Learning Python

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Publisher : "O'Reilly Media, Inc."
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ISBN 10 : 9781449379322
Pages : 1216 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.7/5 (379 users download)


Download Learning Python by Mark Lutz PDF/Ebook Free clicking on the below button will initiate the downloading process of Learning Python by Mark Lutz. This book is available in ePub and PDF format with a single click unlimited downloads. Google and YouTube use Python because it's highly adaptable, easy to maintain, and allows for rapid development. If you want to write high-quality, efficient code that's easily integrated with other languages and tools, this hands-on book will help you be productive with Python quickly -- whether you're new to programming or just new to Python. It's an easy-to-follow self-paced tutorial, based on author and Python expert Mark Lutz's popular training course. Each chapter contains a stand-alone lesson on a key component of the language, and includes a unique Test Your Knowledge section with practical exercises and quizzes, so you can practice new skills and test your understanding as you go. You'll find lots of annotated examples and illustrations to help you get started with Python 3.0. Learn about Python's major built-in object types, such as numbers, lists, and dictionaries Create and process objects using Python statements, and learn Python's general syntax model Structure and reuse code using functions, Python's basic procedural tool Learn about Python modules: packages of statements, functions, and other tools, organized into larger components Discover Python's object-oriented programming tool for structuring code Learn about the exception-handling model, and development tools for writing larger programs Explore advanced Python tools including decorators, descriptors, metaclasses, and Unicode processing


Learning Python for Forensics

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Publisher : Packt Publishing Ltd
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ISBN 10 : 9781789342765
Pages : 476 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.4/5 (342 users download)


Download Learning Python for Forensics by Preston Miller PDF/Ebook Free clicking on the below button will initiate the downloading process of Learning Python for Forensics by Preston Miller. This book is available in ePub and PDF format with a single click unlimited downloads. Learning Python for Forensics, Second Edition begins by introducing you to the fundamentals of Python. You will learn how to develop Python scripts through an iterative design. This book will also help you strengthen your analysis skills and efficiency as you creatively solve real-world problems through instruction-based tutorials.


Applied Deep Learning with Python

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Publisher : Packt Publishing Ltd
Release Date :
ISBN 10 : 9781789806991
Pages : 334 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.0/5 (86 users download)


Download Applied Deep Learning with Python by Alex Galea PDF/Ebook Free clicking on the below button will initiate the downloading process of Applied Deep Learning with Python by Alex Galea. This book is available in ePub and PDF format with a single click unlimited downloads. A hands-on guide to deep learning that’s filled with intuitive explanations and engaging practical examples Key Features Designed to iteratively develop the skills of Python users who don’t have a data science background Covers the key foundational concepts you’ll need to know when building deep learning systems Full of step-by-step exercises and activities to help build the skills that you need for the real-world Book Description Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before we train our first predictive model. We’ll explore a variety of approaches to classification like support vector networks, random decision forests and k-nearest neighbours to build out your understanding before we move into more complex territory. It’s okay if these terms seem overwhelming; we’ll show you how to put them to work. We’ll build upon our classification coverage by taking a quick look at ethical web scraping and interactive visualizations to help you professionally gather and present your analysis. It’s after this that we start building out our keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. By guiding you through a trained neural network, we’ll explore common deep learning network architectures (convolutional, recurrent, generative adversarial) and branch out into deep reinforcement learning before we dive into model optimization and evaluation. We’ll do all of this whilst working on a production-ready web application that combines Tensorflow and Keras to produce a meaningful user-friendly result, leaving you with all the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively. What you will learn Discover how you can assemble and clean your very own datasets Develop a tailored machine learning classification strategy Build, train and enhance your own models to solve unique problems Work with production-ready frameworks like Tensorflow and Keras Explain how neural networks operate in clear and simple terms Understand how to deploy your predictions to the web Who this book is for If you're a Python programmer stepping into the world of data science, this is the ideal way to get started.


