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Machine Learning using Excel - Zero Programming needed
3.9 (6 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
34 students enrolled

Machine Learning using Excel - Zero Programming needed

Machine Learning from Ground up
3.9 (6 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
34 students enrolled
Created by Puneet Mathur
Last updated 12/2019
English
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Machine Learning using Excel - Zero Programming needed
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This course includes
  • 3.5 hours on-demand video
  • 7 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Machine learning using Excel
  • Basics of Statistics
  • Machine Learning framework
Requirements
  • Microsoft Excel 2010 or above
  • Basics of Statistics
Description

You will learn the Basics of Statistics, Types of Analytics, Machine Learning 7 Steps Lifecycle Framework, and practical working demonstrations by the instructor using Microsoft Excel to do advanced calculations in machine learning.

You get 7 Machine Learning Excel templates to use in your professional work as a project management professional.

This is a practical hands-on course covering some theories on Statistics, Analytics, and Machine Learning.

Who this course is for:
  • Beginner Machine Learning and Data Science
  • Beginner Professionals who want to know about Machine Learning but do not want to code or program
Course content
Expand all 44 lectures 03:40:54
+ – Introduction to the Course
7 lectures 09:17

Introduction to the Instructor and his background.

Introduction to the Instructor
Preview 00:49

Learn if you need to know to programming for machine learning.

Do you need to know programming for Machine Learning?
Preview 01:15

Learn what is the purpose of the course.

Purpose of the course
Preview 01:08

Learn what are the limitations of using excel for machine learning.

Limitations of using Excel for Machine Learning
Preview 02:23

Understand from the instructor if this course is for you.

Is this course for you?
Preview 01:35

The instructor explains what are the prerequisites for taking this course.

Prerequisites for the course
Preview 01:28

The instructor explains how this course is structured.

How the course is structured
Preview 00:39
+ – Basics of Datascience and Machine Learning
5 lectures 23:40

In this section, the Instructor explains what is analytics and datascience in detail.

What is Analytics & Datascience
Preview 03:03

The instructor explains what is machine learning in detail.

What is Machine Learning
05:18

In this lecture, the instructor explains the types of machine learning and how they are used practically.

Types of Machine Learning (Supervised & Unsupervised)
05:51

The instructor explains what is artificial intelligence.

What is Artificial Intelligence
06:20
How it all ties up!
03:08
+ – Common Definitions & Postulates of Datascience
8 lectures 27:42

In this lecture, the instructor discusses some of the common definitions of data science.

Common Definitions of Datascience 1
02:35

In this lecture, the instructor discusses some of the common definitions of data science.

Common Definitions of Datascience 2
04:07

In this lecture, the instructor discusses some of the common definitions of data science.

Common Definitions of Datascience 3
03:57

In this lecture, the instructor discusses some of the common definitions of data science.

Common Definitions of Datascience 4
01:56

In this lecture, the instructor discusses some of the common definitions of data science.

Common Definitions of Datascience 5
02:24

Check on your learning so far by answering this simple quiz.

Check your understanding
1 question

In this lecture, the instructor discusses the uses of machine learning in the practical world.

Machine Learning Uses
07:19

The instructor discussed the basic postulates from data science that govern machine learning.

Basic Postulates of Machine Learning
02:20

In this lecture, the instructor discusses what are the types of data used in the business world.

Types of Data in the business world
03:04
Check your understanding
1 question
+ – Basics of Statistics
13 lectures 01:48:31

The instructor introduces the student to this section of the Basics of Statistics. What it covers and what it does not cover.

Introduction to the section
03:59

The instructor introduces to the Reference website which the student can use for studying further on the concepts of data science, machine learning, and statistics.

Reference website
01:06

The student learns in this lecture on the types of common variables used in machine learning.

Types of variables
03:39

The instructor introduces the student to the concept of Five Point Summary.

What is Five points summary (Five Numbers Summary)
03:42

Here in this lecture, the student learns about the method to calculate Five Point Summary or Five Numbers Summary calculation using Excel software.

Calculating Five Points summary in excel
08:08

You learn about what is skewness and how to understand it in any dataset.

What is Skewness
06:24

You learn about what is kurtosis and how to understand it in any dataset.

What is Kurtosis
02:45

You learn how to calculate skewness and kurtosis in excel.

Calculating Skewness & Kurtosis in excel
03:26

You learn how to calculate Boxplots in excel.

Calculating Boxplots in excel
07:26

You learn with a practical demonstration on  how to calculate Linear regression in excel in this lecture

Calculating Linear Regression in Excel
14:05

You learn with a practical demonstration on how to calculate Logistic regression in excel in this lecture

Calculating Logistic Regression in excel
25:29

You learn with a practical demonstration on how to calculate Support Vector Machine in excel in this lecture

Calculating Support Vector Machine in excel
16:58

You learn with a practical demonstration on how to calculate Random Forests in excel in this lecture

Calculating Random Forests in excel
11:24
+ – The Machine Learning Development lifecycle
10 lectures 51:19

You learn about what is the Seven step process.

The Seven Step framework from Puneet Mathur
03:15

You learn about the 1st step in machine learning implementation.

Step 1 in Machine Learning implementation
01:08

You learn about the 2nd step in machine learning implementation.

Step 2 in Machine Learning implementation
06:35

You learn about the 3rd step in machine learning implementation.

Step 3 in Machine Learning implementation
12:35

You learn about the 4th step in machine learning implementation.

Step 4 in Machine Learning implementation
06:35

You learn about the 5th step in machine learning implementation.

Step 5 in Machine Learning implementation
05:38

You learn about the 5th step in machine learning implementation.

Step 5 in Machine Learning Implementation continued
03:07

You learn about the 5th step in machine learning implementation.

Step 5 in Machine Learning Implementation further continued
07:33

You learn about the 6th step in machine learning implementation.

Step 6 in Machine Learning implementation
03:16

You learn about the 7th step in machine learning implementation.

Step 7 in Machine Learning implementation
01:37
Check your understanding
1 question