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Learning to Program in Python

Learning to Program in Python PDF Author: P. M. Heathcote
Publisher:
ISBN: 9781910523117
Category : Computer programming
Languages : en
Pages : 120

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Book Description
Teaches basic syntax and programming techniques and introduces three modules; Tkinter, SQLite, and pdb.

Learning to Program in Python

Learning to Program in Python PDF Author: P. M. Heathcote
Publisher:
ISBN: 9781910523117
Category : Computer programming
Languages : en
Pages : 120

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Book Description
Teaches basic syntax and programming techniques and introduces three modules; Tkinter, SQLite, and pdb.

Mission Python

Mission Python PDF Author: Sheela Preuitt
Publisher: Lerner Publications ™
ISBN: 1541569563
Category : Juvenile Nonfiction
Languages : en
Pages : 32

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Book Description
The Python programming language can be used on any operating system. Readers will learn how it is used to process text, images, numbers, and more! A digital Page Plus feature invites readers to experiment with their own coding activities.

Python Machine Learning

Python Machine Learning PDF Author: Sebastian Raschka
Publisher: Packt Publishing Ltd
ISBN: 1787126021
Category : Computers
Languages : en
Pages : 622

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Book Description
Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. About This Book Second edition of the bestselling book on Machine Learning A practical approach to key frameworks in data science, machine learning, and deep learning Use the most powerful Python libraries to implement machine learning and deep learning Get to know the best practices to improve and optimize your machine learning systems and algorithms Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data. What You Will Learn Understand the key frameworks in data science, machine learning, and deep learning Harness the power of the latest Python open source libraries in machine learning Explore machine learning techniques using challenging real-world data Master deep neural network implementation using the TensorFlow library Learn the mechanics of classification algorithms to implement the best tool for the job Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Delve deeper into textual and social media data using sentiment analysis In Detail 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. Thoroughly updated using the latest Python 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 deep learning library. The scikit-learn code has also been fully updated to include recent 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 in today's world. If you've read the first edition of this book, you'll be delighted to find a new 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 more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn. Style and Approach Python Machine Learning Second Edition takes a practical, hands-on coding approach so you can learn about machine learning by coding with Python. This book moves fluently between the theoretical principles of machine learning and the practical details of implementation with Python.

Learn to Program with Python 3

Learn to Program with Python 3 PDF Author: Irv Kalb
Publisher: Apress
ISBN: 1484238796
Category : Computers
Languages : en
Pages : 354

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Book Description
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.

Robotics in Education

Robotics in Education PDF Author: Wilfried Lepuschitz
Publisher: Springer
ISBN: 3319628755
Category : Technology & Engineering
Languages : en
Pages : 330

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Book Description
This proceedings volume highlights the latest achievements in research and development in educational robotics, which were presented at the 8th International Conference on Robotics in Education (RiE 2017) in Sofia, Bulgaria, from April 26 to 28, 2017. The content will appeal to both researchers and educators interested in methodologies for teaching robotics that confront learners with science, technology, engineering, arts and mathematics (STEAM) through the design, creation and programming of tangible artifacts, giving them the chance to create personally meaningful objects and address real-world societal needs. This also involves the introduction of technologies ranging from robotics controllers to virtual environments. In addition, the book presents evaluation results regarding the impact of robotics on students’ interests and competence development. The approaches discussed cover the whole educational range, from elementary school to the university level, in both formal as well as informal settings.

Python for Kids

Python for Kids PDF Author: Jason R. Briggs
Publisher: No Starch Press
ISBN: 1593274076
Category : Computers
Languages : en
Pages : 348

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Book Description
Python is a powerful, expressive programming language that’s easy to learn and fun to use! But books about learning to program in Python can be kind of dull, gray, and boring, and that’s no fun for anyone. Python for Kids brings Python to life and brings you (and your parents) into the world of programming. The ever-patient Jason R. Briggs will guide you through the basics as you experiment with unique (and often hilarious) example programs that feature ravenous monsters, secret agents, thieving ravens, and more. New terms are defined; code is colored, dissected, and explained; and quirky, full-color illustrations keep things on the lighter side. Chapters end with programming puzzles designed to stretch your brain and strengthen your understanding. By the end of the book you’ll have programmed two complete games: a clone of the famous Pong and "Mr. Stick Man Races for the Exit"—a platform game with jumps, animation, and much more. As you strike out on your programming adventure, you’ll learn how to: –Use fundamental data structures like lists, tuples, and maps –Organize and reuse your code with functions and modules –Use control structures like loops and conditional statements –Draw shapes and patterns with Python’s turtle module –Create games, animations, and other graphical wonders with tkinter Why should serious adults have all the fun? Python for Kids is your ticket into the amazing world of computer programming. For kids ages 10+ (and their parents) The code in this book runs on almost anything: Windows, Mac, Linux, even an OLPC laptop or Raspberry Pi!

Advanced Python Programming

Advanced Python Programming PDF Author: Dr. Gabriele Lanaro
Publisher: Packt Publishing Ltd
ISBN: 183855369X
Category : Computers
Languages : en
Pages : 672

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Book Description
Create distributed applications with clever design patterns to solve complex problems Key FeaturesSet up and run distributed algorithms on a cluster using Dask and PySparkMaster skills to accurately implement concurrency in your codeGain practical experience of Python design patterns with real-world examplesBook Description This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing. By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems. This Learning Path includes content from the following Packt products: Python High Performance - Second Edition by Gabriele LanaroMastering Concurrency in Python by Quan NguyenMastering Python Design Patterns by Sakis KasampalisWhat you will learnUse NumPy and pandas to import and manipulate datasetsAchieve native performance with Cython and NumbaWrite asynchronous code using asyncio and RxPyDesign highly scalable programs with application scaffoldingExplore abstract methods to maintain data consistencyClone objects using the prototype patternUse the adapter pattern to make incompatible interfaces compatibleEmploy the strategy pattern to dynamically choose an algorithmWho this book is for This Learning Path is specially designed for Python developers who want to build high-performance applications and learn about single core and multi-core programming, distributed concurrency, and Python design patterns. Some experience with Python programming language will help you get the most out of this Learning Path.

