Hands On Machine Learning With Microsoft Excel 2019
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| Author | : Julio Cesar Rodriguez Martino |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 243 |
| Release | : 2019-04-30 |
| Genre | : Computers |
| ISBN | : 178934512X |
A practical guide to getting the most out of Excel, using it for data preparation, applying machine learning models (including cloud services) and understanding the outcome of the data analysis. Key FeaturesUse Microsoft's product Excel to build advanced forecasting models using varied examples Cover range of machine learning tasks such as data mining, data analytics, smart visualization, and more Derive data-driven techniques using Excel plugins and APIs without much code required Book Description We have made huge progress in teaching computers to perform difficult tasks, especially those that are repetitive and time-consuming for humans. Excel users, of all levels, can feel left behind by this innovation wave. The truth is that a large amount of the work needed to develop and use a machine learning model can be done in Excel. The book starts by giving a general introduction to machine learning, making every concept clear and understandable. Then, it shows every step of a machine learning project, from data collection, reading from different data sources, developing models, and visualizing the results using Excel features and offerings. In every chapter, there are several examples and hands-on exercises that will show the reader how to combine Excel functions, add-ins, and connections to databases and to cloud services to reach the desired goal: building a full data analysis flow. Different machine learning models are shown, tailored to the type of data to be analyzed. At the end of the book, the reader is presented with some advanced use cases using Automated Machine Learning, and artificial neural network, which simplifies the analysis task and represents the future of machine learning. What you will learnUse Excel to preview and cleanse datasetsUnderstand correlations between variables and optimize the input to machine learning modelsUse and evaluate different machine learning models from ExcelUnderstand the use of different visualizationsLearn the basic concepts and calculations to understand how artificial neural networks workLearn how to connect Excel to the Microsoft Azure cloudGet beyond proof of concepts and build fully functional data analysis flowsWho this book is for This book is for data analysis, machine learning enthusiasts, project managers, and someone who doesn't want to code much for performing core tasks of machine learning. Each example will help you perform end-to-end smart analytics. Working knowledge of Excel is required.
| Author | : Shmuel Oluwa |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 280 |
| Release | : 2019-07-11 |
| Genre | : Computers |
| ISBN | : 1789531632 |
Explore the aspects of financial modeling with the help of clear and easy-to-follow instructions and a variety of Excel features, functions, and productivity tips Key FeaturesA non data professionals guide to exploring Excel's financial functions and pivot tablesLearn to prepare various models for income and cash flow statements, and balance sheetsLearn to perform valuations and identify growth drivers with real-world case studiesBook Description Financial modeling is a core skill required by anyone who wants to build a career in finance. Hands-On Financial Modeling with Microsoft Excel 2019 examines various definitions and relates them to the key features of financial modeling with the help of Excel. This book will help you understand financial modeling concepts using Excel, and provides you with an overview of the steps you should follow to build an integrated financial model. You will explore the design principles, functions, and techniques of building models in a practical manner. Starting with the key concepts of Excel, such as formulas and functions, you will learn about referencing frameworks and other advanced components of Excel for building financial models. Later chapters will help you understand your financial projects, build assumptions, and analyze historical data to develop data-driven models and functional growth drivers. The book takes an intuitive approach to model testing, along with best practices and practical use cases. By the end of this book, you will have examined the data from various use cases, and you will have the skills you need to build financial models to extract the information required to make informed business decisions. What you will learnIdentify the growth drivers derived from processing historical data in ExcelUse discounted cash flow (DCF) for efficient investment analysisBuild a financial model by projecting balance sheets, profit, and lossApply a Monte Carlo simulation to derive key assumptions for your financial modelPrepare detailed asset and debt schedule models in ExcelDiscover the latest and advanced features of Excel 2019Calculate profitability ratios using various profit parametersWho this book is for This book is for data professionals, analysts, traders, business owners, and students, who want to implement and develop a high in-demand skill of financial modeling in their finance, analysis, trading, and valuation work. This book will also help individuals that have and don't have any experience in data and stats, to get started with building financial models. The book assumes working knowledge with Excel.