Hands-on Supervised Learning with Python

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Publisher : BPB Publications
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ISBN 10 : 9789389328974
Pages : 384 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.2/5 (328 users download)


Download Hands-on Supervised Learning with Python by Gnana Lakshmi T C PDF/Ebook Free clicking on the below button will initiate the downloading process of Hands-on Supervised Learning with Python by Gnana Lakshmi T C. This book is available in ePub and PDF format with a single click unlimited downloads. Hands-On ML problem solving and creating solutions using Python KEY FEATURES ●Introduction to Python Programming ●Python for Machine Learning ●Introduction to Machine Learning ●Introduction to Predictive Modelling, Supervised and Unsupervised Algorithms ●Linear Regression, Logistic Regression and Support Vector Machines DESCRIPTION You will learn about the fundamentals of Machine Learning and Python programming post, which you will be introduced to predictive modelling and the different methodologies in predictive modelling. You will be introduced to Supervised Learning algorithms and Unsupervised Learning algorithms and the difference between them. We will focus on learning supervised machine learning algorithms covering Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees and Artificial Neural Networks. For each of these algorithms, you will work hands-on with open-source datasets and use python programming to program the machine learning algorithms. You will learn about cleaning the data and optimizing the features to get the best results out of your machine learning model. You will learn about the various parameters that determine the accuracy of your model and how you can tune your model based on the reflection of these parameters. WHAT WILL YOU LEARN ●Get a clear vision of what is Machine Learning and get familiar with the foundation principles of Machine learning. ●Understand the Python language-specific libraries available for Machine learning and be able to work with those libraries. ●Explore the different Supervised Learning based algorithms in Machine Learning and know how to implement them when a real-time use case is presented to you. ●Have hands-on with Data Exploration, Data Cleaning, Data Preprocessing and Model implementation. ●Get to know the basics of Deep Learning and some interesting algorithms in this space. ●Choose the right model based on your problem statement and work with EDA techniques to get good accuracy on your model WHO THIS BOOK IS FOR This book is for anyone interested in understanding Machine Learning. Beginners, Machine Learning Engineers and Data Scientists who want to get familiar with Supervised Learning algorithms will find this book helpful. TABLE OF CONTENTS 1. Introduction to Python Programming 2. Python for Machine Learning 3. Introduction to Machine Learning 4. Supervised Learning and Unsupervised Learning 5. Linear Regression: A Hands-on guide 6. Logistic Regression – An Introduction 7. A sneak peek into the working of Support Vector machines(SVM) 8. Decision Trees 9. Random Forests 10. Time Series models in Machine Learning 11. Introduction to Neural Networks 12. Recurrent Neural Networks 13. Convolutional Neural Networks 14. Performance Metrics 15. Introduction to Design Thinking 16. Design Thinking Case Study


Machine Learning for Decision Sciences with Case Studies in Python

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Publisher : CRC Press
Release Date :
ISBN 10 : 9781000590937
Pages : 476 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.9/5 (59 users download)


Download Machine Learning for Decision Sciences with Case Studies in Python by S. Sumathi PDF/Ebook Free clicking on the below button will initiate the downloading process of Machine Learning for Decision Sciences with Case Studies in Python by S. Sumathi. This book is available in ePub and PDF format with a single click unlimited downloads. This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learning. This book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.


Learning Data Mining with Python

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Publisher : Packt Publishing Ltd
Release Date :
ISBN 10 : 9781784391201
Pages : 344 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.9/5 (391 users download)


Download Learning Data Mining with Python by Robert Layton PDF/Ebook Free clicking on the below button will initiate the downloading process of Learning Data Mining with Python by Robert Layton. This book is available in ePub and PDF format with a single click unlimited downloads. The next step in the information age is to gain insights from the deluge of data coming our way. Data mining provides a way of finding this insight, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis. This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. Next, we move on to more complex data types including text, images, and graphs. In every chapter, we create models that solve real-world problems. There is a rich and varied set of libraries available in Python for data mining. This book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK. Each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will gain a large insight into using Python for data mining, with a good knowledge and understanding of the algorithms and implementations.


