If your application source code is overly verbose, it can be a nightmare to maintain. Write concise and expressive, type-safe code in an environment that lets you build for the JVM, browser, and more. Key Features *Expert guidance that shows you to efficiently use both object-oriented and functional programming techniques *Understand functional programming libraries, such as Cats and Scalaz, and use them to augment your Scala development *Perfectly balances theory and hands-on exercises, assessments, and activities Book Description This book teaches you how to build and contribute to Scala programs, recognizing common patterns and techniques used with the language. You’ll learn how to write concise, functional code with Scala. After an introduction to core concepts, syntax, and writing example applications with scalac, you’ll learn about the Scala Collections API and how the language handles type safety via static types out-of-the-box. You’ll then learn about advanced functional programming patterns, and how you can write your own Domain Specific Languages (DSLs). By the end of the book, you’ll be equipped with the skills you need to successfully build smart, efficient applications in Scala that can be compiled to the JVM. What you will learn *Understand the key language syntax and core concepts for application development *Master the type system to create scalable type-safe applications while cutting down your time spent debugging *Understand how you can work with advanced data structures via built-in features such as the Collections library *Use classes, objects, and traits to transform a trivial chatbot program into a useful assistant *Understand what are pure functions, immutability, and higher-order functions *Recognize and implement popular functional programming design patterns Who this book is for This is an ideal book for developers who are looking to learn Scala, and is particularly well suited for Java developers looking to migrate across to Scala for application development on the JVM.

Build smart applications by implementing real-world artificial intelligence projects Key Features *Explore a variety of AI projects with Python *Get well-versed with different types of neural networks and popular deep learning algorithms *Leverage popular Python deep learning libraries for your AI projects Book Description Artificial Intelligence (AI) is the newest technology that’s being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence. This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library. By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progress What you will learn *Build a prediction model using decision trees and random forest *Use neural networks, decision trees, and random forests for classification *Detect YouTube comment spam with a bag-of-words and random forests *Identify handwritten mathematical symbols with convolutional neural networks *Revise the bird species identifier to use images *Learn to detect positive and negative sentiment in user reviews Who this book is for Python Artificial Intelligence Projects for Beginners is for Python developers who want to take their first step into the world of Artificial Intelligence using easy-to-follow projects. Basic working knowledge of Python programming is expected so that you’re able to play around with code

Build next-generation Artificial Intelligence systems with Java About This Book ? Implement AI techniques to build smart applications using Deeplearning4j ? Perform big data analytics to derive quality insights using Spark MLlib ? Create self-learning systems using neural networks, NLP, and reinforcement learning Who This Book Is For This book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus. What You Will Learn ? Manage Artificial Intelligence techniques for big data with Java ? Build smart systems to analyze data for enhanced customer experience ? Learn to use Artificial Intelligence frameworks for big data ? Understand complex problems with algorithms and Neuro-Fuzzy systems ? Design stratagems to leverage data using Machine Learning process ? Apply Deep Learning techniques to prepare data for modeling ? Construct models that learn from data using open source tools ? Analyze big data problems using scalable Machine Learning algorithms In Detail In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems. Style and approach An easy-to-follow, step-by-step guide to help you get to grips with real-world applications of Artificial Intelligence for big data using Java

Explore the features included in the latest versions of MQTT for IoT and M2M communications and use them with modern Python 3. About This Book ? Make your connected devices less prone to attackers by understanding security mechanisms ? Take advantage of MQTT features for IoT and Machine-to-Machine communications ? The only book that covers MQTT with a single language, Python Who This Book Is For This book is for developers who want to learn about the MQTT protocol for their IoT projects. Prior knowledge of working with IoT and Python will be helpful. What You Will Learn ? Learn how MQTT and its lightweight messaging system work ? Understand the MQTT puzzle: clients, servers (formerly known as brokers), and connections ? Explore the features included in the latest versions of MQTT for IoT and M2M communications ? Publish and receive MQTT messages with Python ? Learn the difference between blocking and threaded network loops ? Take advantage of the last will and testament feature ? Work with cloud-based MQTT interfaces in Python In Detail MQTT is a lightweight messaging protocol for small sensors and mobile devices. This book explores the features of the latest versions of MQTT for IoT and M2M communications, how to use them with Python 3, and allow you to interact with sensors and actuators using Python. The book begins with the specific vocabulary of MQTT and its working modes, followed by installing a Mosquitto MQTT broker. You will use different utilities and diagrams to understand the most important concepts related to MQTT. You will learn to?make all the necessary configuration to work with digital certificates for encrypting all data sent between the MQTT clients and the server. You will also work with the different Quality of Service levels and later analyze and compare their overheads. You will write Python 3.x code to control a vehicle with MQTT messages delivered through encrypted connections (TLS 1.2), and learn how leverage your knowledge of the MQTT protocol to build a solution based on requirements. Towards the end, you will write Python code to use the PubNub cloud-based real-time MQTT provider to monitor a surfing competition. In the end, you will have a solution that was built from scratch by analyzing the requirements and then write Python code that will run on water-proof IoT boards connected to multiple sensors in surfboards. Style and approach This book shows you what MQTT is, and how to install and secure an MQTT server. You will write Python 3 code to control a vehicle with MQTT messages, test and improve, then monitor a surfing competition with cloud-based real-time MQTT providers.

