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Come and join hands together to learn Python from scratch. This book will help you understand Python from scratch and help you build a career in the field of programming. Key Features -Exciting examples and a solid grasp of the principles of Python. -An easy guide for absolute beginners to enjoy coding while learning. -Exception handling, OOPs fundamentals, inheritance, and reusability explained in detail. Description The book offers to teach a novice programmer the fundamentals of Python programming from the ground up. The book provides a brief history of Python, followed by exploring Python's fundamental concepts, features, and applications in detail. The book explains Python identifiers, keywords, variables, and assignments, as well as basic operators and decision-making statements. This book covers repetitive code, strings and integers (dictionaries), functions and modules (files), exception handling, and object-oriented programming in all of its variants. The book explains concepts with illustrations, thus making it simple for even the most unskilled reader to grasp the basics of the code execution flow. By the end of this book, you will have a firm grasp of all of Python's programming ideas. Additionally, it will help you to prepare for any upcoming job interviews with your comprehensive Python understanding. What you will learn -Quickly grasp the concepts of lists, tuples, dictionaries, and functions. -Examine Python's effective use of exception handling. -Makes object-oriented programming more understandable. -Discover when and how to use Python's decision-making statements. -Use Python to perform and execute file operations. Who this book is for This book is for web application developers, entry level developers, and IT graduates who want to learn the entire web application development by developing a solid hold on Python principles. Basic programming knowledge is recommended but not required. Table of Contents 1. Introduction 2. Basic Syntax 3. Variable Types 4. Basic Operators 5. Decision Making 6. Repeating Code Using Loops 7. Numbers 8. Strings 9. Lists 10. Tuples 11. Dictionaries 12. Functions 13. Modules 14. Files I/O 15. Exception Handling 16. Object-Oriented Programming
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.
A power-packed guide with solutions to crack a Big data Hadoop Interview Key Features Get familiar with Big data concepts Understand the working of Hadoop and its ecosystem. Understand the working of HBase, Pig, Hive, Flume, Sqoop and Spark Understand the capabilities of Big data including Hadoop and HDFS Up and running with how to perform speedy data processing using Apache Spark Description This book prepares you for Big data interviews w.r.t. Hadoop system and its ecosystems such as HBase, Pig, Hive, Flume, Sqoop, and Spark. Over the last few years, there is a rise in demand for Big Data Scientists/Analysts throughout the globe. Data Analysis and Interpretation have become very important lately The book covers many interview questions and the best possible ways to answer them. Along with the answers, you will come across real-world examples that will help you understand the concepts of Big Data. The book is divided into various sections to make it easy for you to remember and associate it with the questions asked. What you will learn Apache Pig interview questions and answers HBase and Hive interview questions and answers Apache Sqoop interview questions and answers Apache Flume interview questions and answers Apache Spark interview questions and answers Who this book is for This book is for anyone interested in big data. It is also useful for all jobseekers and freshers who wants to drive their career in the field of Big Data and Data Processing. Table of Contents 1. Big data, Hadoop and HDFS interview questions 2. Apache PIG interview questions 3. Hive interview questions 4. Hbase interview questions 5. Apache Sqoop interview questions 6. Apache Flume interview questions 7. Apache Spark interview questions About the Authors Vishwanathan Narayanan is an extreme programmer in various technologies, including Java, Python, and R, and he has around 18 years of experience in the field of information technology and data science. Exposure to real-world data science and advanced analytics using big data technologies gives him a great advantage, which he tries to impart through his books. A passionate teacher, he likes writing books as a hobby.
