Skip to playerSkip to main contentSkip to footer
  • 6/3/2025
Welcome to Day 3 of DailyAIWizard, your 20-minute journey to mastering AI! In this beginner-friendly lesson, I’m Anastasia, your AI guide, and we’ll explore the differences between Artificial Intelligence, Machine Learning, and Deep Learning. Sophia joins us for a demo using Google’s Teachable Machine to classify cats and dogs, showing Machine Learning in action! Whether you're new to AI or building on Days 1 and 2, this series will guide you from the fundamentals to Python programming. Let’s dive in!

Task of the Day: Try Google’s Teachable Machine yourself—train it to classify something simple (like cats and dogs) and share your results in the comments!

Google Teachable Machine: teachablemachine.withgoogle.com/

Learn More: Visit wisdomacademy.ai for additional resources and to join our AI learning community.
Subscribe for Daily Lessons: Don’t miss Day 4, where we’ll dive into How Machine Learning Works. Hit the bell to stay updated!

#AIForBeginners #MachineLearning #DeepLearning #ArtificialIntelligence #DailyAIWizard

Category

📚
Learning
Transcript
00:00welcome to day three of daily ai wizard your 20-minute journey to mastering ai i'm anastasia
00:08your ai guide here to make learning ai simple and fun today we're comparing three big concepts
00:14ai machine learning and deep learning we'll explore what they are how they're related and
00:19how they differ with some exciting examples along the way let's get started
00:23let's start with ai which we touched on in day one artificial intelligence or ai is when machines
00:33mimic human intelligence it's a broad field that includes abilities like learning reasoning and
00:38problem solving think of ai as the big picture of intelligent systems covering everything from
00:44chatbots like me to self-driving cars ai is the overarching goal of creating machines that can
00:50think and act like humans and it encompasses many techniques including the ones we'll talk about
00:55today machine learning comes in three main types each suited for different tasks first supervised
01:05learning uses labeled data like teaching a child with examples second unsupervised learning works
01:11with unlabeled data finding patterns on its own third reinforcement learning learns through rewards
01:17like training a dog with treats we'll dive deeper into these types in future lessons but for now
01:23know that they're the building blocks of machine learning helping ai systems learn in different ways
01:28next we have deep learning or dl which is a subset of machine learning deep learning uses neural
01:38networks structures inspired by the human brain with many layers to process data for example deep learning
01:45powers image recognition like identifying cats in photos it's incredibly powerful but requires lots of data
01:52and computing power to work well deep learning is behind many advanced ai applications today taking machine
01:58learning to the next level so how are ai machine learning and deep learning related think of ai as the big
02:08umbrella it's the broad field of creating intelligent machines machine learning is a subset of ai focusing on
02:15learning from data deep learning is a subset of machine learning using neural networks for more
02:20complex tasks each layer builds on the previous one making ai a family of techniques that work together
02:26to create smart systems let's compare ai machine learning and deep learning by their scope ai is the broadest
02:37it includes all intelligent systems from simple rules to advanced learning machine learning is narrower
02:43focusing on data driven learning without explicit programming deep learning is even narrower relying on
02:49neural networks for specific complex tasks the scope narrows as we go deeper but each level adds more
02:55power and complexity to what machines can do another key difference is data and computing needs ai varies
03:05it can use simple rules or learning so its needs depend on the method machine learning needs data to learn and
03:11moderate computing power like for spam filtering deep learning however requires lots of data and
03:17high computing power think of image recognition where it processes millions of images to learn the more advanced the
03:23technique the more resources it demands which is why deep learning often needs powerful machines
03:32let's look at examples to see the differences in action ai includes complex systems like self-driving cars which combine many
03:39techniques to work machine learning powers recommendation systems like netflix suggesting
03:45shows based on your viewing habits deep learning excels in tasks like voice recognition think of alexa
03:51understanding your commands each approach shines in different areas showing how they complement each other within the ai family
04:01machine learning has many real world applications it's used in fraud detection like spotting suspicious transactions in banking
04:07predictive maintenance in factories uses ml to predict when machines might fail saving time and money even
04:15personalization like spotify curating playlists for you relies on machine learning ml is practical and widely used
04:22making our lives easier in so many ways
04:28deep learning on the other hand excels in more complex tasks it powers image recognition like facial recognition to
04:34unlock your phone natural language processing such as translating languages often uses deep learning
04:41think of google translate autonomous driving like tesla's vision systems also relies on deep learning to
04:46process camera data and make decisions deep learning is perfect for complex tasks that need lots of data and power
04:56so which should you use ai machine learning or deep learning it depends on the task for simple tasks
05:03traditional ai or basic machine learning might be enough for data-driven tasks like the teachable
05:09machine demo use machine learning for complex tasks like image recognition deep learning is the way to go
05:15your choice depends on the data you have and the resources available like computing power it's all about
05:21picking the right tool for the job
05:26what's next for ai machine learning and deep learning ai is moving towards general and super intelligent ai
05:33as we discussed in day two machine learning is focusing on more efficient algorithms that need
05:38less data to learn deep learning is improving neural networks opening up even wider applications like
05:44better autonomous systems the future is exciting with advancements that will make ai even more powerful
05:49and helpful and helpful in our lives

Recommended