Being-Creative-with-AI/C2/Machine-Learning-and-Deep-Learning/English - Revision history

Bellatony911 at 11:01, 23 May 2026

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Misbah at 14:20, 22 May 2026

2026-05-22T14:20:18Z

Bellatony911 at 11:53, 22 May 2026

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Bellatony911 at 11:48, 22 May 2026

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Misbah at 11:22, 22 May 2026

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Misbah at 11:17, 22 May 2026

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Misbah at 11:16, 22 May 2026

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Misbah: Created page with "{|border=1 |- || '''Visual Cue''' | '''Narration''' |- || Slide 1 Title slide Machine Learning and Deep Learning | Welcome to this '''Spoken Tutorial''' on '''Machine L..."

2026-05-22T11:08:32Z

Created page with "{|border=1 |- || '''Visual Cue''' | '''Narration''' |- || Slide 1 Title slide Machine Learning and Deep Learning | Welcome to this '''Spoken Tutorial''' on '''Machine L..."

New page

{|border=1

|-

|| '''Visual Cue'''

| '''Narration'''

|-

|| Slide 1
Title slide

Machine Learning and Deep Learning

| Welcome to this '''Spoken Tutorial''' on '''Machine Learning and Deep Learning'''.

|-

|| Slide 2

Learning Objectives

Montage of AI tools from previous tutorial (YouTube, Maps, Photos)

| In this '''tutorial''', we will learn

About '''Rule-based Programming''', '''Machine Learning''' and '''Deep Learning''', and

How '''Machine Learning''' and '''Deep Learning''' are connected to '''AI'''.

|-

|| Disclaimer Content Slide

| As AI tools constantly evolve, if you are unable to locate any icon or encounter difficulty at any step, you may use any conversational AI Chatbot for guidance.

|-

|| Slide 3
System Requirements

| For this '''tutorial''', you will need:

A '''computer''', a '''laptop''', or a '''smartphone'''.

A stable '''internet''' connection.

An updated web browser, such as '''Google Chrome''', '''Microsoft Edge''', or '''Mozilla Firefox'''.

You do '''not''' need coding or technical skills.

|-

|| Slide 4
Pre-requisite
http://EduPyramids.org

| For the Pre-requisites of this tutorial, visit the website shown on your screen.

|-

|| Image/animation: Show Animated gears or code flow

| Let's start with understanding '''rule-based programming''' using a real-life example.

|-

|| Image/animation: Automatic water dispenser with sensor

| Think about the time you used an automatic water tap.

You place your hand under a water tap.

The '''sensor''' in the tap detects your hand.

Then, it automatically turns on the water tap.

The water starts flowing.

|-

|| Image/animation: Close-up of tap sensor

Zoom in on sensor area; show "Detection = green light"

| The '''sensor''' follows one rule.

"'''IF''' a hand is detected under the tap, '''THEN''' turn on the water tap."

|-

|| Image/animation: large vessel placed under the tap

Zoom in on sensor area; show "No Detection"

| Now, think about what would happen if we placed a large vessel under the tap?

The '''sensor''' in the tap will not detect it.

This happens because '''rules''' can't handle new cases.

They have limitations.

They can only follow the exact instructions we give them.

|-

|| Image/animation: Simple flowchart: Hand detected → Tap on → Water flows

| This is '''rule-based programming'''.

The '''machine''' simply follows the exact set of instructions given to it.

|-

|| Image/animation: "Machine Learning"

| Now, let's discuss '''Machine Learning''', commonly referred to as '''ML'''.

|-

|| Image/animation: Email inbox with spam and regular mails

| In the '''email inbox''' shown here, do you see a '''folder''' labelled '''Spam'''?

|-

|| Image/animation: Show Spam folder of an email

"Data → ML Model → Prediction"

Highlight the suspicious words, links, or senders.

Image/animation: Highlight spam mails vs regular mails (side by side)

Highlight "model" in the diagram

| Your unwanted '''email''' automatically goes into this '''folder'''.

