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Build AI applications using Python to intelligently interact with the world around you. Key Features - Covers the practical aspects of Machine Learning and Deep Learning concepts with the help of this example-rich guide to Python. - Includes graphical illustrations of Natural Language Processing and its implementation in NLTK. - Covers deep learning models such as R-CNN and YOLO for object recognition and teaches how to build an image classifier using CNN. Description The book ‘Learn AI with Python’ is intended to provide you with a thorough understanding of artificial intelligence as well as the tools necessary to create your intelligent applications. This book introduces you to artificial intelligence and walks you through the process of establishing an AI environment on a variety of platforms. It dives into machine learning models and various predictive modeling techniques, including classification, regression, and clustering. Additionally, it provides hands-on experience with logic programming, ASR, neural networks, and natural language processing through real-world examples and fully functional Python implementation. Finally, the book deals with profound models of learning such as R-CNN and YOLO. Object detection in images is also explained in detail using Convolutional Neural Networks (CNNs), which are also explained. By the end of this book, you will have a firm grasp of machine learning and deep learning techniques, as well as a steered methodology for formulating and solving related problems. What you will learn - Learn to implement various machine learning and deep learning algorithms to achieve smart results. - Understand how ML algorithms can be applied to real-life applications. - Explore logic programming and learn how to use it practically to solve real-life problems. - Learn to develop different types of artificial neural networks with Python. - Understand reinforcement learning and how to build an environment and agents using Python. - Work with NLTK and build an automatic speech recognition system. Who this book is for This book is for anyone interested in learning about artificial intelligence and putting it into practice with Python. This book is also valuable for intermediate Machine Learning practitioners as a reference guide. Readers should be familiar with the fundamental understanding of Python programming and machine learning techniques. Table of Contents 1. Introduction to AI and Python 2. Machine Learning and Its Algorithms 3. Classification and Regression Using Supervised Learning 4. Clustering Using Unsupervised Learning 5. Solving Problems with Logic Programming 6. Natural Language Processing with Python 7. Implementing Speech Recognition with Python 8. Implementing Artificial Neural Network (ANN) with Python 9. Implementing Reinforcement Learning with Python 10. Implementing Deep Learning and Convolutional Neural Network
Create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learn Key Features Develop a range of healthcare analytics projects using real-world datasets Implement key machine learning algorithms using a range of libraries from the Python ecosystem Accomplish intermediate-to-complex tasks by building smart AI applications using neural network methodologies Book Description Machine Learning (ML) has changed the way organizations and individuals use data to improve the efficiency of a system. ML algorithms allow strategists to deal with a variety of structured, unstructured, and semi-structured data. Machine Learning for Healthcare Analytics Projects is packed with new approaches and methodologies for creating powerful solutions for healthcare analytics. This book will teach you how to implement key machine learning algorithms and walk you through their use cases by employing a range of libraries from the Python ecosystem. You will build five end-to-end projects to evaluate the efficiency of Artificial Intelligence (AI) applications for carrying out simple-to-complex healthcare analytics tasks. With each project, you will gain new insights, which will then help you handle healthcare data efficiently. As you make your way through the book, you will use ML to detect cancer in a set of patients using support vector machines (SVMs) and k-Nearest neighbors (KNN) models. In the final chapters, you will create a deep neural network in Keras to predict the onset of diabetes in a huge dataset of patients. You will also learn how to predict heart diseases using neural networks. By the end of this book, you will have learned how to address long-standing challenges, provide specialized solutions for how to deal with them, and carry out a range of cognitive tasks in the healthcare domain. What you will learn Explore super imaging and natural language processing (NLP) to classify DNA sequencing Detect cancer based on the cell information provided to the SVM Apply supervised learning techniques to diagnose autism spectrum disorder (ASD) Implement a deep learning grid and deep neural networks for detecting diabetes Analyze data from blood pressure, heart rate, and cholesterol level tests using neural networks Use ML algorithms to detect autistic disorders Who this book is for Machine Learning for Healthcare Analytics Projects is for data scientists, machine learning engineers, and healthcare professionals who want to implement machine learning algorithms to build smart AI applications. Basic knowledge of Python or any programming language is expected to get the most from this book.
