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Get hands-on training on any web crawling/scraping tool and uses of web scraping in the real-time industry Key Features -Includes numerous use-cases on the use of web scraping for industrial applications. -Learn how to automate web scraping tasks. -Explore ready-made syntaxes of Python scripts to run web scraping. Description A Python Guide for Web Scraping is a book that will give information about the importance of web scraping using Python. It includes real-time examples of web scraping. It implies the automation use cases of web scraping as well. It gives information about the different tools and libraries of web scraping so that readers get a wide idea about the features and existence of web scraping. In this book, we started with the basics of Python and its syntactical information. We briefed about the use cases and features of Python. We have explained the importance of Python in automation systems. Furthermore, we have added information about real-time industrial examples. We have concentrated and deep-dived into Python’s importance in web scraping, explained the different tools and their usages. We have explained the real-time industrial domain-wise use cases for web scraping. What you will learn -Explore the Python syntax and key features of using Python for web scraping. -Usage of Python in the web scraping tasks and how to automate scraping. -How to use different libraries and modules of Python. Who this book is for This book is basically for data engineers and data programmers who have a basic knowledge of Python and for the readers who want to learn about web scraping projects for industries. Table of Contents 1. Python Basics 2. Use Cases of Python 3. Automation Using Python 4. Industrial Automation-Python 5. Web Scraping 6. Web Scraping and Necessity 7. Python - Web Scraping and Different Tools 8. Automation in Web Scraping 9. Use Cases-Web Scraping 10. Industrial Benefits of Web Scraping About the Authors Mr Pradumna Panditrao is currently working as a Senior Software Engineer and a DevOps tool developer. He has done his Masters in networking and telecommunications. He has a total of 8+ years of experience in various domains like Software Development, DevOps Automation tools, Data mining Crawling tools, Cloud Technologies, and Hardware Profiling. He has good exposure to the cloud and has published a paper on Cognitive Radio, 4G Technology Algorithms. He has given embedded software development lectures and lab demo sessions at Bits Pilani, Goa in 2014-2015.

Learn modern-day technologies from modern-day technical giants Key Features Real-world success and failure stories of artificial intelligence explained Understand concepts of artificial intelligence and deep learning methods Learn how to use artificial intelligence and deep learning methods Know how to prepare dataset and implement models using industry leading Python packages You’ll be able to apply and analyze the results produced by the models for prediction Description The aim of this book is to help the readers understand the concept of artificial intelligence and deep learning methods and implement them into their businesses and organizations. The first two chapters describe the introduction of the artificial intelligence and deep learning methods. In the first chapter, the concept of human thinking process, starting from the biochemical responses within the structure of neurons to the problem-solving steps through computational thinking skills are discussed. All chapters after the first two should be considered as the study of different technological and Artificial Intelligence giants of current age. These chapters are placed in a way that each chapter could be considered a separate study of a separate company, which includes the achievements of intelligent services currently provided by the company, discussion on the business model of the company towards the use of the deep learning technologies, the advancement of the web services which are incorporated with intelligent capability introduced by company, the efforts of the company in contributing to the development of the artificial intelligence and deep learning research. What You Will Learn How to use the algorithms written in the Python programming language to design models and perform predictions in general datasets Understand use cases in different industries related to the implementation of artificial intelligence and deep learning methods Learn the use of potential ideas in artificial intelligence and deep learning methods to improve the operational processes or new products and how services can be produced based on the methods Who this book is for This book is targeted to business and organization leaders, technology enthusiasts, professionals, and managers who seek knowledge of artificial intelligence and deep learning methods. Table of Contents 1. Artificial Intelligence and Deep Learning 2. Data Science for Business Analysis 3. Decision Making 4. Intelligent Computing Strategies By Google 5. Cognitive Learning Services in IBM Watson 6. Advancement web services by Baidu 7. Improved Social Business by Facebook 8. Personalized Intelligent Computing by Apple 9.Cloud Computing Intelligent by Microsoft About the Authors Dr. Jagreet Kaur is a doctorate in computer science and engineering. Her topic of thesis was “ARTIFICIAL INTELLIGENCE BASED ANALYTICAL PLATFORM FOR PREDICTIVE ANALYSIS IN HEALTH CARE.” With more than 12 years of experience in academics and research, she is working in data wrangling, machine learning and deep learning algorithms on large datasets, real-time data often in production environments for data science solutions and data products to get actionable insights for the last four years. Navdeep Singh Gill is a technology and solution architect having more than 15 years of experience in the IT and Telecom industry. For the past six years, he is working in big data analytics, automation and advanced analytics using machine learning and deep learning for planning and architecting of data science solutions and data products.

