Types Of Machine Learning | Machine Learning Algorithms | Machine Learning Tutorial | Simplilearn

Summary

In this tutorial from Simply Learn, Anirban explores the fascinating world of machine learning, a technology that permeates our daily lives more than we realize. He contrasts life without and with machine learning, highlighting its applications in search engines, facial recognition, virtual reality, and recommendation systems. The video delves into machine learning's three types: supervised, unsupervised, and reinforcement learning, each with its unique approach to learning from data. Anirban also discusses how to select the right machine learning solution based on problem statements, data characteristics, and complexity. The tutorial concludes with an overview of four key algorithms: k-Nearest Neighbor, Linear Regression, Decision Tree, and Naive Bayes, illustrating their workings with relatable examples.

Takeaways

• 🌐 Machine learning is a significant topic in technology, impacting various aspects of daily life.
• 🔍 Without machine learning, tasks like searching for information or facial recognition would be much more difficult.
• 🎮 Machine learning enhances gaming experiences through virtual reality and gesture control, adapting to player strategies.
• 🛒 Amazon uses machine learning for product recommendations, dynamic pricing, and customer segmentation.
• 🚖 Uber and similar apps use machine learning to predict destinations and optimize routes based on traffic and other factors.
• 🤖 Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without explicit programming.
• 📊 There are three main types of machine learning: supervised, unsupervised, and reinforcement learning, each with distinct use cases and methodologies.
• 🏫 Supervised learning requires labeled data and is used for tasks like spam filtering, while unsupervised learning finds patterns in unlabeled data, useful for recommendation systems.
• 🔄 Reinforcement learning operates through trial and error, learning from feedback to improve performance, commonly used in gaming for adaptive difficulty.
• 🛠 The choice of machine learning solution depends on the problem statement, data characteristics, and the complexity of the task at hand.
• ⚙️ Key algorithms in machine learning include k-nearest neighbors for classification, linear regression for establishing relationships, decision trees for branching decisions, and naive Bayes for probabilistic predictions.

Q & A

What is the main topic of the tutorial provided by Anirban from Simply Learn?

The main topic of the tutorial is machine learning, focusing on its applications, types, and algorithms.

How does the tutorial describe the impact of machine learning on daily life?

The tutorial illustrates the impact of machine learning on daily life by discussing how it simplifies tasks like searching information on Google, facial recognition on social media, and virtual reality in gaming.

What are some examples given in the tutorial where machine learning is used in gaming?

The tutorial mentions the use of virtual reality glasses for immersive gaming, gesture control gaming, and adaptive AI opponents in games like FIFA.

How does Amazon use machine learning according to the tutorial?

Amazon uses machine learning for product recommendations, dynamic pricing based on demand, and customer segmentation to cater to customer needs more effectively.

What is the definition of machine learning provided in the tutorial?

Machine learning is defined as an application of artificial intelligence that enables systems to learn from experience and improve without being explicitly programmed.

What are the three primary types of machine learning discussed in the tutorial?

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

How does supervised learning work as explained in the tutorial?

Supervised learning works by training a model with labeled data, where the system learns from the labeled examples and applies this knowledge to make predictions on new, unseen data.

What is the difference between supervised and unsupervised learning according to the tutorial?

Supervised learning uses labeled data and provides feedback for predictions, while unsupervised learning works with unlabeled data to discover patterns and does not involve feedback for predictions.

What are some factors that influence the selection of a machine learning solution as mentioned in the tutorial?

The factors that influence the selection of a machine learning solution include the problem statement, the size, quality, and nature of the data, and the complexity of the solution.

Can you provide an example of how the k-nearest neighbors (KNN) algorithm works as described in the tutorial?

The tutorial explains KNN with an example of classifying a new data point based on its proximity to known data points. If the new data point is closer to a cluster of tennis balls, it is classified as a tennis ball.

What is the purpose of the linear regression algorithm as discussed in the tutorial?

The purpose of the linear regression algorithm is to establish a linear relationship between variables to predict numerical values, such as predicting a person's weight based on their height.

Outlines

00:00

🎓 Introduction to Machine Learning

In this introductory section, the speaker Anirban from Simply Learn provides an overview of the tutorial's agenda on machine learning, a hot topic in the tech world. The video is aimed at both beginners and those with basic knowledge, promising to cover the essentials, applications, and advanced concepts of machine learning. The section also emphasizes the significance of understanding machine learning's impact on daily life and its prevalence across various sectors.