Learning Concurrency in Python

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Publisher : Packt Publishing Ltd
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ISBN 10 : 9781787283169
Pages : 360 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.8/5 (283 users download)


Download Learning Concurrency in Python by Elliot Forbes PDF/Ebook Free clicking on the below button will initiate the downloading process of Learning Concurrency in Python by Elliot Forbes. This book is available in ePub and PDF format with a single click unlimited downloads. Practically and deeply understand concurrency in Python to write efficient programs About This Book Build highly efficient, robust, and concurrent applications Work through practical examples that will help you address the challenges of writing concurrent code Improve the overall speed of execution in multiprocessor and multicore systems and keep them highly available Who This Book Is For This book is for Python developers who would like to get started with concurrent programming. Readers are expected to have a working knowledge of the Python language, as this book will build on these fundamentals concepts. What You Will Learn Explore the concept of threading and multiprocessing in Python Understand concurrency with threads Manage exceptions in child threads Handle the hardest part in a concurrent system — shared resources Build concurrent systems with Communicating Sequential Processes (CSP) Maintain all concurrent systems and master them Apply reactive programming to build concurrent systems Use GPU to solve specific problems In Detail Python is a very high level, general purpose language that is utilized heavily in fields such as data science and research, as well as being one of the top choices for general purpose programming for programmers around the world. It features a wide number of powerful, high and low-level libraries and frameworks that complement its delightful syntax and enable Python programmers to create. This book introduces some of the most popular libraries and frameworks and goes in-depth into how you can leverage these libraries for your own high-concurrent, highly-performant Python programs. We'll cover the fundamental concepts of concurrency needed to be able to write your own concurrent and parallel software systems in Python. The book will guide you down the path to mastering Python concurrency, giving you all the necessary hardware and theoretical knowledge. We'll cover concepts such as debugging and exception handling as well as some of the most popular libraries and frameworks that allow you to create event-driven and reactive systems. By the end of the book, you'll have learned the techniques to write incredibly efficient concurrent systems that follow best practices. Style and approach This easy-to-follow guide teaches you new practices and techniques to optimize your code, and then moves toward more advanced ways to effectively write efficient Python code. Small and simple practical examples will help you test the concepts yourself, and you will be able to easily adapt them for any application.


Machine Learning in Python

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Publisher : John Wiley & Sons
Release Date :
ISBN 10 : 9781118961766
Pages : 360 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.6/5 (961 users download)


Download Machine Learning in Python by Michael Bowles PDF/Ebook Free clicking on the below button will initiate the downloading process of Machine Learning in Python by Michael Bowles. This book is available in ePub and PDF format with a single click unlimited downloads. Learn a simpler and more effective way to analyze data andpredict outcomes with Python Machine Learning in Python shows you how to successfullyanalyze data using only two core machine learning algorithms, andhow to apply them using Python. By focusing on two algorithmfamilies that effectively predict outcomes, this book is able toprovide full descriptions of the mechanisms at work, and theexamples that illustrate the machinery with specific, hackablecode. The algorithms are explained in simple terms with no complexmath and applied using Python, with guidance on algorithmselection, data preparation, and using the trained models inpractice. You will learn a core set of Python programmingtechniques, various methods of building predictive models, and howto measure the performance of each model to ensure that the rightone is used. The chapters on penalized linear regression andensemble methods dive deep into each of the algorithms, and you canuse the sample code in the book to develop your own data analysissolutions. Machine learning algorithms are at the core of data analyticsand visualization. In the past, these methods required a deepbackground in math and statistics, often in combination with thespecialized R programming language. This book demonstrates howmachine learning can be implemented using the more widely used andaccessible Python programming language. Predict outcomes using linear and ensemble algorithmfamilies Build predictive models that solve a range of simple andcomplex problems Apply core machine learning algorithms using Python Use sample code directly to build custom solutions Machine learning doesn't have to be complex and highlyspecialized. Python makes this technology more accessible to a muchwider audience, using methods that are simpler, effective, and welltested. Machine Learning in Python shows you how to do this,without requiring an extensive background in math orstatistics.