Digital Technologies and Instructional Design for Personalized Learning

Digital Technologies and Instructional Design for Personalized Learning PDF Author: Zheng, Robert
Publisher: IGI Global
ISBN: 1522539417
Category : Education
Languages : en
Pages : 391

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Book Description
When facilitating high-quality education, using digital technology to personalize students' learning is a focus in the development of instruction. There is a need to unify the multifaceted directions in personalized learning by presenting a coherent and organized vision in the design of personalized learning using digital technology. Digital Technologies and Instructional Design for Personalized Learning is a critical scholarly resource that highlights the theories, principles, and learning strategies in personalized learning with digital technology. Featuring coverage on a broad range of topics, such as collaborative learning, instructional design, and computer-supported collaborative learning, this book is geared towards educators, professionals, school administrators, academicians, researchers, and students seeking current research on the area of personalized learning with digital technology.

Research Anthology on Computational Thinking, Programming, and Robotics in the Classroom

Research Anthology on Computational Thinking, Programming, and Robotics in the Classroom PDF Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1668424126
Category : Education
Languages : en
Pages : 969

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Book Description
The education system is constantly growing and developing as more ways to teach and learn are implemented into the classroom. Recently, there has been a growing interest in teaching computational thinking with schools all over the world introducing it to the curriculum due to its ability to allow students to become proficient at problem solving using logic, an essential life skill. In order to provide the best education possible, it is imperative that computational thinking strategies, along with programming skills and the use of robotics in the classroom, be implemented in order for students to achieve maximum thought processing skills and computer competencies. The Research Anthology on Computational Thinking, Programming, and Robotics in the Classroom is an all-encompassing reference book that discusses how computational thinking, programming, and robotics can be used in education as well as the benefits and difficulties of implementing these elements into the classroom. The book includes strategies for preparing educators to teach computational thinking in the classroom as well as design techniques for incorporating these practices into various levels of school curriculum and within a variety of subjects. Covering topics ranging from decomposition to robot learning, this book is ideal for educators, computer scientists, administrators, academicians, students, and anyone interested in learning more about how computational thinking, programming, and robotics can change the current education system.

A Greater Foundation for Machine Learning Engineering

A Greater Foundation for Machine Learning Engineering PDF Author: Dr. Ganapathi Pulipaka
Publisher: Xlibris Corporation
ISBN: 1664151273
Category : Computers
Languages : en
Pages : 510

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Book Description
This research scholarly illustrated book has more than 250 illustrations. The simple models of supervised machine learning with Gaussian Naïve Bayes, Naïve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest neighbors, and support vector machines lay a more excellent foundation for statistics. The author of the book Dr. Ganapathi Pulipaka, a top influencer of machine learning in the US, has created this as a reference book for universities. This book contains an incredible foundation for machine learning and engineering beyond a compact manual. The author goes to extraordinary lengths to make academic machine learning and deep learning literature comprehensible to create a new body of knowledge. The book aims at readership from university students, enterprises, data science beginners, machine learning and deep learning engineers at scale for high-performance computing environments. A Greater Foundation of Machine Learning Engineering covers a broad range of classical linear algebra and calculus with program implementations in PyTorch, TensorFlow, R, and Python with in-depth coverage. The author does not hesitate to go into math equations for each algorithm at length that usually many foundational machine learning books lack leveraging the JupyterLab environment. Newcomers can leverage the book from University or people from all walks of data science or software lives to the advanced practitioners of machine learning and deep learning. Though the book title suggests machine learning, there are several implementations of deep learning algorithms, including deep reinforcement learning. The book's mission is to help build a strong foundation for machine learning and deep learning engineers with all the algorithms, processors to train and deploy into production for enterprise-wide machine learning implementations. This book also introduces all the concepts of natural language processing required for machine learning algorithms in Python. The book covers Bayesian statistics without assuming high-level mathematics or statistics experience from the readers. It delivers the core concepts and implementations required with R code with open datasets. The book also covers unsupervised machine learning algorithms with association rules and k-means clustering, metal-learning algorithms, bagging, boosting, random forests, and ensemble methods. The book delves into the origins of deep learning in a scholarly way covering neural networks, restricted Boltzmann machines, deep belief networks, autoencoders, deep Boltzmann machines, LSTM, and natural language processing techniques with deep learning algorithms and math equations. It leverages the NLTK library of Python with PyTorch, Python, and TensorFlow's installation steps, then demonstrates how to build neural networks with TensorFlow. Deploying machine learning algorithms require a blend of cloud computing platforms, SQL databases, and NoSQL databases. Any data scientist with a statistics background that looks to transition into a machine learning engineer role requires an in-depth understanding of machine learning project implementations on Amazon, Google, or Microsoft Azure cloud computing platforms. The book provides real-world client projects for understanding the complete implementation of machine learning algorithms. This book is a marvel that does not leave any application of machine learning and deep learning algorithms. It sets a more excellent foundation for newcomers and expands the horizons for experienced deep learning practitioners. It is almost inevitable that there will be a series of more advanced algorithms follow-up books from the author in some shape or form after setting such a perfect foundation for machine learning engineering.