Use artificial intelligence and machine learning on AWS to create engaging applications Key Features Explore popular AI and ML services with their underlying algorithms Use the AWS environment to manage your AI workflow Reinforce key concepts with hands-on exercises using real-world datasets Book Description Machine Learning with AWS is the right place to start if you are a beginner interested in learning useful artificial intelligence (AI) and machine learning skills using Amazon Web Services (AWS), the most popular and powerful cloud platform. You will learn how to use AWS to transform your projects into apps that work at high speed and are highly scalable. From natural language processing (NLP) applications, such as language translation and understanding news articles and other text sources, to creating chatbots with both voice and text interfaces, you will learn all that there is to know about using AWS to your advantage. You will also understand how to process huge numbers of images fast and create machine learning models. By the end of this book, you will have developed the skills you need to efficiently use AWS in your machine learning and artificial intelligence projects. What you will learn Get up and running with machine learning on the AWS platform Analyze unstructured text using AI and Amazon Comprehend Create a chatbot and interact with it using speech and text input Retrieve external data via your chatbot Develop a natural language interface Apply AI to images and videos with Amazon Rekognition Who this book is for Machine Learning with AWS is ideal for data scientists, programmers, and machine learning enthusiasts who want to learn about the artificial intelligence and machine learning capabilities of Amazon Web Services.
Learn to build end-to-end AI apps from scratch for Android and iOS using TensorFlow Lite, CoreML, and PyTorch Key Features Build practical, real-world AI projects on Android and iOS Implement tasks such as recognizing handwritten digits, sentiment analysis, and more Explore the core functions of machine learning, deep learning, and mobile vision Book Description We're witnessing a revolution in Artificial Intelligence, thanks to breakthroughs in deep learning. Mobile Artificial Intelligence Projects empowers you to take part in this revolution by applying Artificial Intelligence (AI) techniques to design applications for natural language processing (NLP), robotics, and computer vision. This book teaches you to harness the power of AI in mobile applications along with learning the core functions of NLP, neural networks, deep learning, and mobile vision. It features a range of projects, covering tasks such as real-estate price prediction, recognizing hand-written digits, predicting car damage, and sentiment analysis. You will learn to utilize NLP and machine learning algorithms to make applications more predictive, proactive, and capable of making autonomous decisions with less human input. In the concluding chapters, you will work with popular libraries, such as TensorFlow Lite, CoreML, and PyTorch across Android and iOS platforms. By the end of this book, you will have developed exciting and more intuitive mobile applications that deliver a customized and more personalized experience to users. What you will learn Explore the concepts and fundamentals of AI, deep learning, and neural networks Implement use cases for machine vision and natural language processing Build an ML model to predict car damage using TensorFlow Deploy TensorFlow on mobile to convert speech to text Implement GAN to recognize hand-written digits Develop end-to-end mobile applications that use AI principles Work with popular libraries, such as TensorFlow Lite, CoreML, and PyTorch Who this book is for Mobile Artificial Intelligence Projects is for machine learning professionals, deep learning engineers, AI engineers, and software engineers who want to integrate AI technology into mobile-based platforms and applications. Sound knowledge of machine learning and experience with any programming language is all you need to get started with this book.
