Practical Business Analytics Using R and Python, Hodeghatta
Автор: Manohar Swamynathan Название: Mastering Machine Learning with Python in Six Steps ISBN: 1484249461 ISBN-13(EAN): 9781484249468 Издательство: Springer Рейтинг: Цена: 7317.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages.You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You’ll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data. Finally, you’ll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.What You'll LearnUnderstand machine learning development and frameworksAssess model diagnosis and tuning in machine learningExamine text mining, natuarl language processing (NLP), and recommender systemsReview reinforcement learning and CNNWho This Book Is ForPython developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.
Автор: Subasi, Abdulhamit Название: Practical Machine Learning For Data Analysis Using Python ISBN: 0128213795 ISBN-13(EAN): 9780128213797 Издательство: Elsevier Science Рейтинг: Цена: 16505.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems.
Описание: Classroom-tested by tens of thousands of students, this new edition of the bestselling intro to programming book is for anyone who wants to understand computer science. Learn about design, algorithms, testing, and debugging. Discover the fundamentals of programming with Python 3.6--a language that`s used in millions of devices.
Автор: Ilya Shpigor Название: Practical Video Game Bots ISBN: 1484237358 ISBN-13(EAN): 9781484237359 Издательство: Springer Рейтинг: Цена: 5487.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms.
For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than the fully connected ANN (FCNN). You will implement a CNN in Python to give you a full understanding of the model.
After consolidating the basics, you will use TensorFlow to build a practical image-recognition model that you will deploy to a web server using Flask, making it accessible over the Internet. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads.
This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production.
What You Will Learn
Understand how ANNs and CNNs work Create computer vision applications and CNNs from scratch using PythonFollow a deep learning project from conception to production using TensorFlowUse NumPy with Kivy to build cross-platform data science applications
Who This Book Is For
Data scientists, machine learning and deep learning engineers, software developers.
Описание: pacote do Courseware consiste em duas publicacoes, VeriSMTM - Foundation Courseware e VeriSM - Foundation Study Guide. Este material de treinamento abrange o plano de estudos para a qualificacao da Fundacao VeriSM . O treinamento pode ser entregue em dois dias. Este material didatico e credenciado para preparar o aluno para a certificacao da VeriSM Foundation. O VeriSM Foundation consiste em duas partes: VeriSM Essentials e VeriSM Plus, cada uma cobrindo um dia de treinamento.Os alunos que ja possuem um certificado de Gerenciamento de Servicos (TI) podem se beneficiar do conhecimento que ja possuem. Eles sao o publico-alvo de apenas um treinamento do VeriSM Plus. Ao serem aprovados no exame VeriSM Plus, recebem o certificado VeriSM Foundation.Provedores de treinamento que desejam oferecer um treinamento de um dia sobre principios de gerenciamento de servicos podem decidir oferecer apenas o treinamento VeriSM Essentials. Os alunos que forem aprovados no exame VeriSM Essentials receberao o certificado VeriSM Essentials. Se eles passarem no exame VeriSM Plus mais tarde, receberao automaticamente o certificado VeriSM Foundation.O "courseware" abrange os seguintes topicos:A organizacao do servico (Essentials)Cultura de servico (Essentials)Pessoas e estrutura organizacional (Essentials)O modelo VeriSM (ambos)Praticas Progressivas (Plus)Tecnologias Inovadoras (Plus)O VeriSM e uma abordagem holistica e orientada aos negocios para o Gerenciamento de Servicos, que ajuda a entender o panorama crescente das melhores praticas e como integra-las para oferecer valor ao consumidor.E uma evolucao no pensamento em Gerenciamento de Servicos e oferece uma abordagem atualizada, incluindo as mais recentes praticas e desenvolvimentos tecnologicos, para ajudar as organizacoes a transformar seus negocios para a nova realidade da era digital.O VeriSM e um gerenciamento orientado a valor, evolutivo, responsivo e integrado.VeriSM e uma marca registrada e propriedade da IFDC, a Fundacao Internacional de Competencias Digitais.
Автор: Singh, Himanshu Название: Practical machine learning and image processing ISBN: 1484241487 ISBN-13(EAN): 9781484241486 Издательство: Springer Рейтинг: Цена: 7317.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing.
The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools.
All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application.
