking to extend their data science toolkit and explore causal machine learning. It will also help developers familiar with causality who have worked in another technology and want to switch to Python, and data scientists with a history of working with traditional causality who want to learn causal machine learning. Its also a must-read for tech-savvy entrepreneurs looking to build a competitive edge for their products and go beyond the limitations of traditional machine learning. Table of Contents Causality - Hey, We Have Machine Learning, So Why Even Bother? Judea Pearl and the Ladder of Causation Regression, Observations, and Interventions Graphical Models Forks, Chains, and Immoralities Nodes, Edges, and Statistical (In)dependence The Four-Step Process of Causal Inference Causal Models - Assumptions and Challenges Causal Inference and Machine Learning - from Matching to Meta-Learners Causal Inference and Machine Learning - Advanced Estimators, Experiments, Evaluations, and More Causal Inference and Machine Learning - Deep Learning, NLP, and Beyond Can I Have a Causal Graph, Please? Causal Discovery and Machine Learning - from Assumptions to Applications Causal Discovery and Machine Learning - Advanced Deep Learning and Beyond Epilogu...

-Titulo Original : Spring Boot And Angular: Hands-on Full Stack Web Development With Java, Spring, And Angular-Fabricante : Packt Publishing-Descripcion Original: Design, build, and deploy performant and maintainable web applications using Spring, Spring Boot, and Angular Key Features: Explore key concepts and best practices in developing full stack applications for real-world use cases Boost your career as a full stack Java developer using advanced Spring tips and techniques Dive into the practical concepts of frontend development with Angular without the need for theory Book Description: Angular is a battle-tested and batteries-included JavaScript framework that is trusted by enterprises across the globe. Together with Spring Boot, it enables you to set up a resilient full stack for your web development project. This guide will help you harness the features of both these technologies and build complete Java web applications with impressive frontend. You will begin your journey of building Java full stack apps by learning how to set up the CI/CD pipeline. Then, youll find out how to build the backend for your app, guided by best practices to ensure security, performance, and testability. As you progress through the chapters, youll discover how to build the frontend for your app, architected for enterprise. The concluding chapters will teach you all about deploying your application using GitHub Actions. By the end of this web development book, youll have learned how to build a full stack web app using the most popular NPM packages for Angular and Java. Youll not just be able to build the app, but also package and deploy it through using continuous integration and continuous deployment practices. What You Will Learn: Find out how to architect Angular for enterprise-level app development Create a Spring Boot project using Spring Initializr Build RESTful APIs for Java web applications Understand how caching with Redis can improve your apps performance Discover how CORS with Spring Boot can improve your apps security Write tests to maintain a healthy Java Spring Boot application Implement testing and modern deployments in full stack app development Who this book is for: The book is for Java web developers with beginner-level experience in Spring Boot who want to develop complete web applications by leveraging the power of the JavaScript framework. An understanding of HTML, CSS, and Spring Boot is assumed as the book is not intended to help you get started with Angular...

-Titulo Original : The Definitive Guide To Power Query (M): Mastering Complex Data Transformation With Power Query-Fabricante : Packt Publishing-Descripcion Original: Learn how to use the Power Query M formula language and its functions effectively for better data modeling and impactful business intelligence reports. Purchase of the print or Kindle book includes a free PDF eBook Key Features Get comprehensive coverage of fundamental and advanced Power Query concepts Gain hands-on experience with practical examples based on real-world problems Delve into the intricacies of Power Query M language for enhanced data manipulation Book Description Data transformation is a critical step in building data models and business intelligence reports. Power Query is an invaluable tool for anyone who wants to master data transformation, and this book will equip you with the knowledge and skills to make the most of it. The Definitive Guide to Power Query (M) will help you build a solid foundation in the Power Query M language. As you progress through the chapters, youll learn how to use that knowledge to implement advanced concepts and data transformations before going on a no-compromise deep dive into the Power Query M language. Youll also get to grips with optimizing performance, handling errors, and implementing efficient data processing techniques. As this is a hands-on guide, the practical examples in the chapters will help you gain the skills to apply Power Query to real-world problems and improve your data analysis capabilities. By the end of this book, you will be able to leverage all of Power Querys remarkable capabilities for data transformation What you will learn Gain a strong understanding of Power query fundamentals Master various functions within Power Query to perform complex data operations Acquire knowledge about values, types, and control structures in Power Query Develop proficiency in error handling techniques Learn performance optimization strategies for Power Query Apply what youve learned to real-world scenarios, including common troublesome patterns Who this book is for This book is for business analysts, business intelligence professionals, and power business users working with data who want to add Power Query mastery to their resume. This book will be beneficial for anyone who wants to automate their process of data cleaning and save huge amount of time. Having some basic experience in Power Query is recommended. Table of Contents Introducing M Working with Power Query/M Accessing and Combining Data Understanding Values and Expressions Understanding Data Types Structured Values Conceptualizing M Working with Nested Structures Parameters and Custom Functions Dealing with Dates, Times, and Durations Comparers, Replacers, Combiners, and Splitters Handling Errors and Debugging Iteration and Recursion Troublesome Data Patterns Optimizing Performance Enabling Extension...