05:01

🤔 Life Without Machine Learning

This paragraph explores the challenges of a world without machine learning, highlighting how deeply integrated it is in our daily lives. Examples include Google search, facial recognition on social media platforms, and virtual assistants like Siri and Cortana. The speaker underscores how machine learning simplifies complex tasks, such as information retrieval and photo tagging, making life significantly easier compared to a few years ago.

10:02

🎮 Life With Machine Learning

Here, the video delves into specific examples of machine learning's influence on modern life. It touches on the gaming industry's advancements, such as virtual reality and gesture control, as well as e-commerce platforms like Amazon, which use machine learning for recommendation systems, dynamic pricing, and customer segmentation. The paragraph illustrates how machine learning enhances user experience across different domains, including ride-sharing apps like Uber.

15:04

🧠 Understanding Machine Learning

This paragraph introduces the core concept of machine learning, describing it as a form of artificial intelligence that enables systems to learn and improve from experience without explicit programming. The speaker uses simple analogies to explain how machine learning works, such as a system analyzing data to identify and categorize different types of fruits, thereby demonstrating the basic learning and prediction process.

20:05

📚 Types of Machine Learning

The speaker categorizes machine learning into three primary types: supervised, unsupervised, and reinforcement learning. Supervised learning involves training a model with labeled data, unsupervised learning deals with unstructured data without labels, and reinforcement learning focuses on systems learning from feedback. Each type is explained with clear examples, helping the audience understand their distinct characteristics and applications.

⚖️ Choosing the Right Machine Learning Solution

This section emphasizes the importance of selecting the appropriate machine learning model based on factors such as the problem statement, data quality, and complexity. The speaker advises caution in choosing between supervised, unsupervised, and reinforcement learning models to avoid inefficiencies in time, energy, and cost. The explanation is tailored to help viewers grasp the considerations involved in building effective machine learning solutions.

🔍 Key Machine Learning Algorithms

In this paragraph, the speaker introduces various algorithms used in machine learning, categorizing them into classification, regression, and clustering. Classification algorithms, like decision trees and logistic regression, are used for binary outcomes, while regression is for predicting numerical values. Clustering is explained as a method for organizing data, commonly used in recommendation systems and search engines. The speaker provides examples to make these concepts more relatable.

📊 Detailed Overview of Four Algorithms

The speaker provides an in-depth look at four widely used machine learning algorithms: K-nearest neighbor (KNN), linear regression, decision tree, and naive Bayes. KNN is explained with a practical example involving the classification of sports balls. Linear regression is described through its application in predicting relationships between variables, like height and weight. The decision tree is illustrated with a real-life scenario of making decisions based on conditions, while naive Bayes is discussed in the context of spam mail filtering.

🏁 Conclusion and Call to Action

The final paragraph wraps up the video by encouraging viewers to apply the concepts learned and engage with the content by asking questions and providing feedback. The speaker also promotes subscribing to the Simply Learn YouTube channel for more educational videos on technology topics, inviting viewers to continue learning and stay updated with new content.

Keywords

💡Machine Learning

Machine learning is a subset of artificial intelligence that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. In the video, it's portrayed as a transformative technology that enhances daily life by automating tasks and providing personalized experiences. Examples include Google search results, facial recognition on social media, and recommendation systems on e-commerce platforms.

💡Artificial Intelligence (AI)

Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The video uses AI as an umbrella term, under which machine learning operates, providing systems with the ability to learn and improve from experience without being explicitly programmed.

💡Supervised Learning

Supervised learning is a type of machine learning where the model is trained on labeled data, meaning the training data includes both the input and the desired output. The video explains how it's used for tasks like spam filtering, where the system learns from labeled examples (spam or not spam) to predict the category of new emails.

💡Unsupervised Learning

Unsupervised learning involves training algorithms on data without labeled responses. The system is tasked with finding patterns and structures within the data. The video uses the example of a recommendation system, like those on Amazon, which groups products based on similarities without prior labels.

💡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 some notion of cumulative reward. The video likens it to a baby learning not to touch a flame, where the system learns from feedback to improve its predictions over time, as seen in adaptive video game AI.

💡Algorithms

In the context of the video, algorithms are the methods or processes that machine learning models use to perform tasks. They are the building blocks of machine learning, with different algorithms suited for different types of problems, such as classification, regression, and clustering.