Connect things to create amazing IoT applications in minutes About This Book ? Use Blynk cloud and Blynk server to connect devices ? Build IoT applications on Android and iOS platforms ? A practical guide that will show how to connect devices using Blynk and Raspberry Pi 3 Who This Book Is For This book is targeted at any stakeholder working in the IoT sector who wants to understand how Blynk works and build exciting IoT projects. Prior understanding of Raspberry Pi, C/C++, and electronics is a must. What You Will Learn ? Build devices using Raspberry Pi and various sensors and actuators ? Use Blynk cloud to connect and control devices through the Blynk app ? Connect devices to Blynk cloud and server through Ethernet and Wi-Fi ? Make applications using Blynk apps (App Builder) on Android and iOS platforms ? Run Blynk personal server on the Windows, MAC, and Raspberry Pi platforms In Detail Blynk, known as the most user-friendly IoT platform, provides a way to build mobile applications in minutes. With the Blynk drag-n-drop mobile app builder, anyone can build amazing IoT applications with minimal resources and effort, on hardware ranging from prototyping platforms such as Arduino and Raspberry Pi 3 to industrial-grade ESP8266, Intel, Sierra Wireless, Particle, Texas Instruments, and a few others. This book uses Raspberry Pi as the main hardware platform and C/C++ to write sketches to build projects. The first part of this book shows how to set up a development environment with various hardware combinations and required software. Then you will build your first IoT application with Blynk using various hardware combinations and connectivity types such as Ethernet and Wi-Fi. Then you'll use and configure various widgets (control, display, notification, interface, time input, and some advanced widgets) with Blynk App Builder to build applications. Towards the end, you will learn how to connect with and use built-in sensors on Android and iOS mobile devices. Finally you will learn how to build a robot that can be controlled with a Blynk app through the Blynk cloud and personal server. By the end of this book, you will have hands-on experience building IoT applications using Blynk. Style and approach A step-by-step guide that will help you build simple yet exciting project in no time.

A definitive guide to creating an intelligent web application with the best of machine learning and JavaScript About This Book ? Solve complex computational problems in browser with JavaScript ? Teach your browser how to learn from rules using the power of machine learning ? Understand discoveries on web interface and API in machine learning Who This Book Is For This book is for you if you are a JavaScript developer who wants to implement machine learning to make applications smarter, gain insightful information from the data, and enter the field of machine learning without switching to another language. Working knowledge of JavaScript language is expected to get the most out of the book. What You Will Learn ? Get an overview of state-of-the-art machine learning ? Understand the pre-processing of data handling, cleaning, and preparation ? Learn Mining and Pattern Extraction with JavaScript ? Build your own model for classification, clustering, and prediction ? Identify the most appropriate model for each type of problem ? Apply machine learning techniques to real-world applications ? Learn how JavaScript can be a powerful language for machine learning In Detail In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added advantage of machine learning research and support for JS libraries, JavaScript makes your browsers smarter than ever with the ability to learn patterns and reproduce them to become a part of innovative products and applications. Hands-on Machine Learning with JavaScript presents various avenues of machine learning in a practical and objective way, and helps implement them using the JavaScript language. Predicting behaviors, analyzing feelings, grouping data, and building neural models are some of the skills you will build from this book. You will learn how to train your machine learning models and work with different kinds of data. During this journey, you will come across use cases such as face detection, spam filtering, recommendation systems, character recognition, and more. Moreover, you will learn how to work with deep neural networks and guide your applications to gain insights from data. By the end of this book, you'll have gained hands-on knowledge on evaluating and implementing the right model, along with choosing from different JS libraries, such as NaturalNode, brain, harthur, classifier, and many more to design smarter applications. Style and approach This is a practical tutorial that uses hands-on examples to step through some real-world applications of machine learning. Without shying away from the technical details, you will explore machine learning with JavaScript using clear and practical examples.

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