Your guide to becoming a marketing guru and supercharge your brand with modern technologies. Key Features - Industry-led best practices and real-world examples of brand successes and failures. - Leading AI tools, guidelines, and templates for Marketing, Sales, and Customer Success. - Advanced forms of marketing such as Consumer Neuroscience, Subliminal Marketing, and Virtual Advertising. Description How can some businesses survive centuries while hundreds collapse every year - from micro-enterprises to global brands? A brand's journey to grow and maintain loyal supporters is one of the timeless foundations for every company that its customer teams need to know. 'Modern Marketing Using AI' covers it all by taking a customer perspective to look at best practices, industry-leading strategies, technologies, and their timing to maximise the value of a brand. The book starts with an overview of a brand journey, how marketing, sales, and customer success work at each stage, and why the usage of AI in this field has become a need. It then dives into each stage, teaching us how to validate a product, define the brand, expand its visibility, and turn customers into fans with AI-led marketing techniques for channels, accounts, referrals, affiliates, influencers, social media and much more. At every stage, it highlights brand stories and how operational automation and insights can be leveraged for marketing. We conclude with guidelines on how marketers can prepare for AI and even estimate its impact on their jobs. You will walk away with a keen awareness of how to drive your organizational growth and gain a professional advantage by being ready for the era of artificial intelligence. What you will learn - Learn how Marketing, Sales, and Customer Success work together to define a brand and grow its customer base. - Learn how to create brand recognition, credibility, engagement, receptivity, and resonance to optimize brand equity. - Create a robust marketing plan and perfectly time the different marketing initiatives, from digital channels to neuroscience. - Learn when, how, and what AI-enabled tools to deploy in marketing, sales, and customer success operations. Who this book is for This book is aimed at entrepreneurs and marketing professionals, as well as educators and students who want to learn how to create a successful brand leveraging modern technologies. Readers are only required to have a rudimentary understanding of marketing, sales, and customer success. Table of Contents Section 1: Understanding the Brand Journey 1. The Importance of Brand Equity 2. A Typical Brand Journey 3. The Convergence of Marketing, Sales and Customer Success 4. AI and the Future of Marketing Section 2: Attracting Customers 5. Validating Your Brand and Product 6. Brand Identity 7. Brand Awareness – Web Presence 8. Brand Awareness – Social Media 9. Brand Awareness – Online Ads Section 3: Winning the Customers 10. Operational Alignment and Automation 11. Brand Credibility 12. Brand Engagement 13. Selling Strategies Section 4: Turning the Customers into Fans 14. Customer Onboarding 15. Brand Receptivity 16. Brand Resonance 17. How to Prepare for the AI Era
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
Artificial Intelligence (AI) is here to stay. No longer confined to the world of science fiction, AI has infiltrated the mainstream and is the new electricity for business. Bestselling author, Bernard Marr, shows you how to harness and integrate it with your business strategy. We all know about driverless cars, automated production lines and chatbots but how do you ensure your business keeps up and where do you start? Bestselling author and strategy guru, Bernard Marr, argues that AI absolutely applies to your business and explains how to design an AI strategy that will guarantee its success. The Intelligence Revolution explores the opportunities and challenges that come with this monumental new taskforce that is defining the new standards of business.Guiding us through intelligent products, services and work processes, The Intelligence Revolution illustrates how new technologies are impacting customer experience, product and service design and work efficiency. Bernard Marr delights us with fascinating case studies of businesses excelling at maximizing the potential of AI like Netflix, Autodesk, Disney, Rolls Royce and Amazon. Don't be left behind. Instead, discover how to turbocharge your business.
Everywhere you look, there are signs of the Fourth Industrial Revolution. R&D on leading digital technologies is conducted around the world, exploring novel technologies aimed at cyber-physical systems, such as the Internet of Things (IoT), Blockchain, 3D Printing, Virtual Reality, AI, and many more. With these rapid changes in technology comes social evolution and the potential for future social crises. Understanding Industry 4.0: AI, the Internet of Things, and the Future of Work looks to determine the most probable oncoming changes in key areas of the economy, to highlight the most important professions of the future, and to offer recommendations for their correct selection and successful mastering. Including sections on careers in education, medicine, R&D, and agriculture, among others, economics experts Bruno S. Sergi, Elena G. Popkova, Aleksei V. Bogoviz, and Tatiana N. Litvinova explore the vastly changing modern workplace and offer a guide to navigating through and adapting to this evolution. For researchers and students of management, economics, and business, this is an unmissable exploration of the new frontier of Industry 4.0.