-Titulo Original : Comptia Security Sy0-701 Certification Guide - Third Edition Master Cybersecurity Fundamentals And Pass The Sy0-701 Exam On Your First Attempt-Fabricante : Packt Publishing-Descripcion Original: Build a solid understanding of cybersecurity principles and develop hands-on skills to pave the way for a successful and impactful career in the field. Purchase of this book unlocks access to web-based exam prep resources, including mock exams and flashcards. Key Features - Gain certified cybersecurity knowledge from Ian Neil, a world-class CompTIA certification trainer - Explore up-to-date content meticulously aligned with 701 exam objectives - Unlock an exclusive 12% exam discount voucher inside the book - Purchase of this book unlocks access to web-based exam prep resources such as mock exams and flashcards Book Description Building on the success of its bestselling predecessor, this third edition of the CompTIA Security SY0-701 Certification Guide serves as your one-stop resource for SY0-701 exam preparation. Written by cybersecurity expert Ian Neil, this comprehensive guide helps you unlock the intricacies of cybersecurity and understand the technology behind the SY0-701 certification, ensuring you approach the exam with confidence. By exploring cybersecurity in detail, this book introduces essential principles, controls, and best practices. The chapters are carefully structured to align with the exam objectives of the 701 update, bringing to you the most recent and relevant exam study material. By mastering cybersecurity fundamentals, youll acquire the knowledge and skills to identify and mitigate threats, manage vulnerabilities, and safeguard enterprise infrastructure. Youll be well equipped to apply the principles of security governance and compliance, conduct risk assessments, and excel in audit and assessment tasks. The book also contains mock exams and flashcards to help reinforce your learning and assess your exam-readiness. Whether you aim to excel the CompTIA Security SY0-701 exam, advance your career in cybersecurity, or enhance your existing knowledge, this book will transform you into a cybersecurity expert. What you will learn - Differentiate between various security control types - Apply mitigation techniques for enterprise security - Evaluate security implications of architecture models - Protect data by leveraging strategies and concepts - Implement resilience and recovery in security - Automate and orchestrate for running secure operations - Execute processes for third-party risk assessment and management - Conduct various audits and assessments with specific purposes Who this book is for Whether you have an IT background or not, if you aspire to pass the CompTIA Security SY0-701 exam or pursue a career in certified security, this book is your perfect resource. It is also a valuable companion for US government and US Department of Defense personnel looking to achieve cybersecurity certification. It serves as an excellent reference guide for college students pursuing a degree in cybersecurity. Table of Contents - Compare and Contrast Various Types of Security Controls - Summarize Fundamental Security Concepts - Explain the Importance of Change Management Processes and the Impact on Security - Explain the Importance of Using Appropriate Cryptographic Solutions - Compare and Contrast Common Threat Actors and Motivations - Explain Common Threat Vectors and Attack Surfaces - Explain Various Types of Vulnerabilities - Given a Scenario, Analyze Indicators of Malicious Activity - Explain the Purpose of Mitigation Techniques Used to Secure the Enterprise - Compare and Contrast Security Implications of Different Architecture Models (N.B. Please use the Read Sample option to see further chapters...

