Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages. Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3— the latest releases in the 3.X and 2.X lines—plus all other releases in common use today. You’ll also learn some advanced language features that recently have become more common in Python code. Explore Python’s major built-in object types such as numbers, lists, and dictionaries Create and process objects with Python statements, and learn Python’s general syntax model Use functions to avoid code redundancy and package code for reuse Organize statements, functions, and other tools into larger components with modules Dive into classes: Python’s object-oriented programming tool for structuring code Write large programs with Python’s exception-handling model and development tools Learn advanced Python tools, including decorators, descriptors, metaclasses, and Unicode processing
Author: Zed A. Shaw
Publisher: Addison-Wesley Professional
Release Date: 2017-06-26
You Will Learn Python 3! Zed Shaw has perfected the world’s best system for learning Python 3. Follow it and you will succeed—just like the millions of beginners Zed has taught to date! You bring the discipline, commitment, and persistence; the author supplies everything else. In Learn Python 3 the Hard Way, you’ll learn Python by working through 52 brilliantly crafted exercises. Read them. Type their code precisely. (No copying and pasting!) Fix your mistakes. Watch the programs run. As you do, you’ll learn how a computer works; what good programs look like; and how to read, write, and think about code. Zed then teaches you even more in 5+ hours of video where he shows you how to break, fix, and debug your code—live, as he’s doing the exercises. Install a complete Python environment Organize and write code Fix and break code Basic mathematics Variables Strings and text Interact with users Work with files Looping and logic Data structures using lists and dictionaries Program design Object-oriented programming Inheritance and composition Modules, classes, and objects Python packaging Automated testing Basic game development Basic web development It’ll be hard at first. But soon, you’ll just get it—and that will feel great! This course will reward you for every minute you put into it. Soon, you’ll know one of the world’s most powerful, popular programming languages. You’ll be a Python programmer. This Book Is Perfect For Total beginners with zero programming experience Junior developers who know one or two languages Returning professionals who haven’t written code in years Seasoned professionals looking for a fast, simple, crash course in Python 3
Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail 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. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Fran�ois Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning--a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Fran�ois Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author Fran�ois Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance
Author: Dr. M. O. Faruque Sarker
Publisher: Packt Publishing Ltd
Release Date: 2015-06-17
Network programming has always been a demanding task. With full-featured and well documented libraries all the way up the stack, Python makes network programming the enjoyable experience it should be. Starting with a walkthrough of today's major networking protocols, with this book you'll learn how to employ Python for network programming, how to request and retrieve web resources, and how to extract data in major formats over the Web. You'll utilize Python for e-mailing using different protocols and you'll interact with remote systems and IP and DNS networking. As the book progresses, socket programming will be covered, followed by how to design servers and the pros and cons of multithreaded and event-driven architectures. You'll develop practical client-side applications, including web API clients, e-mail clients, SSH, and FTP. These applications will also be implemented through existing web application frameworks.
Author: Andreas C. Müller
Publisher: "O'Reilly Media, Inc."
Release Date: 2016-09-26
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills
Author: Jason R. Briggs
Publisher: No Starch Press
Release Date: 2013
Genre: Juvenile Nonfiction
Introduces the basics of the Python programming language, covering how to use data structures, organize and reuse code, draw shapes and patterns with turtle, and create games and animations with tkinter.
Author: Allen B. Downey
Publisher: "O'Reilly Media, Inc."
