Take a look at our COM021030 books. Shulph carries a great selection of COM021030 books, and we are always adding more.
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/
Hands-On ML problem solving and creating solutions using Python. Key Features Introduction to Python Programming Python for Machine Learning Introduction to Machine Learning Introduction to Predictive Modelling, Supervised and Unsupervised Algorithms Linear Regression, Logistic Regression and Support Vector Machines Description You will learn about the fundamentals of Machine Learning and Python programming post, which you will be introduced to predictive modelling and the different methodologies in predictive modelling. You will be introduced to Supervised Learning algorithms and Unsupervised Learning algorithms and the difference between them. We will focus on learning supervised machine learning algorithms covering Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees and Artificial Neural Networks. For each of these algorithms, you will work hands-on with open-source datasets and use python programming to program the machine learning algorithms. You will learn about cleaning the data and optimizing the features to get the best results out of your machine learning model. You will learn about the various parameters that determine the accuracy of your model and how you can tune your model based on the reflection of these parameters. What You Will Learn Get a clear vision of what is Machine Learning and get familiar with the foundation principles of Machine learning. Understand the Python language-specific libraries available for Machine learning and be able to work with those libraries. Explore the different Supervised Learning based algorithms in Machine Learning and know how to implement them when a real-time use case is presented to you. Have hands-on with Data Exploration, Data Cleaning, Data Preprocessing and Model implementation. Get to know the basics of Deep Learning and some interesting algorithms in this space. Choose the right model based on your problem statement and work with EDA techniques to get good accuracy on your model Who this book is for This book is for anyone interested in understanding Machine Learning. Beginners, Machine Learning Engineers and Data Scientists who want to get familiar with Supervised Learning algorithms will find this book helpful. Table of Contents 1. Introduction to Python Programming 2. Python for Machine Learning 3. Introduction to Machine Learning 4. Supervised Learning and Unsupervised Learning 5. Linear Regression: A Hands-on guide 6. Logistic Regression – An Introduction 7. A sneak peek into the working of Support Vector machines(SVM) 8. Decision Trees 9. Random Forests 10. Time Series models in Machine Learning 11. Introduction to Neural Networks 12. Recurrent Neural Networks 13. Convolutional Neural Networks 14. Performance Metrics 15. Introduction to Design Thinking 16. Design Thinking Case Study About the Author Gnana Lakshmi T C —iis Technology Geek, Innovator, Keynote speaker, Community builder and holds a Bachelor degree in Computer Science from National Institute of Technology, Tiruchirappalli. She is currently associated with WileyNXT as Product Manager, Emerging Technologies. She is also a Fellow Alumni at WomenWhoCode and started WomenWhoCode Blockchain community (www.womenwhocode.com/blockchain). She harnesses her knowledge by sharing it with others by conducting live events like webinars and workshops and through online channels like tutorials, social media posts etc. She has conducted several meetups on Machine learning, Blockchain and various other emerging technology topics including a recent meetup at the International Open UP Summit on GPT-3. LinkedIn Profile: https://www.linkedin.com/in/gyan-lakshmi Madeleine Shang —is a Recommender Systems Team Lead @OpenMined. She started the Data Science and Machine Learning community at WomenWhoCode which is now successfully running with 2147 members. She is an expert in AI and Blockchain Research. She has been involved in many startups as a Founder. She is an Adventurer and Futurist at heart. LinkedIn Profile: https://www.linkedin.com/in/madeleine-shang/
Guidance for successful installation of a wide range of IBM software products Key Features -Complete installation guide of IBM software systems, Redhat Enterprise, IBM Cloud, and Docker. -Expert-led demonstration on complete configuration and implementation of IBM software solutions. -Includes best practices and efficient techniques adopted by banks, financial services, and insurance companies. Description This book provides instructions for installation, configuration and troubleshooting sections to improve the IT support productivity and fast resolution of issues that arise. It covers readers' references that are available online and also step-by-step procedures required for a successful installation of a broad range of IBM Data Analytics products. This book provides a holistic in-depth knowledge for students, software architects, installation specialists, and developers of Data Analysis software and a handbook for data analysts who want a single source of information on IBM Data Analysis Software products. This book provides a single resource that covers the latest available IBM Data Analysis software on the most recent RedHat Linux and IBM Cloud platforms. This book includes comprehensive technical guidance, enabling IT professionals to gain an in-depth knowledge of the installation of a broad range of IBM Software products across different operating systems. What you will learn -Step-by-step installation and configuration of IBM Watson Analytics. -Managing RedHat Enterprise Systems and IBM Cloud Platforms. -Installing, configuring, and managing IBM StoredIQ. -Best practices to administer and maintain IBM software packages. -Upgrading VMware stations and installing Docker. Who this book is for This book is a go-to guide for IT professionals who are primarily Solution Architects, Implementation Experts, or Technology Consultants of IBM Software suites. This will also be a useful guide for IT managers who are looking to adopt and enable their enterprise with IBM products. Table of Contents 1. Getting Started with IBM Resources for Analytics 2. IBM Component Software Compatibility Matrix 3. IBM Download Procedures 4. On-Premise Server Configurations and Prerequisites 5. IBM Fix Packs 6. IBM Cloud PAK Systems 7. RedHat OpenShift 4.x Installations 8. IBM Cloud Private System 9. Base VMWare System Platform 10. IBM Cloud Private Cluster on CentOS 8.0 11. UIMA Pipeline and Java Code Extensions 12. IBM Watson Explorer Foundational Components V12 13. IBM Watson Explorer oneWEX 12.0.3 14. IBM StoredIQ for Legal       APPENDIX References and End of Life Support About the Authors Alan Bluck, has over 45 years of IT experience. He has been a Solutions Architect for IBM for over 10 years. He is now the Director and owner of ASB Software Development Limited, an IBM PartnerWorld partner and a consultancy providing systems architecture for a broad range of services. He is a member of the British Computer Society (MBCS, CITP).