-Titulo Original : LLM Engineer's Handbook: Master the art of engineering large language models from concept to production-Fabricante : Packt Publishing-Descripcion Original: Step into the world of LLMs with this practical guide that takes you from the fundamentals to deploying advanced applications using LLMOps best practices Purchase of the print or Kindle book includes a free in format “This book is instrumental in making sure that as many people as possible can not only use LLMs but also adapt them, fine-tune them, quantize them, and make them efficient enough to deploy in the real world.”- Julien Chaumond, CTO and Co-founder, Hugging Face Book Description This LLM book provides practical insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. The guide walks you through building an LLM-powered twin that’s cost-effective, scalable, and modular. It moves beyond isolated Jupyter Notebooks, focusing on how to build production-grade end-to-end LLM systems. Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects. What you will learn Implement robust data pipelines and manage LLM training cycles Create your own LLM and refine with the help of hands-on examples Get started with LLMOps by diving into core MLOps principles like IaC Perform supervised fine-tuning and LLM evaluation Deploy end-to-end LLM solutions using AWS and other tools Explore continuous training, monitoring, and logic automation Learn about RAG ingestion as well as inference and feature pipelines Who this book is for This book is for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. Basic knowledge of LLMs and the Gen AI landscape, Python and AWS is recommended. Whether you are new to AI or looking to enhance your skills, this book provides comprehensive guidance on implementing LLMs in real-world scenarios. Table of Contents Undersstanding the LLM Twin Concept and Architecture Tooling and Installation Data Engineering RAG Feature Pipeline Supervised Fine-tuning Fine-tuning with Preference Alignment Evaluating LLMs Inference Optimization RAG Inference Pipeline Inference Pipeline Deployment MLOps and LLMOps Appendix: MLOps Principle...

-Titulo Original : Hands-on Data Analysis With Pandas A Python Data Science Handbook For Data Collection, Wrangling, Analysis, And Visualization, 2nd Edition-Fabricante : Packt Publishing-Descripcion Original: Get to grips with pandas - a fast, versatile, and high-performance Python library for data discovery, data manipulation, data preparation, and handling data for analytical tasks Key Features Perform efficient data analysis and manipulation tasks using pandas 1.x Apply pandas to different real-world domains with the help of step-by-step examples Become well-versed in using pandas as an effective data exploration tool Book Description Data analysis has become an essential skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making-valuable knowledge that can be applied across multiple domains. What you will learn Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Solve common data representation and analysis problems using pandas Build Python scripts, modules, and packages for reusable analysis code Who this book is for This book is for data science beginners, data analysts, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You’ll also find this book useful if you are a data scientist looking to implement pandas in your machine learning workflow. Working knowledge of the Python programming language will assist with understanding the key concepts covered in this book; however, a Python crash-course tutorial is provided in the code bundle for anyone who needs a refresher. Table of Contents Introduction to Data Analysis Working with Pandas DataFrames Data Wrangling with Pandas Aggregating Pandas DataFrames Visualizing Data with Pandas and Matplotlib Plotting with Seaborn and Customization Techniques Financial Analysis - Bitcoin and the Stock Market Rule-Based Anomaly Detection Getting Started with Machine Learning in Python Making Better Predictions - Optimizing Models Machine Learning Anomaly Detection The Road Ahead About the Author Stefanie Molin is a data scientist and software engineer at Bloomberg LP in NYC, tackling tough problems in information security, particularly revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She has extensive experience in data science, designing anomaly detection solutions, and utilizing machine learning in both R and Python in the AdTech and FinTech industries. She holds a B.S. in operations research from Columbia Universitys Fu Foundation School of Engineering and Applied Science, with minors in economics, and entrepreneurship and innovation. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers...

-Titulo Original : Machine Learning With Pytorch And Scikit-learn Develop Machine Learning And Deep Learning Models With Python-Fabricante : Packt Publishing-Descripcion Original: This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorchs simple to code framework. Purchase of the print or Kindle book includes a free in format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book Description Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference youll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether youre a Python developer new to machine learning or want to deepen your knowledge of the latest developments. What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, youll need a good understanding of calculus, as well as linear algebra. Table of Contents Giving Computers the Ability to Learn from Data Training Simple Machine Learning Algorithms for Classification A Tour of Machine Learning Classifiers Using Scikit-Learn Building Good Training Datasets - Data Preprocessing Compressing Data via Dimensionality Reduction Learning Best Practices for Model Evaluation and Hyperparameter Tuning Combining Different Models for Ensemble Learning Applying Machine Learning to Sentiment Analysis Predicting Continuous Target Variables with Regression Analysis Working with Unlabeled Data - Clustering Analysis (N.B. Please use the Look Inside option to see further chapters...