💡Classification

Classification is a type of supervised learning where the algorithm determines the category or class of an entity based on its features. The video provides an example of a shopkeeper using a classification algorithm to predict whether a customer will return to the store.

💡Regression

Regression is a type of predictive modeling technique that estimates the relationship between variables. In the video, it's used to predict numerical values, such as the price of a product based on its demand, with the example of predicting a person's weight based on their height.

💡Clustering

Clustering is an unsupervised learning technique used for grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. The video mentions its use in recommendation systems and search engines to organize data and provide personalized results.

💡K-Nearest Neighbors (KNN)

K-Nearest Neighbors is a simple, supervised learning algorithm that classifies a data point based on the majority vote of its neighbors, with the 'k' determining the number of neighbors considered. The video uses a sports ball example to illustrate how KNN might classify an unknown ball based on its proximity to known balls in a feature space.

💡Decision Tree

A decision tree is a flowchart-like structure in which each internal node represents a feature, each branch represents a decision rule, and each leaf node represents an outcome. The video explains it as a branching method that mirrors human decision-making, such as deciding whether to go swimming based on the weather.

💡Naive Bayes

Naive Bayes is a classification technique based on applying Bayes' theorem with the assumption of independence between every pair of features. The video uses it to illustrate spam email filtering and decision-making under uncertainty, like predicting the likelihood of playing cricket based on weather conditions.

Highlights

Introduction to the tutorial on machine learning by Anirban from Simply Learn.

Machine learning's role in making everyday tasks easier, such as information search and facial recognition.

The impact of machine learning on gaming with virtual reality and gesture control.

How machine learning enhances the e-commerce experience through recommendation systems and dynamic pricing.

The application of machine learning in ride-sharing apps like Uber for route optimization.

Definition of machine learning as a subset of artificial intelligence that enables systems to learn from experience.

Explanation of how machine learning works with an example of fruit classification.

Types of machine learning: Supervised, Unsupervised, and Reinforcement Learning.

Supervised learning explained through the training of a model with labeled data.

Unsupervised learning demonstrated by clustering data without labels.

Reinforcement learning compared to a baby learning from touching a flame.

The importance of selecting the right machine learning solution based on the problem statement, data nature, and complexity.

Classification, Regression, and Clustering as key methods in machine learning.

K-Nearest Neighbors algorithm explained with a sports ball classification example.

Linear Regression algorithm used for predicting relationships between variables.

Decision Tree algorithm using a branching method for decision-making.

Naive Bayes algorithm utilizing conditional probability for large data set predictions.

Conclusion of the tutorial with a call to action for feedback and further learning.