This sorting happens using '''Machine Learning'''.

'''ML''' is trained using thousands of '''spam''' and regular '''emails'''.

From these examples, the '''machine''' learns patterns of what '''spam''' looks like.

It checks for things like suspicious words, strange '''links''', and unknown senders.

Once trained, it can predict whether a new '''email''' is '''spam''' or not.

|-

|| Pause icon overlay

| Pause the '''tutorial''' now.

Open the '''Spam folder''' in your '''email'''.

Look at the '''emails''' in this '''folder'''.

They have been separated from your '''inbox''' using '''Machine Learning'''.

|-

|| Transition slide: Deep Learning

| Now, let's go one step deeper into '''Machine Learning''' and understand '''Deep Learning'''.

'''Deep Learning''' is commonly referred to as '''DL'''.

|-

|| Visual: Voice assistant (e.g. Google Assistant)

what is the time in India now

Show the response

| Let's open '''Google Chrome'''.

Click on the '''mic icon''' in the '''search bar''' to ask a question in '''voice mode'''.

Then ask, "What's the time in India now?"

It understands and processes the voice '''input''' and gives an answer.

This process happens with the help of '''Deep Learning'''.

|-

|| Visual: Illustration of neural network (dots and lines)

Animation: Small dots connected by lines

Small dots lighting up like decision nodes

Arrows between nodes passing signals

Layers lighting up one after another

"Many Layers = Deep Learning"

| Do you know how '''deep learning''' gave the answer?

'''Deep Learning''' uses '''artificial neural networks'''.

These '''networks''' have many layers that work together.

You can think of them as many tiny helpers.

Each helper understands a small part of the voice.

They pass this information from one layer to the next.

As information travels through the layers, the understanding becomes clearer.

Finally, the '''computer''' understands your question and gives the right answer.

|-

|| Visual: Layers lighting up one after another

"Many Layers = Deep Learning"

Show recognizing faces or understanding speech by computer

| As there are many layers, we refer to it as "'''deep'''."

Each layer helps improve the understanding a little more, like '''building blocks'''.

Hence, '''Deep Learning''' is used for complex tasks.

For example, in understanding speech or recognizing images.

|-

|| Visual: Russian doll animation: AI (outer) → ML (inner) → DL (innermost)

| Imagine '''AI''', '''ML''', and '''DL''' like Russian dolls.

One doll inside the other.

First comes '''AI''', the biggest doll.

Inside '''AI''' is '''ML''', and inside ML is '''DL'''.

|-

|| Visual: Highlight outer doll: AI

| '''AI''' is a broad field that makes '''machines''' intelligent.

It's like the outer shell that includes many different methods.

|-

|| Visual: Highlight middle doll: ML

| '''ML''' is a '''subset''' of '''AI'''.

It learns from '''data'''.

It's a smaller doll inside '''AI'''.

|-

|| Visual: Highlight inner doll: DL

| '''Deep Learning''' is a '''subset''' of '''ML'''.

It is nested inside '''ML'''.

It learns from large amounts of '''data''' using '''artificial neural networks'''.

This lets it handle complex tasks.

|-

|| Summary slide:
Bulleted List

| Let's summarize what we learnt:

'''Rule-based programming''' follows fixed steps.

'''Machine Learning''' learns patterns from data.

'''Deep Learning''' uses artificial neural networks with many layers.

'''ML''' and '''DL''' are parts of '''AI'''.

|-

|| Assignment slide
Think of an '''application''' from your daily life that uses fixed rules.

Next, think of an '''application''' that learns from data or uses deep '''learning'''.

Write down both examples and explain why each one fits its category.

| Now, here's a quick assignment for you.

-

|| Acknowledgement slides

| This brings us to the end of this tutorial.