Explore various recipes to build games using popular artificial intelligence techniques and algorithms such as Navmesh navigation A*, DFS, and UCB1 Key Features Explore different algorithms for creating decision-making agents that go beyond simple behaviors and movement Discover the latest features of the NavMesh API for scripting intelligent behaviour in your game characters Create games that are non-predictable and dynamic and have a high replayability factor Book Description Interactive and engaging games come with intelligent enemies, and this intellectual behavior is combined with a variety of techniques collectively referred to as Artificial Intelligence. Exploring Unity's API, or its built-in features, allows limitless possibilities when it comes to creating your game's worlds and characters. This cookbook covers both essential and niche techniques to help you take your AI programming to the next level. To start with, you'll quickly run through the essential building blocks of working with an agent, programming movement, and navigation in a game environment, followed by improving your agent's decision-making and coordination mechanisms – all through hands-on examples using easily customizable techniques. You'll then discover how to emulate the vision and hearing capabilities of your agent for natural and humanlike AI behavior, and later improve the agents with the help of graphs. This book also covers the new navigational mesh with improved AI and pathfinding tools introduced in the Unity 2018 update. You'll empower your AI with decision-making functions by programming simple board games, such as tic-tac-toe and checkers, and orchestrate agent coordination to get your AIs working together as one. By the end of this book, you'll have gained expertise in AI programming and developed creative and interactive games. What you will learn Create intelligent pathfinding agents with popular AI techniques such as A* and A*mbush Implement different algorithms for adding coordination between agents and tactical algorithms for different purposes Simulate senses so agents can make better decisions, taking account of the environment Explore different algorithms for creating decision-making agents that go beyond simple behaviors and movement Create coordination between agents and orchestrate tactics when dealing with a graph or terrain Implement waypoints by making a manual selector Who this book is for The Unity 2018 Artificial Intelligence Cookbook is for you if you are eager to get more tools under your belt to solve AI- and gameplay-related problems. Basic knowledge of Unity and prior knowledge of C# is an advantage.
Learn and Implement game AI in Unity 2018 to build smart game environments and enemies with A*, Finite State Machines, Behavior Trees and NavMesh. Key Features Build richer games by learning the essential concepts in AI for games like Behavior Trees and Navigation Meshes Implement character behaviors and simulations using the Unity Machine Learning toolkit Explore the latest Unity 2018 features to make implementation of AI in your game easier Book Description Developing Artificial Intelligence (AI) for game characters in Unity 2018 has never been easier. Unity provides game and app developers with a variety of tools to implement AI, from the basic techniques to cutting-edge machine learning-powered agents. Leveraging these tools via Unity's API or built-in features allows limitless possibilities when it comes to creating your game's worlds and characters. This fourth edition with Unity will help you break down AI into simple concepts to give you a fundamental understanding of the topic to build upon. Using a variety of examples, the book then takes those concepts and walks you through actual implementations designed to highlight key concepts and features related to game AI in Unity. Further on, you'll learn how to distinguish the state machine pattern and implement one of your own. This is followed by learning how to implement a basic sensory system for your AI agent and coupling it with a Finite State Machine (FSM). Next, you'll learn how to use Unity's built-in NavMesh feature and implement your own A* pathfinding system. You'll then learn how to implement simple ?ocks and crowd dynamics, which are key AI concepts in Unity. Moving on, you'll learn how to implement a behavior tree through a game-focused example. Lastly, you'll apply all the concepts in the book to build a popular game. What you will learn Create smarter game worlds and characters with C# programming Apply automated character movement using pathfinding and steering behaviors Implement non-player character decision-making algorithms using Behavior Trees and FSMs Build believable and highly efficient artificial flocks and crowds Create sensory systems for your AI with the most commonly used techniques Construct decision-making systems to make agents take different actions Explore the application of machine learning in Unity Who this book is for This book is intended for Unity developers with a basic understanding of C# and the Unity editor. Whether you're looking to build your first game or are looking to expand your knowledge as a game programmer, you will find plenty of exciting information and examples of game AI in terms of concepts and implementation.