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Course Skill Level:

Foundational to Intermediate

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with basic IT, Linux, and machine learning with Excel experience

Who should attend & recommended skills

  • This course is geared for attendees who seek advanced use cases using automated machine learning and artificial neural network, which simplifies the analysis task and represents the future of machine learning and is 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.
  • Skill-level: Foundation-level Machine Learning with Microsoft Excel 2019 skills for Intermediate skilled team members. This is not a basic class.
  • IT Skills: Basic to Intermediate (1-5 years’ experience)
  • Machine Learning with Microsoft Excel 2019: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning with Microsoft Excel 2019 expert instructor, students will learn about and explore:
  • Using Microsoft’s product Excel to build advanced forecasting models using varied examples
  • Range of machine learning tasks such as data mining, data analytics, smart visualization, and more
  • Deriving data-driven techniques using Excel plugins and APIs without much code required
  • Using Excel to preview and cleanse datasets
  • Correlations between variables and optimize the input to machine learning models
  • Using and evaluating different machine learning models from Excel
  • The use of different visualizations
  • Basic concepts and calculations to understand how artificial neural networks work
  • How to connect Excel to the Microsoft Azure cloud
  • Getting beyond proof of concepts and build fully functional data analysis flows

Course breakdown / modules

  • Technical requirements
  • Understanding learning and models
  • Focusing on model features
  • Studying machine learning models in practice
  • Comparing underfitting and overfitting
  • Evaluating models

  • Technical requirements
  • Understanding supervised learning with multiple linear regression
  • Understanding supervised learning with decision trees
  • Understanding unsupervised learning with clustering

  • 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

  • Technical requirements
  • Cleansing data
  • Visualizing data for preliminary analysis
  • Understanding unbalanced datasets

  • 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

  • Technical requirements
  • Learning by example – Market Basket Analysis
  • Learning by example – Customer Cohort Analysis

  • Technical requirements
  • Modeling and visualizing time series
  • Forecasting time series automatically in Excel
  • Studying the stationarity of a time series

  • 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

  • Technical requirements
  • Introducing the perceptron – the simplest type of neural network
  • Building a deep network
  • Understanding the backpropagation algorithm

  • 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

  • Automatic data analysis flows
  • Automated machine learning