Thank you for watching!

|| Disclaimer slide

|

|-

|| EduPyramids logo
This Spoken Tutorial is brought to you by

'''EduPyramids Educational Services Private Limited''', SINE, IIT Bombay

|

|-

|}

@@ Line 310: / Line 310: @@
 | Thank you for joining.
 |-
-|| Disclaimer slide
+|| Final slide
-−
-−
-−
-|-
 This Spoken Tutorial is brought to you by

@@ Line 168: / Line 168: @@
 || Visual: Voice assistant (e.g. Google Assistant)
-what is the time in India now
+what is the time in  '''India''' now
 Show the response
@@ Line 176: / Line 176: @@
 Click on the '''mic icon''' in the '''search bar''' to ask a question in '''voice mode'''.
-Then ask, "What's the time in India now?"
+Then ask, "What's the time in '''India''' now?"
 It understands and processes the voice '''input''' and gives an answer.

@@ Line 41: / Line 41: @@
 System Requirements
-| For this '''tutorial''', you will need:
+| Here I am using the following setup.
-A '''computer''', a '''laptop''', or a '''smartphone'''.
+You do not need coding or techincal skills.
-−
-A stable '''internet''' connection.
-−
-An updated web browser, such as '''Google Chrome''', '''Microsoft Edge''', or '''Mozilla Firefox'''.
-−
-You do not need coding or technical skills.
 |-

@@ Line 49: / Line 49: @@
 An updated web browser, such as '''Google Chrome''', '''Microsoft Edge''', or '''Mozilla Firefox'''.
-You do '''not''' need coding or technical skills.
+You do not need coding or technical skills.
 |-
@@ Line 55: / Line 55: @@
 || Slide 4
 Pre-requisite
 http://EduPyramids.org
@@ Line 186: / Line 187: @@
 This process happens with the help of '''Deep Learning'''.
 |-
@@ Line 201: / Line 207: @@
 "Many Layers = Deep Learning"
-| Do you know how '''deep learning''' gave the answer?
+| '''Deep Learning''' uses '''artificial neural networks'''.
-−
 '''Deep Learning''' uses '''artificial neural networks'''.
 These '''networks''' have many layers that work together.

@@ Line 87: / Line 87: @@
 | The '''sensor''' follows one rule.
-"'''IF''' a hand is detected under the tap, '''THEN''' turn on the water tap."
+"If a hand is detected under the tap, then turn on the water tap."
 |-
@@ Line 237: / Line 237: @@
 || Visual: Russian doll animation: AI (outer) → ML (inner) → DL (innermost)
-| Imagine '''AI''', '''ML''', and '''DL''' like Russian dolls.
+| Imagine '''AI''', '''ML''', and '''DL''' like ''' Russian'''  dolls.
 One doll inside the other.
@@ Line 243: / Line 243: @@
 First comes '''AI''', the biggest doll.
-Inside '''AI''' is '''ML''', and inside ML is '''DL'''.
+Inside '''AI''' is '''ML''', and inside ''' ML''' is '''DL'''.
 |-

@@ Line 26: / Line 26: @@
 | In this '''tutorial''', we will learn
-About '''Rule-based Programming''', '''Machine Learning''' and '''Deep Learning''', and
+About '''Rule-based Programming''', '''Machine Learning''' and '''Deep Learning''',
 How '''Machine Learning''' and '''Deep Learning''' are connected to '''AI'''.

@@ Line 278: / Line 278: @@
 || Summary slide:
 Bulleted List
-| Let's summarize what we learnt:
+| Let's quickly summarize what we learnt in this tutorial.
 '''Rule-based programming''' follows fixed steps.
@@ Line 293: / Line 296: @@
 || Assignment slide
 Think of an '''application''' from your daily life that uses fixed rules.
@@ Line 301: / Line 305: @@
 | Now, here's a quick assignment for you.
+|-
 || Acknowledgement slides
-| This brings us to the end of this tutorial.
+| Thank you for joining.
-Thank you for watching!
+|-
 || Disclaimer slide