Release Date: 2015-12-02
If you want to learn how to program, working with Python is an excellent way to start. This hands-on guide takes you through the language a step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design. This second edition and its supporting code have been updated for Python 3. Through exercises in each chapter, you’ll try out programming concepts as you learn them. Think Python is ideal for students at the high school or college level, as well as self-learners, home-schooled students, and professionals who need to learn programming basics. Beginners just getting their feet wet will learn how to start with Python in a browser. Start with the basics, including language syntax and semantics Get a clear definition of each programming concept Learn about values, variables, statements, functions, and data structures in a logical progression Discover how to work with files and databases Understand objects, methods, and object-oriented programming Use debugging techniques to fix syntax, runtime, and semantic errors Explore interface design, data structures, and GUI-based programs through case studies
More physicists today are taking on the role of software developer as part of their research, but software development isn’t always easy or obvious, even for physicists. This practical book teaches essential software development skills to help you automate and accomplish nearly any aspect of research in a physics-based field. Written by two PhDs in nuclear engineering, this book includes practical examples drawn from a working knowledge of physics concepts. You’ll learn how to use the Python programming language to perform everything from collecting and analyzing data to building software and publishing your results. In four parts, this book includes: Getting Started: Jump into Python, the command line, data containers, functions, flow control and logic, and classes and objects Getting It Done: Learn about regular expressions, analysis and visualization, NumPy, storing data in files and HDF5, important data structures in physics, computing in parallel, and deploying software Getting It Right: Build pipelines and software, learn to use local and remote version control, and debug and test your code Getting It Out There: Document your code, process and publish your findings, and collaborate efficiently; dive into software licenses, ownership, and copyright procedures
Already the industry standard for Python users, ProgrammingPython fromO'Reilly just got even better. This third edition has been updated toreflect current best practices andthe abundance of changes introduced by the latest version of thelanguage, Python 2.5. Whether you're a novice or an advancedpractitioner, you'll find thisrefreshed book more than lives up to its reputation. ProgrammingPython, 3rd Edition, teaches you the rightway to code. It explains Python language syntax and programmingtechniques in a clear and concisemanner, with numerous examples that illustrate both correct usage andcommon idioms. By reading thiscomprehensive guide, you'll learn how to apply Python in real-worldproblem domains such as: GUI programming Internet scripting Parallel processing Database management Networked applications Programming Python, Third Edition coverseach of thesetarget domainsgradually, beginning with in-depth discussions of core concepts andthen progressing toward completeprograms. Large examples do appear, but only after you've learnedenough to understand their techniques andcode. Along the way, you'll also learn how to use the Python language inrealistically scaled programs--concepts such as Object-Oriented Programming (OOP) and code reuseare recurring side themes throughout thistext. If you're interested in Python programming, then this O'Reillyclassic needs to be within arm's reach. Thewealth of practical advice, snippets of code, and patterns of programdesign can all be put into use on adaily basis--making your life easier and more productive. Reviews of the second edition: "...about as comprehensive as any book can be." --Dr. Dobb's Journal "If the language had manuals, they would undoubtedlybe the texts from O'Reilly...'Learning Python' and 'Programming Python'are definitive treatments." --SD Times
Author: Al Sweigart
Publisher: No Starch Press
Release Date: 2015-04-14
If you’ve ever spent hours renaming files or updating hundreds of spreadsheet cells, you know how tedious tasks like these can be. But what if you could have your computer do them for you? In Automate the Boring Stuff with Python, you’ll learn how to use Python to write programs that do in minutes what would take you hours to do by hand—no prior programming experience required. Once you’ve mastered the basics of programming, you’ll create Python programs that effortlessly perform useful and impressive feats of automation to: –Search for text in a file or across multiple files –Create, update, move, and rename files and folders –Search the Web and download online content –Update and format data in Excel spreadsheets of any size –Split, merge, watermark, and encrypt PDFs –Send reminder emails and text notifications –Fill out online forms Step-by-step instructions walk you through each program, and practice projects at the end of each chapter challenge you to improve those programs and use your newfound skills to automate similar tasks. Don’t spend your time doing work a well-trained monkey could do. Even if you’ve never written a line of code, you can make your computer do the grunt work. Learn how in Automate the Boring Stuff with Python. Note: The programs in this book are written to run on Python 3.
DescriptionThis book provides the concept of machine learning with mathematical explanation and programming examples. Every chapter starts with fundamentals of the technique and working example on the real-world dataset. Along with the advice on applying algorithms, each technique is provided with advantages and disadvantages on the data.In this book we provide code examples in python. Python is the most suitable and worldwide accepted language for this. First, it is free and open source. It contains very good support from open community. It contains a lot of library, so you don't need to code everything. Also, it is scalable for large amount of data and suitable for big data technologies.This book:Covers all major areas in Machine Learning.Topics are discussed with graphical explanations.Comparison of different Machine Learning methods to solve any problem.Methods to handle real-world noisy data before applying any Machine Learning algorithm.Python code example for each concept discussed.Jupyter notebook scripts are provided with dataset used to test and try the algorithms ContentsIntroduction to Machine Learning Understanding Python Feature Engineering Data VisualisationBasic and Advanced Regression techniquesClassification Un Supervised LearningText AnalysisNeural Network and Deep Learning Recommendation System Time Series Analysis
Move from zero knowledge of programming to comfortably writing small to medium-sized programs in Python. Fully updated for Python 3, with code and examples throughout, the book explains Python coding with an accessible, step-by-step approach designed to bring you comfortably into the world of software development. Real–world analogies make the material understandable, with a wide variety of well-documented examples to illustrate each concept. Along the way, you’ll develop short programs through a series of coding challenges that reinforce the content of the chapters. Learn to Program with Python 3 guides you with material developed in the author's university computer science courses. The author's conversational style feels like you're working with a personal tutor. All material is thoughtfully laid out, each lesson building on previous ones. What You'll Learn Understand programming basics with Python, based on material developed in the author's college courses Learn core concepts: variables, functions, conditionals, loops, lists, strings, and more Explore example programs including simple games you can program and customize Build modules to reuse your own code Who This Book Is For This book assumes no prior programming experience, and would be appropriate as text for a high school or college introduction to computer science.