1.2. Supervised vs Unsupervised vs Reinforcement Learning | Types of Machine Learning

Summary

In this video, Siddharthan explains the concept of machine learning as a technique to enable artificial intelligence through data learning without explicit programming. He introduces three main types of machine learning: supervised, where algorithms learn from labeled data; unsupervised, where they discern patterns from unlabeled data; and reinforcement, which involves agents learning to maximize rewards in an environment. The video aims to clarify these concepts with examples, such as image recognition for supervised learning and game playing for reinforcement learning.

Takeaways

• 🌟 The channel focuses on artificial intelligence and machine learning topics.
• 📊 In the previous video, the difference between AI, machine learning, and deep learning was explained.
• 📚 This video aims to explain the different types of machine learning.
• 🤖 Machine learning is a technique that enables AI to learn from data without explicit programming.
• 🐶🐱 An example given is training a machine to recognize images of dogs and cats.
• 🏅 There are three main types of machine learning: supervised, unsupervised, and reinforcement learning.
• 🏷️ Supervised learning uses labeled data, where the algorithm is trained on data with known outcomes.
• 🔍 Unsupervised learning involves unlabeled data, where the algorithm finds patterns and groups data without prior knowledge.
• 🎮 Reinforcement learning is about training an agent to take actions in an environment to maximize rewards, distinct from supervised and unsupervised learning.
• 🚗 Applications of reinforcement learning include game-playing AI and autonomous systems like self-driving cars and drones.
• 🔗 A hands-on data science course with Python is recommended for further learning.

Q & A

What is the main focus of Siddharthan's channel?

The main focus of Siddharthan's channel is on artificial intelligence and machine learning topics.

What was the topic of the previous video by Siddharthan?

The previous video explained the difference between artificial intelligence, machine learning, and deep learning.

What is machine learning according to the script?

Machine learning is a technique to implement artificial intelligence that can learn from data without being explicitly programmed.

What is the ultimate goal of machine learning?

The ultimate goal of machine learning is to create intelligent machines that can learn from data.

How does a machine learning model learn to recognize images of dogs and cats?

A machine learning model learns to recognize images of dogs and cats by being fed several labeled images of dogs and cats, and it tries to find patterns in these images to recognize new images.

What are the three main types of machine learning mentioned in the script?

The three main types of machine learning mentioned are supervised learning, unsupervised learning, and reinforcement learning.

In supervised learning, what kind of data does the machine learning algorithm learn from?

In supervised learning, the machine learning algorithm learns from labeled data.

What is the difference between supervised and unsupervised learning in terms of data labeling?

In supervised learning, the data is labeled, meaning the algorithm is told what each data point represents, whereas in unsupervised learning, the data is unlabeled, and the algorithm must find patterns and group the data on its own.

How does reinforcement learning differ from supervised and unsupervised learning?

Reinforcement learning differs from supervised and unsupervised learning as it involves an agent taking actions in an environment to maximize rewards, which is not based on labeled or unlabeled data sets.

What are the four main aspects of reinforcement learning?

The four main aspects of reinforcement learning are environment, agent, action, and reward.

What is an example application of reinforcement learning mentioned in the script?

An example application of reinforcement learning mentioned is developing computer programs that can play chess like a human being.

Outlines

00:00

🤖 Introduction to Machine Learning

The paragraph introduces the concept of machine learning as a technique to implement artificial intelligence, allowing machines to learn from data without explicit programming. The speaker, Siddharthan, explains the goal of machine learning is to create intelligent machines that can learn patterns from data, using the example of a machine learning model trained to recognize images of dogs and cats. The paragraph also outlines the three main types of machine learning: supervised, unsupervised, and reinforcement learning, setting the stage for a deeper exploration in the video.

05:00

📊 Supervised and Unsupervised Learning Explained

This paragraph delves into the specifics of supervised and unsupervised learning. Supervised learning is described as a process where the machine learning algorithm is trained on labeled data, with examples given to illustrate how a model can learn to differentiate between images of apples and mangoes. Unsupervised learning, on the other hand, involves training the algorithm on unlabeled data, allowing it to find patterns and group similar items together without prior knowledge of the data's content. The paragraph provides a clear distinction between the two types of learning, emphasizing the role of labels in supervised learning and the exploratory nature of unsupervised learning.

🚀 Reinforcement Learning: Actions and Rewards

The final paragraph introduces reinforcement learning, a distinct type of machine learning where an 'agent' learns to take actions in an 'environment' to maximize rewards. The concept is explained through the example of a computer program learning to play chess, where the chessboard is the environment and the computer is the agent. The agent's actions are the moves it makes, and it receives rewards or penalties based on the outcomes, guiding its learning process. The paragraph highlights the application of reinforcement learning in game-playing AI and autonomous systems like self-driving cars and drones, concluding the video's exploration of machine learning types.

