||Machine Learning with TensorFlow|
TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine.
Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you'll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own. ...
||Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learning|
Prepare for Microsoft Exam 70-774 - and help demonstrate your real-world mastery of performing key data science activities with Azure Machine Learning services. Designed for experienced IT professionals ready to advance their status, Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the MCSA level.
Focus on the expertise measured by these objectives: Prepare data for analysis in Azure Machine Learning and export from Azure Machine Learning; Develop machine learning models; Operationalize and manage Azure Machine Learning Services; Use other services for machine learning. ...
||Building Intelligent Systems|
Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and achieve success.
This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data science, machine learning, management, and program management to produce working systems.
Building Intelligent Systems is based on more than a decade of experience building Internet-scale Intelligent Systems that have hundreds of millions of user interactions per day in some of the largest and most important software systems in the world.
Understand the concept of an Intelligent System: What it is good for, when you need one, and how to set it up for success;
Design an intelligent user experience: Produce data to help make the Intelligent System better over time;
Implement an Intelligent System: Execute, manage, and measure In ...
||Beginning Xamarin Development for the Mac|
Develop apps for the iPhone, iPad, and Apple wearables using Visual Studio for the Mac.
Learn how to set up your development environment and emulators, and how to create adaptive user interfaces for various platforms. Expert Dawid Borycki guides you through the fundamentals of programming for Apple platforms (Model View Controller, Test Driven Development), navigation patterns, gesture handling, accessing user's location, and reading and consuming data from web services.
After reading this book, you will be able to build native apps that look and feel like other apps built into iOS, watchOS, and tvOS, and have the skills that are in high demand in today's market. If you are already programming C# apps for web or desktop, you will learn how to extend your skill set to Apple mobile, wearable, and smart TV platforms.
Build and implement native apps for Apple platforms; Create adaptive, universal views and handle navigation between them; Access user's location and handle touch in ...
||Machine Learning and Security|
Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you'll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis.
Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike.
Learn how machine learning has contributed to the success of modern spam filters; Quickly detect anomalies, including breaches, fraud, and impending system failure; Conduct malware analysis by extracting useful information from computer binaries; Uncover at ...
||Applied Text Analysis with Python|
The programming landscape of natural language processing has changed dramatically in the past few years. Machine learning approaches now require mature tools like Python's scikit-learn to apply models to text at scale. This practical guide shows programmers and data scientists who have an intermediate-level understanding of Python and a basic understanding of machine learning and natural language processing how to become more proficient in these two exciting areas of data science.
This book presents a concise, focused, and applied approach to text analysis with Python, and covers topics including text ingestion and wrangling, basic machine learning on text, classification for text analysis, entity resolution, and text visualization. Applied Text Analysis with Python will enable you to design and develop language-aware data products.
You'll learn how and why machine learning algorithms make decisions about language to analyze text; how to ingest, wrangle, and preprocess language d ...
||Hands-On Machine Learning with Scikit-Learn and TensorFlow|
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks - scikit-learn and TensorFlow - author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
Explore the machine learning landscape, particularly neural nets; Use scikit-learn to track an example machine-learning project end-to-end; Explore several training models, including support vector machines, deci ...
||Mastering Machine Learning with Python in Six Steps|
Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner.
This book's approach is based on the "Six degrees of separation" theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages.
You'll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining / analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered. Finally, you'll explore advanced text mining techniques, neural networks and deep learning techniques, and their implementation.
All the code presented in the book will be available in the form of iPython ...
||Oracle Business Intelligence with Machine Learning|
Use machine learning and Oracle Business Intelligence Enterprise Edition (OBIEE) as a comprehensive BI solution. This book follows a when-to, why-to, and how-to approach to explain the key steps involved in utilizing the artificial intelligence components now available for a successful OBIEE implementation. Oracle Business Intelligence with Machine Learning covers various technologies including using Oracle OBIEE, R Enterprise, Spatial Maps, and machine learning for advanced visualization and analytics.
The machine learning material focuses on learning representations of input data suitable for a given prediction problem. This book focuses on the practical aspects of implementing machine learning solutions using the rich Oracle BI ecosystem. The primary objective of this book is to bridge the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to machine learning with OBIEE. ...
||Practical Machine Learning with Python|
Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully.
Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code.
Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. ...
||Introduction to Machine Learning with Python|
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.
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 fo ...