Artificial Intelligence may be the disruptive tech to influence our lives, but in the end, it has its own species to grow, so let us not take it as something we use and leave. Key Features The book gives a lucid introduction to the idea of AI Ethics and its geopolitical implications. The book is insightful for an academic understanding of AI Ethics in the concept of Legal Personality meant for every person, including professionals in the field of Law, Social Sciences and Technology Studies. The book provides a special understanding and renders curiosity for readers to establish newer ideas and understand Artificial Intelligence from a socio-cultural scenario. The book gives a cogent aspect of the relationship between Artificial Intelligence and International Law. The book presents about an innovative and dimensional idea of Privacy with respect to AI in Legal Theory. Description The book enters with its first chapter providing a simple and legal backdrop of the idea behind AI Ethics and International Law, its references and some important analogies and conceptual ideas. Also, the first chapter introduces some problems and questions regarding AI for contemplation in the field of jurisprudence. The second chapter vividly focuses on the deeper aspect of Artificial Intelligence, and goes to the principled developments of pure international law, with special analysis of the conceptions of sovereignty, self-determination and human rights. The chapter explores the catchy world of design and technology and covers with the diversity of issues revolving Artificial Intelligence Ethics. The third chapter gets specific with International Law and paves on ways towards the idea of the Privacy Doctrine conceived by the author. The chapter also explores the conceptual propositions in the field of Artificial Intelligence and International Law and renders about the scope of culture as a part of the social ecosystem to affect artificial intelligence. The chapter also lays the origination of the idea of an AI as an Entity, with special examples. The fourth chapter is centric towards human rights, making the debate beyond the legal literature and pragmatizing about the corporate idea of innovation and customer experience in various tech companies and institutions. The final chapter digs deeper into the principles and realms of cosmopolitanism and globalization, giving ways to discover and embark upon the role of human empathy and understanding to solve the issues that disruptive technology renders in its canvas. What You Will Learn The reader will learn about artificial intelligence in the eyes of a social animal, beyond the technical aspect of it. It enables the reader to challenge the conventional understanding of artificial intelligence and gives a motivation to understand the deep connect that AI is capable to create with humans in its social, economic and cultural scenarios rendered. It also poses a sense of curiosity and humility for people to understand the legal and social role of disrupting tech whether they are in a developed country or a developing one. Who This Book is For This book is based for students, academicians, educationists, professionals and policy researchers in the field of law, social sciences, management and technology to understand and get a special insight of artificial intelligence for mankind. It is also a good read for a layman audience to get into the idea of artificial intelligence ethics for their understanding and awareness. Table of Contents 1. Introduction to artificial intelligence and international law 2. The Basic Relationship: The Pragmatism 3. Legal visibility: DOCTRINE and Concept for AI 4. Beyond the Human Rights Discourse: A New Vision 5. Student Devices About the Author Abhivardhan is an Intrapreneur at Alexis Group, Founder of Internationalism, a legal research think tank, a Founding Member and the Secretary-General of Indian Society of Artificial Intelligence and Law, a Eurasian Editor at the Institute for a Greater Europe and a member of the MIT Technology Review Global Panel. He is currently pursuing his studies at Amity University, Lucknow Campus. His prima facie field of learning and research is in the field of International Law, Artificial Intelligence Ethics, Constitutional Jurisprudence and Algorithmic Policing. Despite academics, Abhivardhan is a food lover and is a bilingual poet. He has written over 450 poems in Hindi and English and is an author of 7 books. He is an avid public speaker and legal thinker. Website: https://medium.com/@abhivardhan LinkedIn Profile: https://www.linkedin.com/in/abhivardhan-%C2%B0-92b8b811b/

