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Hands-On Machine Learning with Microsoft Excel 2019

Author : Julio Cesar Rodriguez Martino

Publisher : Packt Publishing Ltd

Page : 243 pages

File Size : 37,34 MB

Release : 2019-04-30

Category : Computers

ISBN : 178934512X

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Book Description

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

Author : Shmuel Oluwa

Publisher : Packt Publishing Ltd

Page : 280 pages

File Size : 28,31 MB

Release : 2019-07-11

Category : Computers

ISBN : 1789531632

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Book Description

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.



Data Forecasting and Segmentation Using Microsoft Excel

Author : Fernando Roque

Publisher : Packt Publishing Ltd

Page : 325 pages

File Size : 30,28 MB

Release : 2022-05-27

Category : Computers

ISBN : 1803235268

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Book Description

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.



Handbook of Research on Acquiring 21st Century Literacy Skills Through Game-Based Learning

Author : Lane, Carol-Ann

Publisher : IGI Global

Page : 958 pages

File Size : 47,47 MB

Release : 2022-01-07

Category : Language Arts & Disciplines

ISBN : 1799872734

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Book Description

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.



Hands-On Machine Learning with IBM Watson

Author : James D. Miller

Publisher : Packt Publishing Ltd

Page : 277 pages

File Size : 24,6 MB

Release : 2019-03-29

Category : Computers

ISBN : 1789616271

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Book Description

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.



Hands-On Data Science for Marketing

Author : Yoon Hyup Hwang

Publisher : Packt Publishing Ltd

Page : 448 pages

File Size : 31,96 MB

Release : 2019-03-29

Category : Computers

ISBN : 178934882X

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Book Description

Optimize your marketing strategies through analytics and machine learning Key FeaturesUnderstand how data science drives successful marketing campaignsUse machine learning for better customer engagement, retention, and product recommendationsExtract insights from your data to optimize marketing strategies and increase profitabilityBook Description Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and failures of marketing campaigns. This book is a comprehensive guide to help you understand and predict customer behaviors and create more effectively targeted and personalized marketing strategies. This is a practical guide to performing simple-to-advanced tasks, to extract hidden insights from the data and use them to make smart business decisions. You will understand what drives sales and increases customer engagements for your products. You will learn to implement machine learning to forecast which customers are more likely to engage with the products and have high lifetime value. This book will also show you how to use machine learning techniques to understand different customer segments and recommend the right products for each customer. Apart from learning to gain insights into consumer behavior using exploratory analysis, you will also learn the concept of A/B testing and implement it using Python and R. By the end of this book, you will be experienced enough with various data science and machine learning techniques to run and manage successful marketing campaigns for your business. What you will learnLearn how to compute and visualize marketing KPIs in Python and RMaster what drives successful marketing campaigns with data scienceUse machine learning to predict customer engagement and lifetime valueMake product recommendations that customers are most likely to buyLearn how to use A/B testing for better marketing decision makingImplement machine learning to understand different customer segmentsWho this book is for If you are a marketing professional, data scientist, engineer, or a student keen to learn how to apply data science to marketing, this book is what you need! It will be beneficial to have some basic knowledge of either Python or R to work through the examples. This book will also be beneficial for beginners as it covers basic-to-advanced data science concepts and applications in marketing with real-life examples.



Machine Learning with R

Author : Brett Lantz

Publisher : Packt Publishing Ltd

Page : 459 pages

File Size : 37,52 MB

Release : 2019-04-15

Category : Computers

ISBN : 1788291557

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Book Description

Solve real-world data problems with R and machine learning Key Features Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett Lantz Book Description Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R. What you will learn Discover the origins of machine learning and how exactly a computer learns by example Prepare your data for machine learning work with the R programming language Classify important outcomes using nearest neighbor and Bayesian methods Predict future events using decision trees, rules, and support vector machines Forecast numeric data and estimate financial values using regression methods Model complex processes with artificial neural networks — the basis of deep learning Avoid bias in machine learning models Evaluate your models and improve their performance Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow Who this book is for Data scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.



Hands-On Artificial Intelligence for IoT

Author : Amita Kapoor

Publisher : Packt Publishing Ltd

Page : 382 pages

File Size : 22,41 MB

Release : 2019-01-31

Category : Computers

ISBN : 1788832760

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Book Description

Build smarter systems by combining artificial intelligence and the Internet of Things—two of the most talked about topics today Key FeaturesLeverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT dataProcess IoT data and predict outcomes in real time to build smart IoT modelsCover practical case studies on industrial IoT, smart cities, and home automationBook Description There are many applications that use data science and analytics to gain insights from terabytes of data. These apps, however, do not address the challenge of continually discovering patterns for IoT data. In Hands-On Artificial Intelligence for IoT, we cover various aspects of artificial intelligence (AI) and its implementation to make your IoT solutions smarter. This book starts by covering the process of gathering and preprocessing IoT data gathered from distributed sources. You will learn different AI techniques such as machine learning, deep learning, reinforcement learning, and natural language processing to build smart IoT systems. You will also leverage the power of AI to handle real-time data coming from wearable devices. As you progress through the book, techniques for building models that work with different kinds of data generated and consumed by IoT devices such as time series, images, and audio will be covered. Useful case studies on four major application areas of IoT solutions are a key focal point of this book. In the concluding chapters, you will leverage the power of widely used Python libraries, TensorFlow and Keras, to build different kinds of smart AI models. By the end of this book, you will be able to build smart AI-powered IoT apps with confidence. What you will learnApply different AI techniques including machine learning and deep learning using TensorFlow and KerasAccess and process data from various distributed sourcesPerform supervised and unsupervised machine learning for IoT dataImplement distributed processing of IoT data over Apache Spark using the MLLib and H2O.ai platformsForecast time-series data using deep learning methodsImplementing AI from case studies in Personal IoT, Industrial IoT, and Smart CitiesGain unique insights from data obtained from wearable devices and smart devicesWho this book is for If you are a data science professional or a machine learning developer looking to build smart systems for IoT, Hands-On Artificial Intelligence for IoT is for you. If you want to learn how popular artificial intelligence (AI) techniques can be used in the Internet of Things domain, this book will also be of benefit. A basic understanding of machine learning concepts will be required to get the best out of this book.



Introduction to Algorithms for Data Mining and Machine Learning

Author : Xin-She Yang

Publisher : Academic Press

Page : 188 pages

File Size : 33,70 MB

Release : 2019-06-19

Category : Mathematics

ISBN : 0128172169

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Book Description

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.



Modern Data Analytics in Excel

Author : George Mount

Publisher : "O'Reilly Media, Inc."

Page : 238 pages

File Size : 28,79 MB

Release : 2024-04-26

Category : Computers

ISBN : 1098148789

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Book Description

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