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Hands On Machine Learning With Microsoft Excel 2019


Hands On Machine Learning With Microsoft Excel 2019
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Hands On Machine Learning With Microsoft Excel 2019


Hands On Machine Learning With Microsoft Excel 2019
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Author : Julio Cesar Rodriguez Martino
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-04-30

Hands On Machine Learning With Microsoft Excel 2019 written by Julio Cesar Rodriguez Martino and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-30 with Computers categories.


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.



Hands On Financial Modeling With Microsoft Excel 2019


Hands On Financial Modeling With Microsoft Excel 2019
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Author : Shmuel Oluwa
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-07-11

Hands On Financial Modeling With Microsoft Excel 2019 written by Shmuel Oluwa and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-11 with Computers categories.


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.



Microsoft Excel 2019 Data Analysis And Business Modeling


Microsoft Excel 2019 Data Analysis And Business Modeling
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Author : Wayne Winston
language : en
Publisher: Microsoft Press
Release Date : 2019-03-28

Microsoft Excel 2019 Data Analysis And Business Modeling written by Wayne Winston and has been published by Microsoft Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-28 with Computers categories.


Master business modeling and analysis techniques with Microsoft Excel 2019 and Office 365 and transform data into bottom-line results. Written by award-winning educator Wayne Winston, this hands-on, scenario-focused guide helps you use Excel to ask the right questions and get accurate, actionable answers. New coverage ranges from Power Query/Get & Transform to Office 365 Geography and Stock data types. Practice with more than 800 problems, many based on actual challenges faced by working analysts. Solve real business problems with Excel—and build your competitive advantage: Quickly transition from Excel basics to sophisticated analytics Use PowerQuery or Get & Transform to connect, combine, and refine data sources Leverage Office 365’s new Geography and Stock data types and six new functions Illuminate insights from geographic and temporal data with 3D Maps Summarize data with pivot tables, descriptive statistics, histograms, and Pareto charts Use Excel trend curves, multiple regression, and exponential smoothing Delve into key financial, statistical, and time functions Master all of Excel’s great charts Quickly create forecasts from historical time-based data Use Solver to optimize product mix, logistics, work schedules, and investments—and even rate sports teams Run Monte Carlo simulations on stock prices and bidding models Learn about basic probability and Bayes’ Theorem Use the Data Model and Power Pivot to effectively build and use relational data sources inside an Excel workbook Automate repetitive analytics tasks by using macros



Data Forecasting And Segmentation Using Microsoft Excel


Data Forecasting And Segmentation Using Microsoft Excel
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Author : Fernando Roque
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-05-27

Data Forecasting And Segmentation Using Microsoft Excel written by Fernando Roque and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-27 with Computers categories.


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.



Deep Learning For Coders With Fastai And Pytorch


Deep Learning For Coders With Fastai And Pytorch
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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29

Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.


Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala



Mos Study Guide For Microsoft Excel Expert Exam Mo 201


Mos Study Guide For Microsoft Excel Expert Exam Mo 201
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Author : Paul McFedries
language : en
Publisher: Microsoft Press
Release Date : 2020-03-06

Mos Study Guide For Microsoft Excel Expert Exam Mo 201 written by Paul McFedries and has been published by Microsoft Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-06 with Computers categories.


Advance your expert-level proficiency with Excel. And earn the credential that proves it! Demonstrate your expert-level competency with Microsoft Excel! Designed to help you practice and prepare for Microsoft Office Specialist: Microsoft Excel Expert (Excel and Excel 2019) certification, this official Study Guide delivers: In-depth preparation for each MOS objective Detailed procedures to help build the skills measured by the exam Hands-on tasks to practice what you’ve learned Ready-made practice files Sharpen the skills measured by these objectives: Manage Workbook Options and Settings Manage and Format Data Create Advanced Formulas and Macros Manage Advanced Charts and Tables About the MOS: Expert Certification A Microsoft Office Specialist (MOS): Expert certification validates your hands-on experience and competency with an Office product at an expert level. It demonstrates that you can apply the product’s principal features at an advanced level, can complete expert tasks independently, and are ready to enter the job market. See full details at: microsoft.com/learn Practice Files Available at: MicrosoftPressStore.com/ MOSExcelExpert201/downloads



Mos Study Guide For Microsoft Excel Exam Mo 200


Mos Study Guide For Microsoft Excel Exam Mo 200
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Author : Joan Lambert
language : en
Publisher: Microsoft Press
Release Date : 2020-03-11

Mos Study Guide For Microsoft Excel Exam Mo 200 written by Joan Lambert and has been published by Microsoft Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-11 with Computers categories.