What You Will Learn
Discover image-processing algorithms and their applications using PythonExplore image processing using the OpenCV libraryUse TensorFlow, scikit-learn, NumPy, and other librariesWork with machine learning and deep learning algorithms for image processingApply image-processing techniques to five real-time projects
Who This Book Is For
Data scientists and software developers interested in image processing and computer vision.
Описание: This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly.
Автор: Wintjen Marc Название: Practical Data Analysis using Jupyter Notebook ISBN: 1838826033 ISBN-13(EAN): 9781838826031 Издательство: Неизвестно Рейтинг: Цена: 7539.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book will take you on a journey through the evolution of data analysis explaining each step in the process in a very simple and easy to understand manner. You will learn how to use various Python libraries to work with data. Learn how to sift through the many different types of data, clean it, and analyze it to gain useful insights.
Discover easy-to-follow solutions and techniques to help you to implement applied mathematical concepts such as probability, calculus, and equations using Python's numeric and scientific libraries
Key Features
Compute complex mathematical problems using programming logic with the help of step-by-step recipes
Learn how to utilize Python's libraries for computation, mathematical modeling, and statistics
Discover simple yet effective techniques for solving mathematical equations and apply them in real-world statistics
Book Description
Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain.
The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.
By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
What you will learn
Get familiar with basic packages, tools, and libraries in Python for solving mathematical problems
Explore various techniques that will help you to solve computational mathematical problems
Understand the core concepts of applied mathematics and how you can apply them in computer science
Discover how to choose the most suitable package, tool, or technique to solve a certain problem
Implement basic mathematical plotting, change plot styles, and add labels to the plots using Matplotlib
Get to grips with probability theory with the Bayesian inference and Markov Chain Monte Carlo (MCMC) methods
Who this book is for
This book is for professional programmers and students looking to solve mathematical problems computationally using Python. Advanced mathematics knowledge is not a requirement, but a basic knowledge of mathematics will help you to get the most out of this book. The book assumes familiarity with Python concepts of data structures.
Автор: Smart Gary Название: Practical Python Programming for IoT ISBN: 1838982469 ISBN-13(EAN): 9781838982461 Издательство: Неизвестно Рейтинг: Цена: 9562.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Leverage Python and Raspberry Pi to create complex IoT applications capable of creating and detecting movement and measuring distance, light, and a host of other environmental conditions
Key features
Learn the fundamentals of electronics and how to integrate them with a Raspberry Pi
Understand how to build RESTful APIs, WebSocket APIs, and MQTT-based applications
Explore alternative approaches to structuring IoT applications with Python
Book Description
The age of connected devices is here, be it fitness bands or smart homes. It's now more important than ever to understand how hardware components interact with the internet to collect and analyze user data. The Internet of Things (IoT), combined with the popular open source language Python, can be used to build powerful and intelligent IoT systems with intuitive interfaces.
This book consists of three parts, with the first focusing on the "Internet" component of IoT. You'll get to grips with end-to-end IoT app development to control an LED over the internet, before learning how to build RESTful APIs, WebSocket APIs, and MQTT services in Python. The second part delves into the fundamentals behind electronics and GPIO interfacing. As you progress to the last part, you'll focus on the "Things" aspect of IoT, where you will learn how to connect and control a range of electronic sensors and actuators using Python. You'll also explore a variety of topics, such as motor control, ultrasonic sensors, and temperature measurement. Finally, you'll get up to speed with advanced IoT programming techniques in Python, integrate with IoT visualization and automation platforms, and build a comprehensive IoT project.
By the end of this book, you'll be well-versed with IoT development and have the knowledge you need to build sophisticated IoT systems using Python.
What you will learn
Understand electronic interfacing with Raspberry Pi from scratch
Gain knowledge of building sensor and actuator electronic circuits
Structure your code in Python using Async IO, pub/sub models, and more
Automate real-world IoT projects using sensor and actuator integration
Integrate electronics with ThingSpeak and IFTTT to enable automation
Build and use RESTful APIs, WebSockets, and MQTT with sensors and actuators
Set up a Raspberry Pi and Python development environment for IoT projects
Who this book is for
This IoT Python book is for application developers, IoT professionals, or anyone interested in building IoT applications using the Python programming language. It will also be particularly helpful for mid to senior-level software engineers who are experienced in desktop, web, and mobile development, but have little to no experience of electronics, physical computing, and IoT.
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