Wydawca: Packt Publishing
Giuseppe Ciaburro, Prateek Joshi
This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks.With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning.By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples.
Sebastian Raschka
Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you’ll soon be able to answer some of the most important questions facing you and your organization.
Sebastian Raschka, Vahid Mirjalili
Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new third edition, updated for 2020 and featuring TensorFlow 2 and the latest in scikit-learn, reinforcement learning, and GANs, has now been published.Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Using Python's open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities.If you’ve read the first edition of this book, you’ll be delighted to find a balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You’ll be able to learn and work with TensorFlow 1.x more deeply than ever before, and get essential coverage of the Keras neural network library, along with updates to scikit-learn 0.18.1.
Sebastian Raschka, Vahid Mirjalili
Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.Packed with clear explanations, visualizations, and working 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, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.
Python Made Easy. A First Course in Computer Programming Using Python
Kevin Wilson
Python Made Easy is designed to transform beginners into proficient Python programmers. The journey begins with an introduction to Python, covering basic concepts and syntax that lay the foundation for your coding skills. As you progress, you'll dive into essential programming constructs like data structures, functions, and file handling.In the second phase of the course, you'll explore more complex topics such as object-oriented programming, modules, and libraries. These sections will give you the tools to write efficient, reusable, and modular code. You'll also learn how to handle exceptions, ensuring your programs are robust and error-resistant. Special attention is given to graphical user interfaces (GUIs) and game development, making your Python skills applicable to a wide range of projects.The final part of the course covers advanced topics like debugging, testing, and deploying Python applications. You'll also delve into web development, where you'll learn to build and deploy web applications using Python. The course concludes with valuable video resources to reinforce your learning and provide additional insights. By the end of this course, you'll have a solid understanding of Python and be ready to tackle real-world programming challenges.
Python: Master the Art of Design Patterns. Click here to enter text
Dusty Phillips, Chetan Giridhar, Sakis Kasampalis
Python is an object-oriented scripting language that is used in everything from data science to web development. Known for its simplicity, Python increases productivity and minimizes development time. Through applying essential software engineering design patterns to Python, Python code becomes even more efficient and reusable from project to project. This learning path takes you through every traditional and advanced design pattern best applied to Python code, building your skills in writing exceptional Python. Divided into three distinct modules, you’ll go from foundational to advanced concepts by following a series of practical tutorials.Start with the bedrock of Python programming – the object-oriented paradigm. Rethink the way you work with Python as you work through the Python data structures and object-oriented techniques essential to modern Python programming. Build your confidence as you learn Python syntax, and how to use OOP principles with Python tools such as Django and Kivy.In the second module, run through the most common and most useful design patterns from a Python perspective. Progress through Singleton patterns, Factory patterns, Façade patterns and more all with detailed hands-on guidance. Enhance your professional abilities in in software architecture, design, and development.In the final module, run through the more complex and less common design patterns, discovering how to apply them to Python coding with the help of real-world examples. Get to grips with the best practices of writing Python, as well as creating systems architecture and troubleshooting issues.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:? Python 3 Object-Oriented Programming - Second Edition by Dusty Phillips? Learning Python Design Patterns - Second Edition by Chetan Giridhar? Mastering Python Design Patterns by Sakis Kasampalis
Simon Fraser, Tarek Ziadé
The small scope and self-contained nature of microservices make them faster, cleaner, and more scalable than code-heavy monolithic applications. However, building microservices architecture that is efficient as well as lightweight into your applications can be challenging due to the complexity of all the interacting pieces.Python Microservices Development, Second Edition will teach you how to overcome these issues and craft applications that are built as small standard units using proven best practices and avoiding common pitfalls. Through hands-on examples, this book will help you to build efficient microservices using Quart, SQLAlchemy, and other modern Python toolsIn this updated edition, you will learn how to secure connections between services and how to script Nginx using Lua to build web application firewall features such as rate limiting. Python Microservices Development, Second Edition describes how to use containers and AWS to deploy your services. By the end of the book, you’ll have created a complete Python application based on microservices.
Python Microservices Development. Build, test, deploy, and scale microservices in Python
Tarek Ziadé
We often deploy our web applications into the cloud, and our code needs to interact with many third-party services. An efficient way to build applications to do this is through microservices architecture. But, in practice, it's hard to get this right due to the complexity of all the pieces interacting with each other.This book will teach you how to overcome these issues and craft applications that are built as small standard units, using all the proven best practices and avoiding the usual traps. It's a practical book: you’ll build everything using Python 3 and its amazing tooling ecosystem. You will understand the principles of TDD and apply them. You will use Flask, Tox, and other tools to build your services using best practices. You will learn how to secure connections between services, and how to script Nginx using Lua to build web application firewall features such as rate limiting. You will also familiarize yourself with Docker’s role in microservices, and use Docker containers, CoreOS, and Amazon Web Services to deploy your services.This book will take you on a journey, ending with the creation of a complete Python application based on microservices. By the end of the book, you will be well versed with the fundamentals of building, designing, testing, and deploying your Python microservices.