Applied Supervised Learning with Python

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Publisher : Packt Publishing Ltd
Release Date :
ISBN 10 : 9781789955835
Pages : 404 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.5/5 (955 users download)


Download Applied Supervised Learning with Python by Benjamin Johnston PDF/Ebook Free clicking on the below button will initiate the downloading process of Applied Supervised Learning with Python by Benjamin Johnston. This book is available in ePub and PDF format with a single click unlimited downloads. Explore the exciting world of machine learning with the fastest growing technology in the world Key Features Understand various machine learning concepts with real-world examples Implement a supervised machine learning pipeline from data ingestion to validation Gain insights into how you can use machine learning in everyday life Book Description Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own! What you will learn Understand the concept of supervised learning and its applications Implement common supervised learning algorithms using machine learning Python libraries Validate models using the k-fold technique Build your models with decision trees to get results effortlessly Use ensemble modeling techniques to improve the performance of your model Apply a variety of metrics to compare machine learning models Who this book is for Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you to have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.


Python Machine Learning Illustrated Guide For Beginners & Intermediates

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Publisher : PublishDrive
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ISBN 10 : PKEY:6610000218202
Pages : 148 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4./5 ( users download)


Download Python Machine Learning Illustrated Guide For Beginners & Intermediates by William Sullivan PDF/Ebook Free clicking on the below button will initiate the downloading process of Python Machine Learning Illustrated Guide For Beginners & Intermediates by William Sullivan. This book is available in ePub and PDF format with a single click unlimited downloads. Python Machine Learning Illustrated Guide For Beginners & Intermediates Machines Can Learn ?! Automation and systematization is taking over the world. Slowly but surely we continuously see the rapid expansion of artificial intelligence, self-check out cash registers, automated phone lines, people-less car-washes , etc. The world is changing, find out how python programming ties into machine learning so you don't miss out on this next big trend! This is your beginner's step by step guide with illustrated pictures! Let's face it, machine learning is here to stay for the foreseeable future and will impact the lives billions worldwide! Drastically changing the world we live in the most fundamental ways, from our perceptions, life-style, thinking and in other aspects as well. What You Will Learn Linear & Polynomial Regression Support Vector Machines Decision Trees Random Forest KNN Algorithm Naive Bayes Algorithm Unsupervised Learning Clustering Cross Validation Grid Search And, much, much more! If you want to learn more about python machine learning it is highly recommended you start from the ground up by using this book. Normally books on this subject matter are expensive! Why not start off by making a small and affordable investment with your illustrated beginners guide that walks you through python machine learning step by step Why choose this book? Addresses Fundamental Concepts Goes Straight To The Point, uNo fluff or Nonsense Practical Examples High Quality Diagrams "Noob friendly" (Good For Beginners & Intermediates) Contains Various Aspects of Machine Learning Endorses Learn "By Doing Approach" Concise And To The Point I been working tirelessly to provide you quality books at an affordable price. I believe this book will give you the confidence to tackle python machine learning at a fundamental level. What are you waiting for? Make the greatest investment in YOUR knowledge base right now. Buy your copy now!


Learn Python 3 the Hard Way

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Publisher : Addison-Wesley Professional
Release Date :
ISBN 10 : 9780134693903
Pages : 320 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.9/5 (693 users download)


Download Learn Python 3 the Hard Way by Zed A. Shaw PDF/Ebook Free clicking on the below button will initiate the downloading process of Learn Python 3 the Hard Way by Zed A. Shaw. This book is available in ePub and PDF format with a single click unlimited downloads. 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


The Beginner’s Guide to Learn Python GUI with MySQL and SQLite

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Publisher : SPARTA PUBLISHING
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ISBN 10 :
Pages : 551 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4./5 ( users download)


