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Data Analysis and Machine Learning in Excel is a three-day, foundation-level, hands-on course that explores this fast-changing field and how experienced Excel users can leverage their skills to contribute.

PRIVATE
Course Access

Unlimited Duration

Last Updated

March 3, 2021

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Certification

This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. In this course you will learn about:

· 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

Course Curriculum

    • Technical requirements 00:00:00
    • Understanding learning and models 00:00:00
    • Focusing on model features 00:00:00
    • Studying machine learning models in practice 00:00:00
    • Comparing underfitting and overfitting 00:00:00
    • Evaluating models 00:00:00
    • Technical requirements 00:00:00
    • Understanding supervised learning with multiple linear regression 00:00:00
    • Understanding supervised learning with decision trees 00:00:00
    • Understanding unsupervised learning with clustering 00:00:00
    • Technical requirements 00:00:00
    • Importing data from a text file 00:00:00
    • Importing data from another Excel workbook 00:00:00
    • Importing data from a web page 00:00:00
    • Importing data from Facebook 00:00:00
    • Importing data from a JSON file 00:00:00
    • Importing data from a database 00:00:00
    • Technical requirements 00:00:00
    • Cleansing data 00:00:00
    • Visualizing data for preliminary analysis 00:00:00
    • Understanding unbalanced datasets 00:00:00
    • Technical requirements 00:00:00
    • Building a scatter diagram 00:00:00
    • Calculating the covariance 00:00:00
    • Calculating the Pearson’s coefficient of correlation 00:00:00
    • Studying the Spearman’s correlation 00:00:00
    • Understanding least squares 00:00:00
    • Focusing on feature selection 00:00:00
    • Technical requirements 00:00:00
    • Learning by example – Market Basket Analysis 00:00:00
    • Learning by example – Customer Cohort Analysis 00:00:00
    • Technical requirements 00:00:00
    • Modeling and visualizing time series 00:00:00
    • Forecasting time series automatically in Excel 00:00:00
    • Studying the stationarity of a time series 00:00:00
    • Technical requirements 00:00:00
    • Showing basic comparisons and relationships between variables 00:00:00
    • Building data distributions using histograms 00:00:00
    • Representing geographical distribution of data in maps 00:00:00
    • Showing data that changes over time 00:00:00
    • Technical requirements 00:00:00
    • Introducing the perceptron – the simplest type of neural network 00:00:00
    • Building a deep network 00:00:00
    • Understanding the backpropagation algorithm 00:00:00
    • Technical requirements 00:00:00
    • Introducing the Azure Cloud 00:00:00
    • Using AMLS for free – a step-by-step guide 00:00:00
    • Loading your data into AMLS 00:00:00
    • Creating and running an experiment in AMLS 00:00:00
    • Automatic data analysis flows 00:00:00
    • Automated machine learning 00:00:00

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