Artificial Intelligence, the Revolutionary Transformation that no one can scape Key Features Provides perfect ‘playground’ for enterprises and institutions globally to develop Artificial Intelligence solutions The world has achieved an enormous amount of technological advancement and skyrocketing progress in mass Digitization, Data Science, and FinTech The gist of the golden era of AI and FinTech AI-powered autonomous vehicles are undoubtedly the future. Autonomous vehicles are the dawn of a whole new lifestyle Using Artificial Intelligence to redefine their products, processes and strategies Description The book ‘Artificial Intelligence for All’ is a snapshot of AI applications in different industries, society, and everyday life. The book is written considering possibilities AI can bring in the Indian context and considering Indian industries and economy at the center stage. The book starts with describing the race for the supremacy of different countries in the field of Artificial Intelligence that has already taken a great momentum and how AI has managed to influence even mainstream politics and the world leaders. In the subsequent chapters, the book brings in AI applications primarily in the Banking and Finance sectors like Financial Crime detection using AI, Credit Risk Assessment, AI-powered conversational banking, Predictive Analytics, and recommendations in Banking and Finance. What will you learn This book is for both technical and non-technical readers, a cutting edge technology like Artificial Intelligence is simplified for all and a genuine effort has been made to democratize it as much as possible. The book will provide insights into the real applications of AI in different industries like health care and medicine, banking and finance, manufacturing, retail, sports, and many more, including how it’s transforming our life which probably many of us are not even aware of. And most importantly how a country like India can be benefitted by embracing this groundbreaking technology and the huge opportunities and economic impact that AI can bring. Who this book is for This book is useful for AI Professionals, Data Scientists... The content of the book is for both Technical and Non Technical readers who want to know the applications of AI in different industries. No prior technical or programming experience is required to understand this book. Table of Contents 1. Super Powers of AI – The Leaders and the Contenders 2. AI – The Core Fabric for NextGen Banking 3. How an AI Framework can be a Game-Changer in Your AI Journey 4. Artificial Neural Networks 5. The Next Wave of Automation will Transform our Living Experience 6. Self-Driving Cars – Socio-Economic Impact of Autonomous Vehicles 7. How Artificial Intelligence is Transforming the BFSI Sector 43 8. AI Now is a Race Among Startups and Tech Giants 9. AI in the top of priorities for CIOs and CTOs 10. AI in Sports 11. How a Country can be Transformed Using Artificial Intelligence 12. Don’t Underestimate the Power of an AI Chatbot 13. Industry Adoption of Cognitive and Artificial Intelligence 14. Artificial Intelligence – The Biggest Disruptor in the BFSI Industry 15. AI in Healthcare 16. AI in Cyber Security – Cognitive Cyber Defense 17. Be Aware of Cyber Threat 18. AI Revolution in India – National Strategy for AI 19. AI in Tour and Travels – Journey of a Digital Traveler 20. Top 100 Business Use Cases of Artificial Intelligence 21. T Impact of Modern Automation on Employment

Understand how to adopt and implement AI in your organization Key Features 7 Principles of an AI JourneyThe TUSCANE Approach to Become Data ReadyThe FAB-4 Model to Choose the Right AI SolutionMajor AI Techniques & their Applications: - CART & Ensemble Learning - Clustering, Association Rules & Search - Reinforcement Learning - Natural Language Processing - Image Recognition Description Most AI initiatives in organizations fail today not because of a lack of good AI solutions, but because of a lack of understanding of AI among its end users, decision makers and investors. Today, organizations need managers who can leverage AI to solve business problems and provide a competitive advantage. This book is designed to enable you to fill that need, and create an edge for your career.The chapters offer unique managerial frameworks to guide an organization's AI journey. The first section looks at what AI is, and how you can prepare for it, decide when to use it, and avoid pitfalls on the way. The second section dives into the different AI techniques and shows you where to apply them in business. The final section then prepares you from a strategic AI leadership perspective to lead the future of organizations.By the end of the book, you will be ready to offer any organization the capability to use AI successfully and responsibly - a need that is fast becoming