| Author | : Fernando Roque |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 325 |
| Release | : 2022-05-27 |
| Genre | : Computers |
| ISBN | : 1803235268 |
Perform time series forecasts, linear prediction, and data segmentation with no-code Excel machine learning Key Features • Segment data, regression predictions, and time series forecasts without writing any code • Group multiple variables with K-means using Excel plugin without programming • Build, validate, and predict with a multiple linear regression model and time series forecasts Book Description Data Forecasting and Segmentation Using Microsoft Excel guides you through basic statistics to test whether your data can be used to perform regression predictions and time series forecasts. The exercises covered in this book use real-life data from Kaggle, such as demand for seasonal air tickets and credit card fraud detection. You'll learn how to apply the grouping K-means algorithm, which helps you find segments of your data that are impossible to see with other analyses, such as business intelligence (BI) and pivot analysis. By analyzing groups returned by K-means, you'll be able to detect outliers that could indicate possible fraud or a bad function in network packets. By the end of this Microsoft Excel book, you'll be able to use the classification algorithm to group data with different variables. You'll also be able to train linear and time series models to perform predictions and forecasts based on past data. What you will learn • Understand why machine learning is important for classifying data segmentation • Focus on basic statistics tests for regression variable dependency • Test time series autocorrelation to build a useful forecast • Use Excel add-ins to run K-means without programming • Analyze segment outliers for possible data anomalies and fraud • Build, train, and validate multiple regression models and time series forecasts Who this book is for This book is for data and business analysts as well as data science professionals. MIS, finance, and auditing professionals working with MS Excel will also find this book beneficial.
| Author | : Lane, Carol-Ann |
| Publisher | : IGI Global |
| Total Pages | : 958 |
| Release | : 2022-01-07 |
| Genre | : Language Arts & Disciplines |
| ISBN | : 1799872734 |
Emerging technologies are becoming more prevalent in global classrooms. Traditional literacy pedagogies are shifting toward game-based pedagogy, addressing 21st century learners. Therefore, within this context there remains a need to study strategies to engage learners in meaning-making with some element of virtual design. Technology supports the universal design learning framework because it can increase the access to meaningful engagement in learning and reduce barriers. The Handbook of Research on Acquiring 21st Century Literacy Skills Through Game-Based Learning provides theoretical frameworks and empirical research findings in digital technology and multimodal ways of acquiring literacy skills in the 21st century. This book gains a better understanding of how technology can support leaner frameworks and highlights research on discovering new pedagogical boundaries by focusing on ways that the youth learn from digital sources such as video games. Covering topics such as elementary literacy learning, indigenous games, and student-worker training, this book is an essential resource for educators in K-12 and higher education, school administrators, academicians, pre-service teachers, game developers, researchers, and libraries.
| Author | : Prashila Naik |
| Publisher | : BPB Publications |
| Total Pages | : 309 |
| Release | : 2020-11-12 |
| Genre | : Computers |
| ISBN | : 9389898145 |
Explore and work with various Microsoft Azure services for real-time Data Analytics KEY FEATURESÊ 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 builtÊ Go through some Data Analytics real-world use cases ÊÊ DESCRIPTIONÊ 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 LEARNÊÊ 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 Ê WHO THIS BOOK IS FORÊ 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. Ê TABLE OF CONTENTSÊÊ 1. Ê Data and its power 2. Ê Evolution of Analytics and its Types 3. Ê Internet of Things 4. Ê AI and ML 5. Ê Why cloud 6. Ê What are a data lake and a modern datamart 7. Ê Introduction to Azure services 8. Ê Types of data 9. Ê Azure Data Factory 10. Stream Analytics 11. Azure Data Lake Store and Azure Storage 12. Cosmos DB 13.Ê Synapse Analytics 14.Ê Azure Databricks 15.Ê Azure Analysis Services 16.Ê Power BI 17.Ê Azure Machine Learning 18.Ê Sample Architectures and synergies - Real-Time and Batch 19.Ê Azure Data Catalog 20.Ê Azure Active Directory 21.Ê Azure Webapps 22.Ê Power apps 23.Ê Time Series Insights 24.Ê Azure Cognitive Services 25.Ê Azure Logicapps 26.Ê Azure VM 27.Ê Azure Functions 28.Ê Azure Containers 29.Ê Azure KubernetesÊ Service 30.Ê Use Case 1 31.Ê Use Case 2
| Author | : George Mount |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 238 |
| Release | : 2024-04-26 |
| Genre | : Computers |
| ISBN | : 1098148789 |
