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Machine Learning with Microsoft Excel 2019

  • Course Code: Artificial Intelligence - Machine Learning with Microsoft Excel 2019
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for those who wants 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.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Machine Learning with Microsoft Excel 2019 skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants 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. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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 course 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 lesson, 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. 

Working in a hands-on learning environment, led by our Machine Learning with Microsoft Excel 2019 expert instructor, students will learn about and explore: 

  • Use 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 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Use Excel to preview and cleanse datasets 
  • Understand correlations between variables and optimize the input to machine learning models 
  • Use and evaluate different machine learning models from Excel 
  • Understand the use of different visualizations 
  • Learn the basic concepts and calculations to understand how artificial neural networks work 
  • Learn how to connect Excel to the Microsoft Azure cloud 
  • Get beyond proof of concepts and build fully functional data analysis flows 

Audience & Pre-Requisites 

This course is geared for attendees with Machine Learning with Microsoft Excel 2019 who wish some advance use cases using Automated Machine Learning, and artificial neural network, which simplifies the analysis task and represents the future of machine learning. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills and Machine Learning with Microsoft Excel 2019 knowledge 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Implementing Machine Learning Algorithms 
  • Technical requirements 
  • Understanding learning and models 
  • Focusing on model features 
  • Studying machine learning models in practice 
  • Comparing underfitting and overfitting 
  • Evaluating models 
  1. Hands-On Examples of Machine Learning Models 
  • Technical requirements 
  • Understanding supervised learning with multiple linear regression 
  • Understanding supervised learning with decision trees 
  • Understanding unsupervised learning with clustering 
  1. Importing Data into Excel from Different Data Sources 
  • Technical requirements 
  • Importing data from a text file 
  • Importing data from another Excel workbook 
  • Importing data from a web page 
  • Importing data from Facebook 
  • Importing data from a JSON file 
  • Importing data from a database 
  1. Data Cleansing and Preliminary Data Analysis 
  • Technical requirements 
  • Cleansing data 
  • Visualizing data for preliminary analysis 
  • Understanding unbalanced datasets 
  1. Correlations and the Importance of Variables 
  • Technical requirements 
  • Building a scatter diagram 
  • Calculating the covariance 
  • Calculating the Pearson’s coefficient of correlation 
  • Studying the Spearman’s correlation 
  • Understanding least squares 
  • Focusing on feature selection 
  1. Data Mining Models in Excel Hands-On Examples 
  • Technical requirements  
  • Learning by example – Market Basket Analysis 
  • Learning by example – Customer Cohort Analysis 
  1. Implementing Time Series 
  • Technical requirements 
  • Modeling and visualizing time series 
  • Forecasting time series automatically in Excel 
  • Studying the stationarity of a time series 
  1. Visualizing Data in Diagrams, Histograms, and Maps 
  • Technical requirements 
  • Showing basic comparisons and relationships between variables 
  • Building data distributions using histograms 
  • Representing geographical distribution of data in maps 
  • Showing data that changes over time 
  1. Artificial Neural Networks 
  • Technical requirements 
  • Introducing the perceptron – the simplest type of neural network 
  • Building a deep network 
  • Understanding the backpropagation algorithm 
  1. Azure and Excel – Machine Learning in the Cloud 
  • Technical requirements 
  • Introducing the Azure Cloud 
  • Using AMLS for free – a step-by-step guide 
  • Loading your data into AMLS 
  • Creating and running an experiment in AMLS 
  1. The Future of Machine Learning 
  • Automatic data analysis flows 
  • Automated machine learning 
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