Keywords

💡Artificial Intelligence

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the context of the video, AI is the overarching field within which machine learning operates. The script discusses how AI can learn from data without being explicitly programmed, which is a fundamental concept in machine learning.

💡Machine Learning

Machine Learning is a subset of AI that enables computers to learn and improve from experience without being explicitly programmed. The video script explains that machine learning is about creating models that can learn patterns from data, such as distinguishing between images of dogs and cats, which is a practical application of machine learning in image recognition.

💡Deep Learning

Deep Learning, while not explicitly defined in the script, is a branch of machine learning based on artificial neural networks with representation learning. It is typically used for more complex data patterns and is implied when discussing the progression from AI to more specialized techniques like machine learning and deep learning.

💡Supervised Learning

Supervised Learning is a type of machine learning where the algorithm is trained on labeled data. The video provides an example of an algorithm learning to recognize apples and mangoes from labeled images. This method is called 'supervised' because the model is given guidance in the form of labels to learn from.

💡Unsupervised Learning

Unsupervised Learning is another type of machine learning where the model is given unlabeled data and is expected to find patterns on its own. The script uses the same example of apples and mangoes but without labels, and the model groups similar images together, which is how unsupervised learning works to discover inherent structures in data.

💡Reinforcement Learning

Reinforcement Learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize rewards. The video script describes this as a different approach from supervised and unsupervised learning, focusing on how an agent, like a computer program playing chess, learns to make moves that lead to winning.

💡Data

Data in the context of machine learning refers to the input that the algorithms learn from. The video script mentions images of dogs and cats as examples of data that a machine learning model would use to learn pattern recognition, emphasizing the importance of data in training machine learning models.

💡Pattern Recognition

Pattern Recognition is the ability of a machine learning model to identify regularities or patterns in data. In the video, pattern recognition is crucial for a model to distinguish between different images, such as recognizing whether an image is of a dog or a cat.

💡Labels

Labels are data points in a dataset that have been identified and categorized. In supervised learning, as explained in the video, labels are used to guide the machine learning model by telling it what each data point represents, such as associating certain images with 'apple' or 'mango'.

💡Environment

In the context of reinforcement learning, the 'environment' refers to the setting where the agent operates and learns. The video uses the example of a chessboard as the environment where the computer, acting as the agent, learns to make moves to maximize rewards, such as winning the game.

💡Agent

An 'agent' in reinforcement learning is an entity that can observe the environment through sensors and act upon that environment. The video script describes the computer as the agent in a chess-playing scenario, where it learns to make strategic moves to achieve the goal of winning.

Highlights

Introduction to the channel focusing on artificial intelligence and machine learning.

Explanation of the difference between artificial intelligence, machine learning, and deep learning from a previous video.

Definition of machine learning as a technique to implement artificial intelligence.

The goal of machine learning is to create intelligent machines that learn from data without explicit programming.

Example of a machine learning model learning to recognize images of dogs and cats.

Introduction to the three main types of machine learning: supervised, unsupervised, and reinforcement learning.

Supervised learning involves algorithms learning from labeled data with programmer supervision.

Unsupervised learning is where algorithms learn from unlabeled data without any guidance.

Reinforcement learning is distinct, focusing on agents taking actions in an environment to maximize rewards.

In supervised learning, machine learning models are trained on labeled datasets to recognize patterns.

Unsupervised learning involves clustering data into groups based on patterns without prior labels.

Reinforcement learning is applied in game-playing AI and autonomous systems like self-driving cars.

The four main aspects of reinforcement learning: environment, agent, action, and reward.

Example of reinforcement learning in teaching a computer program to play chess.

The ultimate reward in reinforcement learning is winning the game, with positive rewards for good moves and negative for bad ones.

Encouragement to subscribe for more related videos and a mention of a hands-on data science course with Python.

