||Machine Learning Solutions|
Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job.
You'll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you'll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples.
The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, ...
||Hands-On Automated Machine Learning|
AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners' work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible.
In this book, you'll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning.
By the end of this book, you will have a clearer understanding of the different aspects of automated Machine Learning, and you'll be able to incorporate automation tasks using practical datasets. You can leverage your lea ...
||Feature Engineering for Machine Learning|
Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you'll learn techniques for extracting and transforming features - the numeric representations of raw data - into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.
Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples.
Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms; Natural text techniques: bag-of- ...
||Deep Belief Nets in C++ and CUDA C: Volume 1|
Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards.
The first of three in a series on C++ and CUDA C deep learning and belief nets, Deep Belief Nets in C++ and CUDA C: Volume 1 shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a thought process that is capable of learning abstract concepts built from simpler primitives. As such, you'll see that a typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting.
All the routines and algorithms presented in the book are available in the c ...
||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 ...
||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 ...
||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 ...
||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 ...
||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. ...
||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. ...
||Objective-C for Absolute Beginners, 4th Edition|
Learn Objective-C and its latest release, and learn how to mix Swift with it. You have a great idea for an app, but how do you bring it to fruition? With Objective-C, the universal language of iPhone, iPad, and Mac apps.
Using a hands-on approach, you'll learn how to think in programming terms, how to use Objective-C to construct program logic, and how to synthesize it all into working apps. Gary Bennett, an experienced app developer and trainer, will guide you on your journey to becoming a successful app developer. Along the way you'll discover the flexibility of Apple's developer tools
If you're looking to take the first step towards App Store success, Objective-C for Absolute Beginners, Fourth Edition is the place to start.
Understand the fundamentals of computer programming: variables, design data structures, and working with file systems; Examine the logic of object-oriented programming: how to use classes, objects, and methods; Install Xcode and write programs in ...