a necessity.What will you learn Understand the major AI techniques & how they are used in business.Determine which AI technique(s) can solve your business problem.Decide whether to build or buy an AI solution. Estimate the financial value of an AI solution or company.Frame a robust policy to guide the responsible use of AI. Who this book is for This book is for Executives, Managers and Students on both Business and Technical teams who would like to use Artificial Intelligence effectively to solve business problems or get an edge in their careers.Table of Contents 1. Preface2. Acknowledgement3. About the Author4. Section 1: Beginning an AI Journey a. AI Fundamentals b. 7 Principles of an AI Journey c. Getting Ready to Use AI5. Section 2: Choosing the Right AI Techniques a. Inside the AI Laboratory b. How AI Predicts Values & Categories c. How AI Understands and Predicts Behaviors & Scenarios d. How AI Communicates & Learns from Mistakes e. How AI Starts to Think Like Humans 6. Section 3: Using AI Successfully & Responsibly a. AI Adoption & Valuation b. AI Strategy, Policy & Risk Management 7. Epilogue About the AuthorsMalay A. Upadhyay is a Customer Journey executive, certified in Machine Learning. Over the course of his role heading the function at a N. American AI SaaS firm in Toronto, Malay trained 150+ N. American managers on the basics of AI and its successful adoption, held executive thought leadership sessions for CEOs and CHROs on AI strategy & IT modernization roadmaps, and worked as the primary liaison to realize AI value on unique customer datasets. It was here that he learnt the growing need for greater knowledge and awareness of how to use AI both responsibly and successfully. Malay was also one of 25 individuals chosen globally to envision the industrial future for the Marzotto Group, Italy, on its 175th anniversary. He holds an MBA, M.Sc. and B.E., with experiences across India, UAE, Italy and Canada. A Duke of Edinburgh awardee, Malay has been driving the subject of responsible AI management as an advisor, author, online instructor and member of the European AI Alliance that informed the HLEG on the European Commission’s AI policy. At other times, he remains a Fly that loves to travel and blog with Mrs. Fly. Blog links: www.TheUpadhyays.com

Explore Machine Learning Techniques, Different Predictive Models, and its Applications Key Features -Extensive coverage of real examples on implementation and working of ML models. -Includes different strategies used in Machine Learning by leading data scientists. -Focuses on Machine Learning concepts and their evolution to algorithms. Description This book covers basic concepts of Machine Learning, various learning paradigms, different architectures and algorithms used in these paradigms. You will learn the power of ML models by exploring different predictive modeling techniques such as Regression, Clustering, and Classification. You will also get hands-on experience on methods and techniques such as Overfitting, Underfitting, Random Forest, Decision Trees, PCA, and Support Vector Machines. In this book real life examples with fully working of Python implementations are discussed in detail. At the end of the book you will learn about the unsupervised learning covering Hierarchical Clustering, K-means Clustering, Dimensionality Reduction, Anomaly detection, Principal Component Analysis. What you will learn -Learn to perform data engineering and analysis. -Build prototype ML models and production ML models from scratch. -Develop strong proficiency in using scikit-learn and Python. -Get hands-on experience with Random Forest, Logistic Regression, SVM, PCA, and Neural Networks. Who this book is for This book is meant for beginners who want to gain knowledge about Machine Learning in detail. This book can also be used by Machine Learning users for a quick reference for fundamentals in Machine Learning. Readers should have basic knowledge of Python and Scikit-Learn before reading the book. Table of Contents 1. Introduction to Machine Learning 2. Linear Regression 3. Classification Using Logistic Regression 4. Overfitting and Regularization 5. Feasibility of Learning 6. Support Vector Machine 7. Neural Network 8. Decision Trees 9. Unsupervised Learning 10. Theory of Generalization 11. Bias and Fairness in ML About the Authors Dr Deepti Chopra is working as an Assistant Professor (IT) at Lal Bahadur Shastri Institute of Management, Delhi. She has around 7 years of teaching experience. Her areas of interest include Natural Language Processing, Computational Linguistics, and Artificial Intelligence. She is the author of three books and has written several research papers in various international conferences and journals.