Transcripts

hello everyone this is siddharthan

if you are new here this channel is all

about artificial intelligence

and machine learning stuff subscribe for

more related videos

in the previous video i have explained

you what is the difference between

artificial intelligence machine learning

and deep learning in this video i would

like to explain you about

the different types of machine learning

okay so first of all i'll explain you

what is mean by machine learning with an

example

then we will look into all the different

types okay

so machine learning machine learning is

a technique to implement artificial

intelligence

that can learn from the data by

themselves without being explicitly

programmed

okay so the ultimate goal in machine

learning is to make

intelligent machines right and how we do

is

by making the machine to learn from the

data okay

so we don't do explicit program which

means we don't

tell the machine exactly what it has to

do so it has to

you know find those ways by itself so i

will try to explain this

with an example okay so we want a

machine

to see an image and to recognize whether

the image represents a dog

or cat okay so this is the goal for the

system we are building

now in machine learning what we will do

is we will

make the model or the machine learning

model to learn from the data

here the data will be several images of

docs and catch

okay so we will feed this images of dogs

and cats to our machine learning model

and with the help of these images it

tries to find

pattern in these images and when you

give a new image it can recognize

whether the image represents a dog

or cat okay so this is how machine

learning works so it basically learns

from the data

okay now let's discuss about the

different types of machine learning

okay so there are three main types of

machine learning one is supervised

learning

the next one is unsupervised learning

and the third one is reinforcement

learning

okay so in supervised learning there is

some supervision to the machine learning

algorithm

by the programmers or by us and in

unsupervised learning there is no

supervision

for the machine and reinforcement

learning is completely a different type

and it is not related to each of

this supervised or unsupervised learning

okay so

let's try to understand this in more

detail first of all surprise learning

in supervised learning the machine

learning algorithm learns from

labeled data so we already see that you

know

in machine learning the model learns

from the data right

so what is what is meant by label data

set let's say

that a machine learning model has to see

an image and

recognize whether the image represents

an apple or a mango

okay so now what we will do is we will

take several images of apples and

mangoes and

we will tell the machine that these

images belongs to apples

and these are the images of mangos okay

now

these apples and mangos so this name is

called as labels

and we feed this label dataset to our

machine learning model

now our machine learning model or

machine learning algorithm

tries to find the patterns between these

images

okay and once it it has learned from the

data

when you give an unknown image it can

correctly recognize whether the image

represents

an apple or a mango so this is how

supervised learning works so we are

telling it it is you know known as

supervised learning because

we are giving a supervision in terms of

labels

okay now let's discuss about

unsupervised learning

so in unsupervised learning the machine

learning algorithm learns from unlabeled

data

here we won't tell what that data

represents

okay so we won't give any labels let's

consider a similar example

so we will give several images of apple

and mangos

to our machine learning model and we

won't tell that these images belong to

apple or

these images belong to mango so we feed

all these images without telling what it

is to our machine learning model

and what it does is it tries to again

find the pattern

and it tries to group all these images

and it will group the images into group

one

and group two okay so all the apples

will be grouped in one group

and all the mangoes will be grouped in

another group so when you give

a new image of an apple or a mango it

tells you whether it belongs to the

groupon

or group so this is called as

unsupervised learning because

we are not giving any supervision in

terms of labels

okay so this is called as unsupervised

learning

and the third one is reinforcement

learning okay so the reinforcement

running

is not similar to you know supervised or

unsupervised learning

it's quite different from both other

types okay so let's try to understand

this in more detail

so this is the definition of

reinforcement learning reinforcement

learning is an area of machine learning

consult with how intelligent agents take

actions in an environment

to maximize its rewards okay so it can

be a bit difficult to understand

but i will try to break down this you

know definition into

a simple steps so there are four main

aspects in reinforcement learning

they are environment agent action and

reward

okay so there will be an environment and

what we need to do is

we need to build an agent that acts in

that environment

okay so that agent in that environment

he tries to take

some actions and for that action it

gains

some rewards okay so let's try to

understand this with an example

we want to make a computer software or a

computer program

that can play chess like a human being

okay so here

our chess board becomes the environment

and our computer become the agent okay

so in the environment of chessboard our

agent which is the computer

tries to take actions so the actions

represents the move

the computer takes okay so in the chest

and for each step it gets a reward so

the ultimate reward is winning the chess

game okay

so for each step it takes closer to

winning

it will get a positive reward okay so if

it takes a bad step or bad move

so it will get a negative reward so by

this the machine tries to

learn how to play that game okay so

several applications are there for

reinforcement learning for example

you know several game playing artificial

intelligence or based on reinforcement

learning and

all the autonomous systems like cars and

automatic drones

are based on reinforcement learning okay

so these are the different types of

machine learning so first we have

discussed about what is meant by

supervised learning

where we basically give the machine

learning algorithm labeled data set

and in unsupervised learning we give

unlabeled data set and reinforcement

learning

we will try to make an agent that acts

in an environment

to increase its chance of winning okay

so this is

the three main types of machine learning

okay

so that's it about the types of machine

learning subscribe for more related

videos

and if you want to learn data science

check out my hands-on data science

course with python

i have given the link in the description

do check out so that's it from my side

thanks for watching