Advance your everyday proficiency with Excel! And earn the credential that proves it! Demonstrate your expertise with Microsoft Excel! Designed to help you practice and prepare for Microsoft Office Specialist: Excel Associate (Excel and Excel 2019) certification, this official Study Guide delivers: In-depth preparation for each MOS objective Detailed procedures to help build the skills measured by the exam Hands-on tasks to practice what you’ve learned Ready-made practice files Sharpen the skills measured by these objectives: Manage Worksheets and Workbooks Manage Data Cells and Ranges Manage Tables and Table Data Perform Operations by Using Formulas and Functions Manage Charts About MOS A Microsoft Office Specialist (MOS) certification validates your proficiency with Microsoft Office programs, demonstrating that you can meet globally recognized performance standards. Hands-on experience with the technology is required to successfully pass Microsoft Certification exams.



Handbook Of Research On Acquiring 21st Century Literacy Skills Through Game Based Learning


Handbook Of Research On Acquiring 21st Century Literacy Skills Through Game Based Learning
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Author : Lane, Carol-Ann
language : en
Publisher: IGI Global
Release Date : 2022-01-07

Handbook Of Research On Acquiring 21st Century Literacy Skills Through Game Based Learning written by Lane, Carol-Ann and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-07 with Language Arts & Disciplines categories.


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.



The Hundred Page Machine Learning Book


The Hundred Page Machine Learning Book
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Author : Andriy Burkov
language : en
Publisher:
Release Date : 2019

The Hundred Page Machine Learning Book written by Andriy Burkov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Machine learning categories.


Provides a practical guide to get started and execute on machine learning within a few days without necessarily knowing much about machine learning.The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue.



Hands On Machine Learning With Ibm Watson


Hands On Machine Learning With Ibm Watson
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Author : James D. Miller
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-03-29

Hands On Machine Learning With Ibm Watson written by James D. Miller and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-29 with Computers categories.


Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services Key FeaturesImplement data science and machine learning techniques to draw insights from real-world dataUnderstand what IBM Cloud platform can help you to implement cognitive insights within applicationsUnderstand the role of data representation and feature extraction in any machine learning systemBook Description IBM Cloud is a collection of cloud computing services for data analytics using machine learning and artificial intelligence (AI). This book is a complete guide to help you become well versed with machine learning on the IBM Cloud using Python. Hands-On Machine Learning with IBM Watson starts with supervised and unsupervised machine learning concepts, in addition to providing you with an overview of IBM Cloud and Watson Machine Learning. You'll gain insights into running various techniques, such as K-means clustering, K-nearest neighbor (KNN), and time series prediction in IBM Cloud with real-world examples. The book will then help you delve into creating a Spark pipeline in Watson Studio. You will also be guided through deep learning and neural network principles on the IBM Cloud using TensorFlow. With the help of NLP techniques, you can then brush up on building a chatbot. In later chapters, you will cover three powerful case studies, including the facial expression classification platform, the automated classification of lithofacies, and the multi-biometric identity authentication platform, helping you to become well versed with these methodologies. By the end of this book, you will be ready to build efficient machine learning solutions on the IBM Cloud and draw insights from the data at hand using real-world examples. What you will learnUnderstand key characteristics of IBM machine learning servicesRun supervised and unsupervised techniques in the cloudUnderstand how to create a Spark pipeline in Watson StudioImplement deep learning and neural networks on the IBM Cloud with TensorFlowCreate a complete, cloud-based facial expression classification solutionUse biometric traits to build a cloud-based human identification systemWho this book is for This beginner-level book is for data scientists and machine learning engineers who want to get started with IBM Cloud and its machine learning services using practical examples. Basic knowledge of Python and some understanding of machine learning will be useful.