Download The Beginner’s Guide to Learn Python GUI with MySQL and SQLite by Vivian Siahaan PDF/Ebook Free clicking on the below button will initiate the downloading process of The Beginner’s Guide to Learn Python GUI with MySQL and SQLite by Vivian Siahaan. This book is available in ePub and PDF format with a single click unlimited downloads. This book explains relational theory in practice, and demonstrates through two projects how you can apply it to your use of MySQL and SQLite databases. This book covers the important requirements of teaching databases with a practical and progressive perspective. This book offers the straightforward, practical answers you need to help you do your job. This hands-on tutorial/reference/guide to MySQL and SQLite is not only perfect for students and beginners, but it also works for experienced developers who aren't getting the most from both databases. In designing a GUI and as an IDE, you will make use Qt Designer. In the first chapter, you will learn to use several widgets in PyQt5: Display a welcome message; Use the Radio Button widget; Grouping radio buttons; Displays options in the form of a check box; and Display two groups of check boxes. In chapter two, you will learn to use the following topics: Using Signal / Slot Editor; Copy and place text from one Line Edit widget to another; Convert data types and make a simple calculator; Use the Spin Box widget; Use scrollbars and sliders; Using the Widget List; Select a number of list items from one Widget List and display them on another Widget List widget; Add items to the Widget List; Perform operations on the Widget List; Use the Combo Box widget; Displays data selected by the user from the Calendar Widget; Creating a hotel reservation application; and Display tabular data using Table Widgets. In chapter three, you will learn: How to create the initial three tables project in the School database: Teacher, Class, and Subject tables; How to create database configuration files; How to create a Python GUI for inserting and editing tables; How to create a Python GUI to join and query the three tables. In chapter four, you will learn how to: Create a main form to connect all forms; Create a project will add three more tables to the school database: Student, Parent, and Tuition tables; Create a Python GUI for inserting and editing tables; Create a Python GUI to join and query over the three tables. In chapter five, you will join the six classes, Teacher, TClass, Subject, Student, Parent, and Tuition and make queries over those tables. In chapter six, you will create dan configure database. In this chapter, you will create Suspect table in crime database. This table has eleven columns: suspect_id (primary key), suspect_name, birth_date, case_date, report_date, suspect_ status, arrest_date, mother_name, address, telephone, and photo. You will also create GUI to display, edit, insert, and delete for this table. In chapter seven, you will create a table with the name Feature_Extraction, which has eight columns: feature_id (primary key), suspect_id (foreign key), feature1, feature2, feature3, feature4, feature5, and feature6. The six fields (except keys) will have VARBINARY(MAX) data type. You will also create GUI to display, edit, insert, and delete for this table. In chapter eight, you will create two tables, Police and Investigator. The Police table has six columns: police_id (primary key), province, city, address, telephone, and photo. The Investigator table has eight columns: investigator_id (primary key), investigator_name, rank, birth_date, gender, address, telephone, and photo. You will also create GUI to display, edit, insert, and delete for both tables. In the last chapter, you will create two tables, Victim and Case_File. The Victim table has nine columns: victim_id (primary key), victim_name, crime_type, birth_date, crime_date, gender, address, telephone, and photo. The Case_File table has seven columns: case_file_id (primary key), suspect_id (foreign key), police_id (foreign key), investigator_id (foreign key), victim_id (foreign key), status, and description. You will create GUI to display, edit, insert, and delete for both tables.


Learn Python

Author :
Publisher : Anthony Adams
Release Date :
ISBN 10 :
Pages : 184 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4./5 ( users download)


Download Learn Python by Anthony Adams PDF/Ebook Free clicking on the below button will initiate the downloading process of Learn Python by Anthony Adams. This book is available in ePub and PDF format with a single click unlimited downloads. Do you want to become a coding & programming expert in no time? This guide will teach you how! Are you interested in coding, programming, and artificial intelligence? Would you like to learn Python, but you have no idea how to start? This guide is the answer to all your problems! Python is one of the top 10 popular programming languages, and it can be used to developing desktop GUI applications, websites, and web applications. There are many reasons why learning Python is essential. The syntax rules of Python allow you to express concepts without writing additional code. At the same time, Python, unlike other programming languages, emphasizes code readability and will enable you to use English keywords instead of punctuations. Then, Python has an extensive and robust standard library, which makes it score over other programming languages. Besides, it is an open-source programming language, meaning that it will help you curtail software development cost significantly. Last but not least, Python is designed with features to facilitate data analysis and visualization. You can use it to create custom big data solutions without putting in extra time and effort. Are you excited about learning more about Python and coding? Here is what you can learn from this book: • The importance of data analysis and machine learning • How is Python different from other languages • Learn from more than 25 Python programming examples • All the benefits of learning Python • How can Python help you out with learning other languages Learning Python is easy, even if you’ve never learned about coding before. It offers excellent readability and simple-to-learn syntax, which helps beginners learn this programming language in no time! The software is user-friendly and designed to increase speed and productivity during programming. With Python, you can create any app you want to! Ready to start coding? This book will teach you how to do it and guide you through the coding process! Scroll up, click on "Buy", and Get Your Copy Now!