Get answers to frequently asked questions on Data Science and Machine Learning using R Key Features Understand the capabilities of the R programming language Most of the machine learning algorithms and their R implementation covered in depth Answers on conceptual data science concepts are also covered Description This book prepares you for the Data Scientist and Machine Learning Engineer interview w.r.t. R programming language. The book is divided into various parts, making it easy for you to remember and associate with the questions asked in an interview. It covers multiple possible transformations and data filtering techniques in depth. You will be able to create visualizations like graphs and charts using your data. You will also see some examples of how to build complex charts with this data. This book covers the frequently asked interview questions and shares insights on the kind of answers that will help you get this job. By the end of this book, you will not only crack the interview but will also have a solid command of the concepts of Data Science as well as R programming. What will you learn Get answers to the basics, intermediate and advanced questions on R programming Understand the transformation and filtering capabilities of R Know how to perform visualization using R Who this book is for This book is a must for anyone interested in Data Science and Machine Learning. Anyone who wants to clear the interview can use it as a last-minute revision guide. Table of Contents 1. Data Science basic questions and terms 2. R programming questions 3. GGPLOT Questions 4. Statistics with excel sheet About the Author Vishwanathan Narayanan has 18 years of experience in the field of information technology and data analysis. He made many enterprise-level applications with stable output and scalability. Advanced level data analysis for complex problems using both R and Python has been the key area of work for many years. Extreme programmer on Java, Python, R, and many more technologies
Primer into the multidisciplinary world of Data Science Key Features Explore and use the key concepts of Statistics required to solve data science problems Use Docker, Jenkins, and Git for Continuous Development and Continuous Integration of your web app Learn how to build Data Science solutions with GCP and AWS Description The book will initially explain the What-Why of Data Science and the process of solving a Data Science problem. The fundamental concepts of Data Science, such as Statistics, Machine Learning, Business Intelligence, Data pipeline, and Cloud Computing, will also be discussed. All the topics will be explained with an example problem and will show how the industry approaches to solve such a problem. The book will pose questions to the learners to solve the problems and build the problem-solving aptitude and effectively learn. The book uses Mathematics wherever necessary and will show you how it is implemented using Python with the help of an example dataset. What you will learn Understand the multi-disciplinary nature of Data Science Get familiar with the key concepts in Mathematics and Statistics Explore a few key ML algorithms and their use cases Learn how to implement the basics of Data Pipelines Get an overview of Cloud Computing & DevOps Learn how to create visualizations using Tableau Who this book is for This book is ideal for Data Science enthusiasts who want to explore various aspects of Data Science. Useful for Academicians, Business owners, and Researchers for a quick reference on industrial practices in Data Science. Table of Contents 1. Data Science in Practice 2. Mathematics Essentials 3. Statistics Essentials 4. Exploratory Data Analysis 5. Data preprocessing 6. Feature Engineering 7. Machine learning algorithms 8. Productionizing ML models 9. Data Flows in Enterprises 10. Introduction to Databases 11. Introduction to Big Data 12. DevOps for Data Science 13. Introduction to Cloud Computing 14. Deploy Model to Cloud 15. Introduction to Business Intelligence 16. Data Visualization Tools 17. Industry Use Case 1 – FormAssist 18. Industry Use Case 2 – PeopleReporter 19. Data Science Learning Resources 20. Do It Your Self Challenges 21. MCQs for Assessments About the Author The book has been written by collective experience of many of Probyto past client projects, academic collaborations and team members for last 5 years. The collective work is represented by different experts in data driven decision making and portion they deal with in creating value for the clients at Probyto. The team has experienced professionals and freshers who have gained from the approach as mentioned in the book as well. Two key contributions for the book goes to Parvej Reja Saleh (Manager) and Namachivayam Dharmalingam (Senior Analyst). Blog links: https://probyto/resources/blogs LinkedIn Profile: https://www.linkedin.com/company/probyto
Learn how to process and analysis data using Python Key Features The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code. The book is not just dealing with the background mathematics alone or only the programs but beautifully correlates the background mathematics to the theory and then finally translating it into the programs. A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. Description This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems. Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language. Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic. What will you learn Perform processing on data for making it ready for visual plot and understand the pattern in data over time. Understand what machine learning is and how learning can be incorporated into a program. Know how tools can be used to perform analysis on big data using python and other standard tools. Perform social media analytics, business analytics, and data analytics on any data of a company or organization. Who this book is for The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems. Table of Contents 1. Fundamentals of Data Science1 2. Data Preprocessing 3. Data Plotting and Visualization 4. Statistical Data Analysis 5. Machine Learning for Data Science 6. Time-Series Analysis 7. Deep Learning for Data Science 8. Social Media Analytics 9. Business Analytics 10. Big Data Analytics About the Author Dr. Gypsy Nandi is an Assistant Professor (Sr) in the Department of Computer Applications, Assam Don Bosco University, India. Her areas of interest include Data Science, Social Network Mining, and Machine Learning. She has completed her Ph.D. in the field of ‘Social Network Analysis and Mining’. Her research scholars are currently working mainly in the field of Data Science. She has several research publications in reputed journals and book series. Dr. Rupam Kumar Sharma is an Assistant Professor in the Department of Computer Applications, Assam Don Bosco University, India. His area of interest includes Machine Learning, Data Analytics, Network, and Cyber Security. He has several research publications in reputed SCI and Scopus journals.
