Hands-On Machine Learning with Microsoft Excel 2019 PDF Download

Author: Julio Cesar Rodriguez Martino
Publisher: Packt Publishing Ltd
ISBN: 178934512X
Category : Computers
Languages : en
Pages : 243

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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.

Author: Shmuel Oluwa
Publisher: Packt Publishing Ltd
ISBN: 1789531632
Category : Computers
Languages : en
Pages : 280

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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.

Author: Fernando Roque
Publisher: Packt Publishing Ltd
ISBN: 1803235268
Category : Computers
Languages : en
Pages : 325

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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.

Author: Lane, Carol-Ann
Publisher: IGI Global
ISBN: 1799872734
Category : Language Arts & Disciplines
Languages : en
Pages : 958

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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.

Author: Hong Zhou
Publisher: Apress
ISBN: 1484259823
Category : Computers
Languages : en
Pages : 223

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Book Description
Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help. Excel allows you to work with data in a transparent manner. When you open an Excel file, data is visible immediately and you can work with it directly. Intermediate results can be examined while you are conducting your mining task, offering a deeper understanding of how data is manipulated and results are obtained. These are critical aspects of the model construction process that are hidden in software tools and programming language packages. This book teaches you data mining through Excel. You will learn how Excel has an advantage in data mining when the data sets are not too large. It can give you a visual representation of data mining, building confidence in your results. You will go through every step manually, which offers not only an active learning experience, but teaches you how the mining process works and how to find the internal hidden patterns inside the data. What You Will Learn Comprehend data mining using a visual step-by-step approachBuild on a theoretical introduction of a data mining method, followed by an Excel implementationUnveil the mystery behind machine learning algorithms, making a complex topic accessible to everyoneBecome skilled in creative uses of Excel formulas and functionsObtain hands-on experience with data mining and Excel Who This Book Is For Anyone who is interested in learning data mining or machine learning, especially data science visual learners and people skilled in Excel, who would like to explore data science topics and/or expand their Excel skills. A basic or beginner level understanding of Excel is recommended.

Author: Wayne Winston
Publisher: Microsoft Press
ISBN: 1509306080
Category : Computers
Languages : en
Pages : 1488

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Book Description
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

Author: Christoph Molnar
Publisher: Lulu.com
ISBN: 0244768528
Category : Artificial intelligence
Languages : en
Pages : 320

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Book Description
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Author: Hong Zhou
Publisher: Apress
ISBN: 9781484297704
Category : Computers
Languages : en
Pages : 0

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Book Description
Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Most software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help, and this book will show you exactly how. This updated edition demonstrates how to work with data in a transparent manner using Excel. When you open an Excel file, data is visible immediately and you can work with it directly. You’ll see how to examine intermediate results even as you are still conducting your mining task, offering a deeper understanding of how data is manipulated, and results are obtained. These are critical aspects of the model construction process that are often hidden in software tools and programming language packages. Over the course of Learn Data Mining Through Excel, you will learn the data mining advantages the application offers when the data sets are not too large. You’ll see how to use Excel’s built-in features to create visual representations of your data, enabling you to present your findings in an accessible format. Author Hong Zhou walks you through each step, offering not only an active learning experience, but teaching you how the mining process works and how to find hidden patterns within the data. Upon completing this book, you will have a thorough understanding of how to use an application you very likely already have to mine and analyze data, and how to present results in various formats. What You Will Learn Comprehend data mining using a visual step-by-step approach Gain an introduction to the fundamentals of data mining Implement data mining methods in Excel Understand machine learning algorithms Leverage Excel formulas and functions creatively Obtain hands-on experience with data mining and Excel Who This Book Is For Anyone who is interested in learning data mining or machine learning, especially data science visual learners and people skilled in Excel who would like to explore data science topics and/or expand their Excel skills. A basic or beginner level understanding of Excel is recommended.

Author: Yoon Hyup Hwang
Publisher: Packt Publishing Ltd
ISBN: 178934882X
Category : Computers
Languages : en
Pages : 448

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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.

Author: Suresh Kumar Mukhiya
Publisher: Packt Publishing Ltd
ISBN: 178953562X
Category : Computers
Languages : en
Pages : 342

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Book Description
Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas Key FeaturesUnderstand the fundamental concepts of exploratory data analysis using PythonFind missing values in your data and identify the correlation between different variablesPractice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python packageBook Description Exploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization. You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence. By the end of this EDA book, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes. What you will learnImport, clean, and explore data to perform preliminary analysis using powerful Python packagesIdentify and transform erroneous data using different data wrangling techniquesExplore the use of multiple regression to describe non-linear relationshipsDiscover hypothesis testing and explore techniques of time-series analysisUnderstand and interpret results obtained from graphical analysisBuild, train, and optimize predictive models to estimate resultsPerform complex EDA techniques on open source datasetsWho this book is for This EDA book is for anyone interested in data analysis, especially students, statisticians, data analysts, and data scientists. The practical concepts presented in this book can be applied in various disciplines to enhance decision-making processes with data analysis and synthesis. Fundamental knowledge of Python programming and statistical concepts is all you need to get started with this book.