LEARNING PyQt5

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Publisher : SPARTA PUBLISHING
Release Date :
ISBN 10 :
Pages : 524 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4./5 ( users download)


Download LEARNING PyQt5 by Vivian Siahaan PDF/Ebook Free clicking on the below button will initiate the downloading process of LEARNING PyQt5 by Vivian Siahaan. This book is available in ePub and PDF format with a single click unlimited downloads. In this book, you will learn PyQt5 with accompanied by a step-by-step tutorial to develop mysql-base applications. In the first chapter, you will learn to use several widgets in PyQt5: Display a welcome message; Use the Radio Button widget; Grouping radio buttons; Displays options in the form of a check box; and Display two groups of check boxes. In chapter two, you will learn to use the following topics: Using Signal / Slot Editor; Copy and place text from one Line Edit widget to another; Convert data types and make a simple calculator; Use the Spin Box widget; Use scrollbars and sliders; Using the Widget List; Select a number of list items from one Widget List and display them on another Widget List widget; Add items to the Widget List; Perform operations on the Widget List; Use the Combo Box widget; Displays data selected by the user from the Calendar Widget; Creating a hotel reservation application; and Display tabular data using Table Widgets. In the next three chapters, you will learn Basic MySQL statements including how to implement querying data, sorting data, filtering data, joining tables, grouping data, subquerying data, dan setting operators. Aside from learning basic SQL statements, you will also learn step by step how to develop stored procedures in MySQL. First, we introduce you to the stored procedure concept and discuss when you should use it. Then, we show you how to use the basic elements of the procedure code such as create procedure statement, if-else, case, loop, stored procedure’s parameters. In the sixth chapter, you will study: Creating the initial three table in the School database project: Teacher table, Class table, and Subject table; Creating database configuration files; Creating a Python GUI for viewing and navigating the contents of each table. Creating a Python GUI for inserting and editing tables; and Creating a Python GUI to merge and query the three tables. In last chapter, you will learn: Creating the main form to connect all forms; Creating a project that will add three more tables to the school database: the Student table, the Parent table, and the Tuition table; Creating a Python GUI to view and navigate the contents of each table; Creating a Python GUI for editing, inserting, and deleting records in each table; Create a Python GUI to merge and query the three tables and all six tables. Finally, this book is hopefully useful for you.


Practical Deep Learning

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Publisher : No Starch Press
Release Date :
ISBN 10 : 9781718500747
Pages : 464 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.0/5 (5 users download)


Download Practical Deep Learning by Ron Kneusel PDF/Ebook Free clicking on the below button will initiate the downloading process of Practical Deep Learning by Ron Kneusel. This book is available in ePub and PDF format with a single click unlimited downloads. Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance. You’ll also learn: • How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines • How neural networks work and how they’re trained • How to use convolutional neural networks • How to develop a successful deep learning model from scratch You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.


Machine Learning for Evolution Strategies

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Publisher : Springer
Release Date :
ISBN 10 : 9783319333830
Pages : 124 pages
File Format : PDF, EPUB, TEXT, KINDLE or MOBI
Rating : 4.3/5 (333 users download)


Download Machine Learning for Evolution Strategies by Oliver Kramer PDF/Ebook Free clicking on the below button will initiate the downloading process of Machine Learning for Evolution Strategies by Oliver Kramer. This book is available in ePub and PDF format with a single click unlimited downloads. This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction for search and visualization of high-dimensional optimization processes, and clustering-based niching. After giving an introduction to evolution strategies and machine learning, the book builds the bridge between both worlds with an algorithmic and experimental perspective. Experiments mostly employ a (1+1)-ES and are implemented in Python using the machine learning library scikit-learn. The examples are conducted on typical benchmark problems illustrating algorithmic concepts and their experimental behavior. The book closes with a discussion of related lines of research.


Python Programming for Beginners: A Comprehensive Crash Course With Practical Exercises to Quickly Learn Coding and Programming for Data Analysis and Machine Learning

Author :
Publisher : Anthony Adams
Release Date :
ISBN 10 :
Pages : 202 pages
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