A Cookbook that will help you implement Machine Learning algorithms and techniques by building real-world projects Key Features Learn how to handle an entire Machine Learning Pipeline supported with adequate mathematics. Create Predictive Models and choose the right model for various types of Datasets. Learn the art of tuning a model to improve accuracy as per Business requirements. Get familiar with concepts related to Data Analytics with Visualization, Data Science and Machine Learning. Description Machine Learning does not have to be intimidating at all. This book focuses on the concepts of Machine Learning and Data Analytics with mathematical explanations and programming examples. All the codes are written in Python as it is one of the most popular programming languages used for Data Science and Machine Learning. Here I have leveraged multiple libraries like NumPy, Pandas, scikit-learn, etc. to ease our task and not reinvent the wheel. There are five projects in total, each addressing a unique problem. With the recipes in this cookbook, one will learn how to solve Machine Learning problems for real-time data and perform Data Analysis and Analytics, Classification, and beyond. The datasets used are also unique and will help one to think, understand the problem and proceed towards the goal. The book is not saturated with Mathematics, but mostly all the Mathematical concepts are covered for the important topics. Every chapter typically starts with some theory and prerequisites, and then it gradually dives into the implementation of the same concept using Python, keeping a project in the background. What will you learn Understand the working of the O.S.E.M.N. framework in Data Science. Get familiar with the end-to-end implementation of Machine Learning Pipeline. Learn how to implement Machine Learning algorithms and concepts using Python. Learn how to build a Predictive Model for a Business case. Who this book is for This cookbook is meant for anybody who is passionate enough to get into the World of Machine Learning and has a preliminary understanding of the Basics of Linear Algebra, Calculus, Probability, and Statistics. This book also serves as a reference guidebook for intermediate Machine Learning practitioners. Table of Contents 1. Boston Crime 2. World Happiness Report 3. Iris Species 4. Credit Card Fraud Detection 5. Heart Disease UCI About the Author Rehan Guha —A Researcher by the day and an Artist by night. Our Author is a Scholar -lecturer, an Innovator, and also a Humanitarian -Philanthropist. He started his life as a Coder, Developer, and now he is into research in the field of Machine Learning and Algorithms but also has a keen interest in General Science, Technology, Invention & Innovation. The author holds a graduation degree from the Institute of Engineering & Management, Kolkata, and a Postgraduate certification on Deep Learning from the Indian Institute of Technology, Kharagpur (IIT-K)-AICTE approved FDP course. If we talk about Rehan's area of interest, it lies in Optimization Problems, Explainable AI, Deep Learning Architecture, Algorithms, Complexity, Algorithmic Thinking, et cetera… He has multiple publications through Journals and Open Publications, along with his publications he has filed multiple patents for his Innovations and Inventions. At an early age, one of his patents was also demonstrated to the Indian Army. In Rehan’s career, he has been involved with a variety of Business Verticals, starting from Banking, Consulting, Law, Insurance, Freight & Logistics, and Telcom.
Covers Data Science concepts, processes, and the real-world hands-on use cases. Key Features -Covers the journey from a basic programmer to an effective Data Science developer. -Applied use of Data Science native processes like CRISP-DM and Microsoft TDSP. -Implementation of MLOps using Microsoft Azure DevOps. Description "How is the Data Science project to be implemented?" has never been more conceptually sounding, thanks to the work presented in this book. This book provides an in-depth look at the current state of the world's data and how Data Science plays a pivotal role in everything we do. This book explains and implements the entire Data Science lifecycle using well-known data science processes like CRISP-DM and Microsoft TDSP. The book explains the significance of these processes in connection with the high failure rate of Data Science projects. The book helps build a solid foundation in Data Science concepts and related frameworks. It teaches how to implement real-world use cases using data from the HMDA dataset. It explains Azure ML Service architecture, its capabilities, and implementation to the DS team, who will then be prepared to implement MLOps. The book also explains how to use Azure DevOps to make the process repeatable while we're at it. By the end of this book, you will learn strong Python coding skills, gain a firm grasp of concepts such as feature engineering, create insightful visualizations and become acquainted with techniques for building machine learning models. What you will learn -Organize Data Science projects using CRISP-DM and Microsoft TDSP. -Learn to acquire and explore data using Python visualizations. -Get well versed with the implementation of data pre-processing and Feature Engineering. -Understand algorithm selection, model development, and model evaluation. -Hands-on with Azure ML Service, its architecture, and capabilities. -Learn to use Azure ML SDK and MLOps for implementing real-world use cases. Who this book is for This book is intended for programmers who wish to pursue AI/ML development and build a solid conceptual foundation and familiarity with related processes and frameworks. Additionally, this book is an excellent resource for Software Architects and Managers involved in the design and delivery of Data Science-based solutions. Table of Contents 1. Data Science for Business 2. Data Science Project Methodologies and Team Processes 3. Business Understanding and Its Data Landscape 4. Acquire, Explore, and Analyze Data 5. Pre-processing and Preparing Data 6. Developing a Machine Learning Model 7. Lap Around Azure ML Service 8. Deploying and Managing Models