If you haven't modernized your data cleaning and reporting processes in Microsoft Excel, you're missing out on big productivity gains. And if you're looking to conduct rigorous data analysis, more can be done in Excel than you think. This practical book serves as an introduction to the modern Excel suite of features along with other powerful tools for analytics. George Mount of Stringfest Analytics shows business analysts, data analysts, and business intelligence specialists how to make bigger gains right from your spreadsheets by using Excel's latest features. You'll learn how to build repeatable data cleaning workflows with Power Query, and design relational data models straight from your workbook with Power Pivot. You'll also explore other exciting new features for analytics, such as dynamic array functions, AI-powered insights, and Python integration. Learn how to build reports and analyses that were previously difficult or impossible to do in Excel. This book shows you how to: Build repeatable data cleaning processes for Excel with Power Query Create relational data models and analysis measures with Power Pivot Pull data quickly with dynamic arrays Use AI to uncover patterns and trends from inside Excel Integrate Python functionality with Excel for automated analysis and reporting
| Author | : Yifan Zhao |
| Publisher | : Elsevier |
| Total Pages | : 261 |
| Release | : 2023-05-24 |
| Genre | : Technology & Engineering |
| ISBN | : 0443189986 |
Human-Machine Interaction for Automated Vehicles: Driver Status Monitoring and the Takeover Process explains how to design an intelligent human-machine interface by characterizing driver behavior before and during the takeover process. Multiple solutions are presented to accommodate different sensing technologies, driving environments and driving styles. Depending on the availability and location of the camera, the recognition of driving and non-driving tasks can be based on eye gaze, head movement, hand gesture or a combination. Technical solutions to recognize drivers various behaviors in adaptive automated driving are described with associated implications to the driving quality. Finally, cutting-edge insights to improve the human-machine-interface design for safety and driving efficiency are also provided, based on the use of this sensing capability to measure drivers' cognition capability. - Covers everything needed to design an effective driver monitoring system, including sensors, areas to monitor, computing devices, and data analysis algorithms - Explores aspects of driver behavior that should be considered when designing an intelligent HMI - Examines the L3 take-over process in detail
| Author | : Gennady Verkhivker |
| Publisher | : Frontiers Media SA |
| Total Pages | : 129 |
| Release | : 2019-10-21 |
| Genre | : |
| ISBN | : 2889631362 |
Machine learning methods such as neural networks, non-linear dimensionality reduction techniques, random forests and others meet in this research topic with biomolecular simulations. The authors of eight articles applied these methods to analyze simulation results, accelerate simulations or to make molecular mechanics force fields more accurate.
| Author | : Cheng Few Lee |
| Publisher | : World Scientific |
| Total Pages | : 1001 |
| Release | : 2023-10-16 |
| Genre | : Business & Economics |
| ISBN | : 9811280282 |
Futures and Options are concerned with the valuation of derivatives and their application to hedging and speculating investments. This book contains 22 chapters and is divided into five parts. Part I contains an overview including a general introduction as well as an introduction to futures, options, swaps, and valuation theories. Part II: Forwards and Futures discusses futures valuation, the futures market, hedging strategies, and various types of futures. Part III: Option Theories and Applications includes both the basic and advanced valuation of options and option strategies in addition to index and currency options. Part IV: Advanced Analyses of Options takes a look at higher level strategies used to quantitatively approach the analysis of options. Part V: Special Topics of Options and Futures covers the applications of more obscure and alternative methods in derivatives as well as the derivation of the Black-Scholes Option Pricing Model.This book applies an active interdisciplinary approach to presenting the material; in other words, three projects involving the use of real-world financial data on derivative, in addition to homework assignments, are made available for students in this book.
| Author | : Martin Ebers |
| Publisher | : Springer Nature |
| Total Pages | : 255 |
| Release | : 2023-01-14 |
| Genre | : Law |
| ISBN | : 3031196678 |
This book gives a comprehensive overview of the state of Artificial Intelligence (AI), especially machine learning (ML) applications in public service delivery in Estonia, discussing the manifold ethical and legal issues that arise under both European and Estonian law. Final conclusions and recommendations set out and analyze various policy options for the public sector, taking into account recent developments at the European level – such as the AIA proposal – as well as the experience of countries that have issued principles and guidelines or even laws for the use of ML in the public sector. “For two reasons, this study is relevant not only for an audience which is interested in Estonian administrative law. First, the authors base their legal analysis primarily on EU law and provide a state of the art-analysis of the relevant secondary legislation. This makes the book a reference text for the European debate on public sector AI governance. Second, this study is part of a larger research project in which four specific use cases of public sector AI have been developed and tested. The practical insights gained in these projects have provided the authors with an excellent understanding of the opportunities and risks of the technology, which distinguishes this legal analysis from similar enterprises.” Excerpt from the foreword by Professor Thomas Wischmeyer (University of Bielefeld)