Learn AI & Machine Learning from the first principles. Key Features Explore how different industries are using AI and ML for diverse use-cases. Learn core concepts of Data Science, Machine Learning, Deep Learning and NLP in an easy and intuitive manner. Cutting-edge coverage on use of ML for business products and services. Explore how different companies are monetizing AI and ML technologies. Learn how you can start your own journey in the AI field from scratch. Description AI and machine learning (ML) are probably the most fascinating technologies of the 21st century. AI is literally in every industry now. From medical to climate change, education to sport, finance to entertainment, AI is disrupting every industry as we know. So, the basic knowledge of AI/ML becomes mandatory for everyone. This book is your first step to start the journey in this field. Along with basic concepts of fields, like machine learning, deep learning and NLP, we will also explore how big companies are using these technologies to deliver greater user experience and earning millions of dollars in profit. Also, we will see how the owners of small- or medium-sized businesses can leverage and integrate these technologies with their products and services. Leveraging AI and ML can become that competitive moat which can differentiate the product from others. In this book, you will learn the root concepts of AI/ML and how these inanimate machines can actually become smarter than the humans at a few tasks, and how companies are using AI and how you can leverage AI to earn profits. What you will learn Core concepts of data science, machine learning, deep learning and NLP in simple and intuitive words How you can leverage and integrate AI technologies in your business to differentiate your product in the market. The limitations of traditional non-tech businesses and how AI can bridge those gaps to increase revenues and decrease costs. How AI can help companies in launching new products, improving existing ones and automating mundane processes. Explore how big tech companies are using AI to automate different tasks and providing unique product experiences to their users. Who this book is for This book is for anyone who is curious about this fascinating technology and how it really works at its core. It is also beneficial to those who want to start their career in AI/ ML. Table of Contents 1.Introduction 2. Going deeper in ML concepts 3. Business perspective of AI 4. How to get started and pitfalls to avoid About the Authors Prashant Kikani is an experienced data scientist, who ranks in the top 1% worldwide in competitions and kernels on Kaggle which is the world's largest community and platform for data science and machine learning. As part of his day-to-day work, he is working on solving some of the hardest problems for the human kind, like language translation using state-of-the-art deep learning-based NLP models and infusion of knowledge graphs in NLP models. He is one of the youngest students to achieve the Master title on the Kaggle kernel platform. Also, he has worked on other deep learning sub-fields like computer vision via Kaggle competitions. His interests lie in AI/ML and deep learning, and teaching others what he has learned in a very simple and intuitive manner. This book is part of his interest to share his knowledge in the simplest possible manner with everyone so that everyone they can learn about this fascinating technology called AI! Blog links:http://prashantkikani.com LinkedIn Profile://https://www.linkedin.com/in/prashant-kikani/
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
Learn, develop, test and document powerful yet simple RAML API specifications using MuleSoft API Designer and API Toolkit. Key Features Explore concept of API and its significance in enterprise applications Design your own API using Mulesoft Anypoint Platform Exciting coverage on how API works in Enterprise Applications Live demonstration on how to build and integrate API with end-to-end implementation and working code Description Hands-on MuleSoft Anypoint platform book directs you step-by-step in designing API, its Implementation, and how to integrate smartly with other applications. This book is enriched with lots of interactive screenshots and working source codes. Throughout this book, you will learn key industry insights on System Integration, API Led Connectivity, Centre for Enablement, and RAML. This book will talk about how to use publicly available free mock REST APIs and how to call and test them from RESTful clients like Postman. You can also see some of the commercially available license-based APIs. Equipped with exercises, you will practice developing your own RESTful API specification along with how to add, retrieve, update, and delete data for your business use. You will be using the MuleSoft Anypoint Platform Designer for designing and simulating your RAML API design specifications. At the end of the book, you will be summarizing your learnings with an end-to-end implementation demonstration on the API design and its implementation. What you will learn Know-how of public APIs, commercial APIs, and cloud-based SaaS APIs Role of Mulesoft in SaaS applications You learn to design and test the API development and implementation You get handy with all the features and mechanism of Mulesoft Anypoint Platform Who this book is for This book is for fresher, IT employees with less