Transcripts

hi guys i'm anirban from simply learn

and today i will be giving you a

tutorial on one of the most talked about

topics in the world of technology and

that is machine learning

i'm sure that if you are watching this

video you must have heard about machine

learning somewhere or the other and yes

it is quite an intriguing topic

this video will act as a supplement for

the ones who know the basics of machine

learning and are aware of where it is

being used and how it is being used for

the ones who are completely new to this

you don't have to worry

we will be brushing up the basics as

well

so to start off with let's have a look

at the agenda

first we'll look into a life without

machine learning

then we'll move into a life with machine

learning

then we'll try to understand what is

machine learning actually

then we look into the types of machine

learning we'll talk about the right

machine learning solutions

then we'll move into the different

algorithms that machine learning has

let's talk about a life without machine

learning

for someone who doesn't know anything

about machine learning or artificial

intelligence he or she might think that

it is only being used in robots or

machines and stuff which is actually

true as ported by most of the sci-fi

movies nowadays

but you wouldn't believe how much more

machine learning is giving us how much

of it we are using in our daily lives

let's say you need information on any

random topic say a computer

the first place you would check would

definitely be google right

it would collect all the information on

the word you search for and present it

to you according to relevance if there

was no google you would have to do it in

a very hard way that is by going through

tens or hundreds of books and articles

even after which you won't find the

answer

if we go back four to five years facial

recognition would be a thing only showed

in movies like mission impossible or

terminator or any of the sci-fi movies

you pick

but machine learning has made it

possible for facebook and instagram to

use this feature for your benefits you

don't have to go through the trouble of

tagging every single person present in a

picture that you have posted

facebook will automatically recognize

the people in the photo and tag them for

you

that saves a lot of time for you doesn't

it

also siri cortana iris all of them would

not be there to help you if there was no

machine learning

now that we know life would be a lot

more difficult without machine learning

let's look into the things that are run

by machine learning and we are using

them on our daily basis

let's enter the world of gaming ps4s and

xboxes have introduced virtual reality

glasses which brings a whole new level

of detail into gaming

every time your head moves in real world

it replicates the movement in the

virtual world providing an excellent

gaming experience

then there is gesture control gaming

where in machine learning tracks your

body movement and makes a corresponding

movement in the game

finally in the game fifa your opponent

tends to adapt based on the kind of

strategy or gameplay you follow

that is again done by machine learning

i'm sure you must have shopped a lot

from amazon so let's look into some of

the places amazon makes use of machine

learning

let's say you buy a formal t-shirt on

amazon now as you buy it it suggests you

formal shoes ties blazers and apparels

that go with what you have bought

that's the recommendation system powered

by machine learning again

the price on every product that you see

on amazon is varying every moment based

on demand

that's being done by machine learning

algorithms

then comes customer segmentation

customer segmentation is one of the most

crucial thing for all the e-commerce

platforms machine learning helps them

differentiate between customers based on

what they buy how frequent are they and

their reviews

this helps the companies to make sure

that their customers are taken care of

and the needs are being fulfilled

now let's talk about an app

that you use on a daily basis to reach

office on time

yes it's uber gaps once you have

traveled with uber you must have noticed

that it suggests you the places you

might want to go based on your previous

journeys when you are taking a share how

does the app make sure that the cab you

get is travelled by the same route you

want to travel in

there are so many factors like the

distance the traffic the ratings all of

it is taken care of by machine learning

for the past five minutes we have been

talking about how machine learning is

improving our lives where all it is

being used i have probably said machine

learning 100 times already

i think it's about time that i tell you

what machine learning actually is

let's take an ordinary system that you

currently are using it can't do much

except from the basic operations that

you already know about

now

let's add artificial intelligence or for

a layman's point of view let's give the

same machine the power to think on its

own

that is what machine learning basically

is

it's an application of artificial

intelligence that provides systems the

ability to learn on their own and

improve from experience without being

programmed externally

so if your computer had machine learning

may be able to play the difficult parts

of a game for you or probably solve a

complicated mathematical equation for

you

that could be really helpful

let's dive in a little deeper and see

how machine learning works

let's say you provide a system with the

input data that carries the photos of

various kinds of fruits now you want the

system to figure out what are the

different fruits and group them

accordingly so what the system does it

analyzes the input data

then it tries to find patterns patterns

like shapes size and color

based on these patterns the system will

try to predict the different types of

fruit and segregate them

finally it keeps track of all such

decisions it took in the process to make

sure it's learning the next time you ask

the same system to predict and segregate

the different types of fruits

it won't have to go through the entire

process again

that's how machine learning works

now let's look into the types of machine

learning

machine learning is primarily of three

types first one is supervised machine

learning as the name suggests you have

to supervise your machine learning while

you train it to work on its own it

requires labeled training data

next up is unsupervised learning wherein

there will be training data but it won't

be labeled