Master Computer Vision concepts using Deep Learning with easy-to-follow steps Key Features Setting up the Python and TensorFlow environment Learn core Tensorflow concepts with the latest TF version 2.0 Learn Deep Learning for computer vision applications Understand different computer vision concepts and use-cases Understand different state-of-the-art CNN architectures Build deep neural networks with transfer Learning using features from pre-trained CNN models Apply computer vision concepts with easy-to-follow code in Jupyter Notebook Description This book starts with setting up a Python virtual environment with the deep learning framework TensorFlow and then introduces the fundamental concepts of TensorFlow. Before moving on to Computer Vision, you will learn about neural networks and related aspects such as loss functions, gradient descent optimization, activation functions and how backpropagation works for training multi-layer perceptrons. To understand how the Convolutional Neural Network (CNN) is used for computer vision problems, you need to learn about the basic convolution operation. You will learn how CNN is different from a multi-layer perceptron along with a thorough discussion on the different building blocks of the CNN architecture such as kernel size, stride, padding, and pooling and finally learn how to build a small CNN model. The book concludes with a chapter on sequential models where you will learn about RNN, GRU, and LSTMs and their architectures and understand their applications in machine translation, image/video captioning and video classification.

Build machine learning models and train them to make Android applications much smarter. Key Features -Learn by doing, training, and evaluating your own machine learning models. -Includes pre-trained TensorFlow models for image processing. -Explains practical use cases of artificial intelligence in Android. Description This book features techniques and real implementations of machine learning applications on Android phones. This the book covers various developer tools, including TensorFlow and Google ML Kit. The book begins with a quick review of android application development fundamentals and a couple of Java and Kotlin implementations developed using the Android Studio integrated development environment. The book explores TensorFlow Lite and Google ML Kit, along with some of the most widely used machine learning techniques. The book covers real projects on TensorFlow, demonstrates how to collect photos with Camera X, and preprocess them with the Google ML Kit. It explains how to onboard the power of machine learning in Android applications that detect images, identify faces, and apply effects to photographs, among other things. These applications are constructed on top of TensorFlow models – some of which were created and trained by the reader – and then converted to TensorFlow Lite for mobile applications. After reading the book, the reader will be able to apply machine learning techniques to create Android applications and take their applications to the next level. This book can be a successful tool to deep dive into Data Science for all mobile programmers. What you will learn -Get well-versed with Android Development and the fundamentals of AI. -Learn to set up the ML environment with hands-on knowledge of TensorFlow. -Build, train, and evaluate Machine Learning models. -Practice ML by working on real face verification and identification applications. -Explore cutting-edge models such as GAN and RNN in detail. -Experience the use of CameraX, SQLite, and Google ML Kit on Android. Who this book is for This book is intended for android developers, application engineers, machine learning engineers, and anybody interested in infusing intelligent, inventive, and smart features into mobile phones. Readers should have a basic understanding of the Java programming language. Table of Contents 1. Building an Application with Android Studio and Java 2. Event Handling and Intents in Android 3. Building our Base Application with Kotlin and SQLite 4. An Overview of Artificial Intelligence and Machine Learning 5. Introduction to TensorFlow 6. Training a Model for Image Recognition with TensorFlow 7. Android Camera Image Capture with CameraX 8. Using the Image Recognition Model in an Android Application 9. Detecting Faces with the Google ML Kit 10. Verifying Faces in Android with TensorFlow Lite 11. Registering Faces in the Application 12. Image Processing with Generative Adversarial Networks 13. Describing Images with NLP