or no programming background such as Business Analysts, Quality Engineers, HR, Technical persons who are looking for a change in technology area if they are working in outdated technologies. Table of Contents 1. MuleSoft Fundamentals 2. MuleSoft Internals 3. MuleSoft Salient features 4. From ESB to API Led Connectivity 5. Cloud based SaaS Applications and MuleSoft Connectors 6. REST, SOAP, Postman and Anypoint Studio 7. Start RAML 8. RAML in detail 9. RAML Project About the Authors Nanda Nachimuthu is an Engineering graduate from Tamilnadu Agricultural University, Coimbatore, and Tamilnadu and has done Advanced Diploma from Indian Institute of Technology, Kharagpur in the field of Java and Internet Computing. He has also completed an Advanced Diploma from Indian Institute of International Trading, Delhi which specializes on Strategies for International Business. His 25 years of experience comes from various domains like Banking, Healthcare, Government and Airlines. He is, in to the technologies like Java, Big Data, Cloud, ESB, Security and IoT. He played various roles like Technical Architect, Solutions Architect, Cloud Architect and Enterprise Integration Architect and wanted to be in an Individual Contributor role always with hands-on coding experience. He is passionate about Social Entrepreneurship and Pro Bono consultations in multiple fields like Information Technology, Manufacturing, Trading, Agriculture and Internet of Things. He is the founder of some social platforms, and he owns few trademarks under his kitty. Presently he is focusing on Integration Technology Platforms like MuleSoft, where he finds lots of scope in the future for Digital Marketing and Machine to Machine Communications. LinkedIn Profile: https://www.linkedin.com/in/nanda3008/ Github: https://github.com/nanda3008
Hands-on MuleSoft Flows using MuleSoft Anypoint Studio Components and understanding Payload processing along with debugging. Key Features Get familiar with the MuleSoft Anypoint Studio key techniques such as Payload, Logger, Variables, Flow and Flow Reference. Deep dive into Massage Structure and Payload value handling. Get familiar with the Global Configuration Properties and Securing properties. Explore Mule Run Time and Deploying Mule Projects in CloudHub. Description This book is aimed to teach the readers how to design RAML APIs using Anypoint Platform. It also focuses on popular topics such as System Integration, API Led Connectivity, and Centre for Enablement and RAML. It will show how to use, call and test free mock REST APIs. The readers can also work with some commercially available license-based APIs. Furthermore, the book will explain most of the examples provided by RAML.org so that you can simulate it from your local system. This book will then help you develop your RESTful API specification for adding, retrieving, updating and deleting data for a business entity. Later, you will learn how to use the MuleSoft Anypoint Platform Designer for designing and simulating your RAML API design specifications. By the end, you will be able to develop an end to end RAML API using the MuleSoft Anypoint Studio. What you will learn Get exposed to Payload handling, logging and variables Work with different Flow Control components such as Choice, First Successful, Round-Robin and Scatter Gather Explore and work with Error Handling components such as Error Handler, On Error, Continue, On Error Propagate and Raise Error Understand Global Configuration Properties and Securing properties Gain knowledge about various scopes involved in MuleSoft Flow designing Who this book is for This book is meant for anyone interested to become an API designer. Experienced technical persons of the IT industry also can utilize the book to get extra insights, and they can align their knowledge in line with it. Table of Contents 1. Start Project 2. Anypoint Studio Components 3. Flow Control Components 4. Idempotent, Parse Template and Scheduler 5. Payload Component 6. MUnit 7. MuleSoft Runtime 8. Global Secured Configurations 9. Error Handling 10. RAML and Anypoint Studio About the Authors Nanda Nachimuthu is an Engineering graduate from Tamil Nadu Agricultural University, Coimbatore, and has completed Advanced Diploma from the Indian Institute of Technology, Kharagpur, in the field of Java and Internet Computing. He has also completed an Advanced Diploma from Indian Institute of International Trading, Delhi, which specializes in Strategies for International Business. He has 25 years of experience in various domains like banking, healthcare, government, and airlines. He’s into technologies like Java, Big Data, Cloud, ESB, Security and IoT. He has taken up various roles like technical architect, solutions architect, cloud architect, and enterprise integration architect and always wanted to take up an individual contributor’s role with hands-on coding experience. He is passionate about social entrepreneurship and takes pro-bono consultations in multiple fields like information technology, manufacturing, trading, agriculture, and internet of things. The founder of some social platforms, he also has a few trademarks under his kitty. Presently, he is focusing on integration technology platforms like MuleSoft, where he finds a wide scope in the future for digital marketing and machine to machine communication. Github Link: github.com/nanda3008 LinkedIn Profile: https://www.linkedin.com/in/nanda3008/