finally there's reinforcement learning

wherein the system learns on its own

let's talk about all these types in

detail

let's try to understand how supervised

learning works look at the pictures very

very carefully the monitor depicts the

model or the system that we are going to

train

this is how the training is done

we provide a data set that contains

pictures of a kind of a fruit say an

apple

then we provide another data set which

lets the model know that these pictures

were that of a fruit called apple

this ends the training phase

now

what we will do is we provide a new set

of data which only contains pictures of

apple

now here comes the fun part the system

can actually tell you what fruit it is

and it will remember this and apply this

knowledge in future as well that's how

supervised learning works you are

training the model to do a certain kind

of an operation on its own

this kind of a model is generally used

into filtering spam mails from your

email accounts as well

yes

surprise aren't you

so let's move on to unsupervised

learning now let's say we have a data

set which is cluttered in this case we

have a collection of pictures of

different fruits we feed this data to

the model and the model analyzes the

data to figure out patterns in it

in the end it categorizes the photos

into three types as you can see in the

image based on their similarities

so you provide the data to the system

and let the system do the rest of the

work simple isn't it

this kind of a model is used by flipkart

to figure out the products that are well

suited for you

honestly speaking this is my favorite

type of machine learning out of all the

three and this type has been widely

shown in most of the sci-fi movies

lately

let's find out how it works

imagine a newborn baby

you put a burning candle in front of the

baby

the baby does not know that if it

touches the flame its fingers might get

burned

so it does that anyway and gets hurt

the next time you put that candle in

front of the baby it will remember what

happened the last time and would not

repeat what it did

that's exactly how reinforcement

learning works

we provide the machine with a data set

wherein we ask it to identify a

particular kind of a fruit in this case

an apple

so what it does as a response it tells

us that it's a mango

but as we all know it's a completely

wrong answer so as a feedback we tell

the system that it's wrong it's not a

mango it's an apple what it does it

learns from the feedback and keeps that

in mind

when the next time when we ask a same

question it gives us the right answer it

is able to tell us that it's actually an

apple that is a reinforced response so

that's how reinforcement learning works

it learns from its mistakes and

experiences

this model is used in games like prince

of persia or assassin's creed or fifa

wherein the level of difficulty

increases as you get better with the

games just to make it more clear for you

let's look at a comparison between

supervised and unsupervised learning

firstly the data involved in case of

supervised learning is labeled as we

mentioned in the examples previously

we provide the system with a photo of an

apple and let the system know that this

is actually an apple

that is called label data so the system

learns from the label data and makes

future predictions

now unsupervised learning does not

require any kind of label data because

its work is to look for patterns in the

input data and organize it

the next point is that you get a

feedback in case of supervised learning

that is once you get the output the

system tends to remember that and uses

it for the next operation

that does not happen for unsupervised

learning

and the last point is that supervised

learning is mostly used to predict data

whereas unsupervised learning is used to

find out hidden patterns or structures

in data

i think this would have made a lot of

things clear for you regarding

supervised and unsupervised learning

now let's talk about a question that

everyone needs to answer before building

a machine learning model

what kind of a machine learning solution

should we use

yes you should be very careful with

selecting the right kind of solution for

your model because if you don't you

might end up losing a lot of time energy

and processing cost

i won't be naming the actual solutions

because you guys aren't familiar with

them yet

so we will be looking at it based on

supervised unsupervised and

reinforcement learning

so let's look into the factors that

might help us select the right kind of

machine learning solution

first factor is the problem statement

describes the kind of model you will be

building or as the name suggests it

tells you what the problem is

for example let's say the problem is to

predict the future stock market prices

so for anyone who is new to machine

learning would have trouble figuring out

the right solution

but with time and practice you will

understand that for a problem statement

like this

solution based on supervised learning

would work the best for obvious reasons

then comes the size quality and nature

of the data if the data is cluttered you

go for unsupervised if the data is very

large and categorical we normally go for

supervised learning solutions

finally we choose the solution based on

their complexity

as for the problem statement wherein we

predict the stock market prices it can

also be solved by using reinforcement

learning

but that would be very very difficult

and time consuming unlike supervised

learning

algorithms are not types of machine

learning

in the most simplest language they are

methods of solving a particular problem

so the first kind of method is

classification which falls under

supervised learning

classification is used when the output

you are looking for is a yes or no or in

the form a or b or true or false like if

a shopkeeper wants to predict if a

particular customer will come back to

his shop or not he will use a

classification algorithm

the algorithms that fall under

classification are decision tree knife

base random forest logistic regression

and

knn the next kind is regression

this kind of a method is used when the

predicted data is numerical in nature

like if the shopkeeper wants to predict

the price of a product based on its

demand it would go for regression the

last method is clustering

clustering is a kind of unsupervised

learning again it is used when the data

needs to be organized

most of the recommendation system used

by flipkart amazon etc make use of

clustering

another major application of it is in