Explore and work with various Microsoft Azure services for real-time Data Analytics Key Features Understanding what Azure can do with your data Understanding the analytics services offered by Azure Understand how data can be transformed to generate more data Understand what is done after a Machine Learning model is built Go through some Data Analytics real-world use cases Description Data is the key input for Analytics. Building and implementing data platforms such as Data Lakes, modern Data Marts, and Analytics at scale require the right cloud platform that Azure provides through its services. The book starts by sharing how analytics has evolved and continues to evolve. Following the introduction, you will deep dive into ingestion technologies. You will learn about Data processing services in Azure. You will next learn about what is meant by a Data Lake and understand how Azure Data Lake Storage is used for analytical workloads. You will then learn about critical services that will provide actual Machine Learning capabilities in Azure. The book also talks about Azure Data Catalog for cataloging, Azure AD for Access Management, Web Apps and PowerApps for cloud web applications, Cognitive services for Speech, Vision, Search and Language, Azure VM for computing and Data Science VMs, Functions as serverless computing, Kubernetes and Containers as deployment options. Towards the end, the book discusses two use cases on Analytics. What will you learn Explore and work with various Azure services Orchestrate and ingest data using Azure Data Factory Learn how to use Azure Stream Analytics Get to know more about Synapse Analytics and its features Learn how to use Azure Analysis Services and its functionalities Who this book is for This book is for anyone who has basic to intermediate knowledge of cloud and analytics concepts and wants to use Microsoft Azure for Data Analytics. This book will also benefit Data Scientists who want to use Azure for Machine Learning. Table of Contents 1. Data and its power 2. Evolution of Analytics and its Types 3. Internet of Things 4. AI and ML 5. Why cloud 6. What are a data lake and a modern datamart 7. Introduction to Azure services 8. Types of data 9. Azure Data Factory 10. Stream Analytics 11. Azure Data Lake Store and Azure Storage 12. Cosmos DB 13. Synapse Analytics 14. Azure Databricks 15. Azure Analysis Services 16. Power BI 17. Azure Machine Learning 18. Sample Architectures and synergies - Real-Time and Batch 19. Azure Data Catalog 20. Azure Active Directory 21. Azure Webapps 22. Power apps 23. Time Series Insights 24. Azure Cognitive Services 25. Azure Logicapps 26. Azure VM 27. Azure Functions 28. Azure Containers 29. Azure Kubernetes Service 30. Use Case 1 31. Use Case 2 About the Authors Prashila Naik has over 16 years of experience in the tech sector. She has worked for multiple global organizations, primarily in the data and analytics space. She has seen data and analytics grow from strength to strength and thinks it will always be one of the most interesting areas in technology ever. She She is also a writer who primarily writes creative fiction and non-fiction, as well as an occasional translator. Her short stories have been published in various leading literary journals in India and elsewhere. Your LinkedIn Profile: https://www.linkedin.com/in/prashila-naik-7645604

State-of-the-art BERT implementation for text classification Description This book provides a solid foundation for ‘Natural Language Processing’ with pragmatic explanation and implementation of a wide variety of industry wide scenarios. After reading this book, one can simply jump to solve real world problems and join the league of NLP developers. It starts with the introduction of Natural Language Processing and provides a good explanation of different practical situations which are currently implemented across the globe. Thereafter, it takes a deep dive into the text classification with different types of algorithms to implement the same. Then, it further introduces the second important NLP use case called Named Entity Recognition with its popular algorithm choices. Thereafter, it provides an introduction to a state of the art language model called BERT and its application. What you will learn -Learn to implement transfer learning on pre-trained BERT models. -Learn to demonstrate a production ready Text Classification for domain specific data and networking using Python 3.x. -Learn about the domain specific pre trained models with a library called `aiops` which has been specially designed for this book. Who this book is for This book is meant for Data Scientists and Machine Learning Engineers who are new to Natural Language Processing and want to quickly implement different NLP use-cases. Readers should have a basic knowledge of Python before reading the book. Table of Contents 1. Introduction to NLP and Different Use-Cases 2. Deep Dive into Text Classification and Different Types of Algorithms in Industry 3. Named Entity Recognition 4. BERT and its Application 5. BERT: Text Classification 6. BERT: Text Classification Code About the Authors Amandeep has been working as a technical lead in the field of software development at the time of publishing this book. He has worked for almost eight years in a few of the top MNCs.