Basics-of-Artificial-Intelligence/C2/Machine-Learning-and-Deep-Learning/English

Spoken Tutorial-AI-2

Introduction to Machine Learning and Deep Learning

Meta Tags: AI, Artificial Intelligence, Machine Learning, Deep Learning, Rule-based Programming, Neural Networks, Spam Filter, Voice Assistant, Spoken Tutorial, EduPyramids

Pre-requisite Tutorial : Introduction to Artificial Intelligence

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.
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 Chrome, Edge, or Firefox. You do not need coding or technical skills.
Image/animation: Show Animated gears or code flow	Let’s start with 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 / Siri) 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 quickly summarize what we learned in this tutorial. We learned that: Rule-based programming follows fixed steps. Machine Learning learns patterns from data. Deep Learning uses 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. (This is your rule-based programming example.) Next, think of another application that learns from data or uses Deep Learning. (Examples include spam filters, image recognition, or voice assistants.) Write down both examples and explain why each one fits its category.	Now, here’s a quick assignment for you. 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.
EduPyramids logo	This Spoken Tutorial is brought to you by EduPyramids Educational Services Private Limited.
Closing slide	Thank you for watching!

Contributors and Content Editors

Madhurig