search engines the search engines study

your old search history to figure out

your preferences and provide you the

best search results

one of the algorithms that fall under

clustering is k-means

now that we know the various algorithms

let's look into four key algorithms that

are used widely

we will understand them with very simple

examples

the four algorithms that we will try to

understand are k nearest neighbor

linear regression

decision tree and naive bayes

let's start with our first machine

learning solution k nearest neighbor k

near its neighbor is again a kind of a

classification algorithm as you can see

on the screen the similar data points

form clusters

the blue one

the red one

and the green one there are three

different clusters

now if we get a new and unknown data

point it is classified based on the

cluster closest to it or the most

similar to it

k in k n is the number of nearest

neighboring data points we wish to

compare the unknown data with

let's make it clear with an example

let's say we have three clusters in a

cost to durability graph

first cluster is of footballs

the second one is of tennis balls

and the third one is of basketballs

from the graph we can say that the cost

of footballs is high and the durability

is less

the cost of tennis balls is very less

but the durability is high and the cost

of basketballs

is as high as the durability

now let's say we have an unknown data

point

we have a black spot which can be one

kind of the balls but we don't know what

kind it is

so what we'll do we'll try to classify

this using knn

so if we take k is equal to 5 we draw a

circle keeping the unknown data point at

the center and we make sure that we have

five

balls inside that circle in this case we

have a football a basketball and three

tennis balls

now since we have the highest number of

tennis balls inside the circle

the classified ball would be a tennis

ball

so that's how k nearest neighbor

classification is done

linear regression is again a type of

supervised learning algorithm this

algorithm is used to establish linear

relationship between variables one of

which would be dependent and the other

one would be independent

like if we want to predict the weight of

a person based on his height

weight would be the dependent variable

and height would be independent

let's have a look at it through an

example

let's say we have a graph here showing a

relationship between height and weight

of a person

let's put the y-axis as h

and the

x-axis as weight

so the green dots are the various data

points

these green dots are the data points

and d

is the mean squared error that is the

perpendicular distances from the line to

the data points

are the error values

this error tells us how much the

predicted values vary from the original

value

let's ignore this blue line for a while

so let's say if this is our regression

line

you can see the distance from all the

data points from this line is very high

so if we take this line as a regression

line

the error in the prediction will be too

high

so

in this case the model will not be able

to give us a good prediction

let's say we draw another regression

line here like this

even in this case you can see that the

perpendicular distance of the data

points from the line is very high

so the error value will still come as

high as the last one

so this model will also not be able to

give us a good prediction

so what to do

so finally we draw a line which is this

blue line

so here we can see that the distance of

the data points from the line is very

less

relative to the other two lines we drew

so the value of d for this line will be

very less

so in this case if we take

any value on the x-axis the

corresponding value on the y-axis will

be our prediction

and

given the fact that the d is very low

our prediction should be good also

this is how regression works

we draw a line a regression line that is

in such a way that the value of d is the

least eventually giving us good

predictions

this algorithm that is decision tree is

a kind of an algorithm you can very

strongly relate to

it uses a kind of a branching method to

realize the problem and make decisions

based on the conditions

let's take this graph as an example

imagine yourself sitting at home getting

bored you feel like going for a swim

what you do is you check if it's sunny

outside so that's your first condition

if the answer to that condition is yes

you go for a swim if it's not sunny then

the next question you would ask yourself

is if it's raining outside so that's

condition number two if it's actually

raining you cancel the plan and stay

indoors if it's not raining then you

would probably go outside and have a

walk

so that's the final node

that's how decision tree algorithm works

you probably use this every day it

realizes a problem and then takes the

decisions based on the answers to every

conditions

nybis algorithm is mostly used in cases

where a prediction needs to be done on a

very large data set it makes use of

conditional probability

conditional probability is the

probability of an event say a happening

given that another event b has already

happened

this algorithm is most commonly used in

filtering spam mails in your email

account

let's say you receive a mail

the model goes through your old spam

mail records

then it uses space theorem to predict if

the present mail is a spam mail or not

so pc of a is the probability of even c

occurring when a has already occurred

b

a of c is the probability of event a

occurring when c has already occurred

and b c

is the probability of event c occurring

and p a is a probability of event a

occurring

let's try to understand naive bayes with

a better example

nybase can be used to determine on which

days to play cricket

based on the probabilities of a day

being rainy windy or sunny the model

tells us if a match is possible

if we consider all the weather

conditions to be event a for us

and the probability of a match being

possible even c

so the model applies the probabilities

of event a and c into the bayes theorem

and predicts if a game of cricket is

possible on a particular day or not

in this case if the probability of c of

a is more than 0.5 we can be able to

play a game of cricket if it's less than

0.5 we won't be able to do that that's

how naive algorithm works

so that brings us to the end of the

video i hope you guys understood the

concepts put your doubts and feedbacks

in the comments below and stay tuned for

more videos

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