Complete Machine Learning Course: 60-Hour Hands-On Tutorial with Python Projects

hello everyone i am siddharthan in this youtube channel i teach about artificial intelligence and machine learning

recently i planned to make a complete machine learning course with both conceptual topics and hands-on part in

python this will be a 60 hours long course with five parts and each part will be around

12 hours and there will be several use cases and projects in each of these parts and i have tried to add as much

topics as possible that are like very important so all these videos are already present

in my youtube channel individually and i wanted to combine all these things together so that it is easier for a

person who is just starting to learn about machine learning so i hope you have a great time learning this and all

the best to you so let me just quickly uh tell you what are all the topics that will be present

in this first part so we totally have about 10 modules in this machine learning course and this first

module will be machine learning basics where i explain you about what is the difference between artificial

intelligence machine learning and deep learning then the next topic will be what are all the different types of

machine learning such as supervised learning and surprise learning reinforcement learning and we will

discuss in detail about each of these topics individually and i explain you about what is meant by deep learning and

how it differs from machine learning and what are all the different applications of it so this is more of a theoretical

part where we understand the very basics of eml and the second module is again a very important one on python basics for

machine learning so all the use cases and programming that we do in this uh

you know course will be in python so it is important for us to understand some of the basics of python so there will be

uh you know how to use google collaboratory so google collaboratory is the platform that we use for our coding

again in this course so i have given you the basic understanding of how to use this google collaboratory

and what are all the features then there are topics such as like various data types list dictionary tuple etcetera and

you know how to use loops in python and how to create functions in python so those topics will be covered in the

second module and third module is again an interesting module where we discuss about some of the important libraries

that we need in machine learning such as numpy pandas matplotlib and c bond so numpy is more

of a library that supports this numpy array where we do several mathematical

operations and pandas is more of a data frame which you can consider about like a tables and matplotlib and c1 are

mainly used for data visualization where we build this you know plots and graphs and this is like a very important

library that is like widely used in data science applications where we need to build these plots to understand the data

better and the fourth module will be data collection and pre-processing where i will explain you about where you can

collect this data what are all the reliable sources that we have for this data collection and how to do various

data processing uh like handling the missing values and and you know how to handle

imbalance dataset train test split label encoding and all these topics will be covered here and also i have explained

about how to handle textual data as well so these are all the four modules that we have

and uh once all these modules are completed so there will be three use cases video so the first use case will

be on rock versus mind prediction where we train a machine learning model to predict whether a object is a rock or it

is a mind so that will be the first use case and the second use case will be predicting whether a person will be

having diabetes or not so this will be the second use case and the third one is an interesting one regarding textual

analysis where we try to build a machine learning system that predicts whether a male is a normal male or it is a spam

male so this will be the topics that we will be covering in this first part of this machine learning course so once

this part is completed so i'll upload videos on the upcoming parts where you have like more advanced and complex

topics on machine learning such as uh different machine learning models processes like cross validation hyper

parameter tuning etc okay so again all the very best so i hope you have a great time and

i'll create a github repository and put all the code and jupyter notebook files that is done in all these videos so you

can like refer that as well and most importantly i'll also give you time stamps for all these individual topics

so if you are interested in a specific topic you can like skip to that part okay so let's get started

artificial intelligence machine learning and deep learning so when you start learning about ai or machine learning

this is one of the most basic and inevitable question you need to answer and often interviewers ask this question

to know whether someone really knows about this topic or is just making things up so let's try to understand

what is the relationship between these terms and then we shall try to understand what these three concepts

mean okay first of all so there is a picture which clearly represents the relationship

between these terms so as you can see this image this venn diagram artificial intelligence is a broader field and

machine learning is a subset of artificial intelligence and again deep learning is a further subset of machine

learning okay so this is the relationship between them now let's try to understand about these

topics separately so what happens is people sometimes think that all these three terms mean the same thing but

that's not the case so as i said earlier ml is a subset of ai and deep learning is a subset of ml okay now let's try to

understand these topics separately so what is artificial intelligence artificial intelligence is a branch of

computer science that is concerned with building smart and intelligent machines so what does intelligent machines mean

and what does non-intelligent machines mean let's try to understand this with some examples

so examples of non-intelligent machines can be you know it can be a warts or it can be a bike because these machines

cannot think these machines cannot make decisions or do new things they are given a set work and they just do that

work repeatedly and what are intelligent machines so example for intelligent machines are autonomous cars example you

can consider a test locker and you can also consider google assistant which we you know encounter it in our daily life

so a tesla car is more you know intelligent than a normal car because it you know it doesn't require any input

from the driver and it can drive the car autonomously and google assistance is something that

you know it uses ai for its function so when we you know text google assistant in our phone

we don't feel like it is a computer software if we feel as if it is a human being so these are intelligent machines

because they can think and give you an answer they can do new things they can make the decisions of their own which is

not possible in the case of non-intelligent machines so artificial intelligence is all about

making these intelligent machines okay now let's try to understand about machine learning so what is machine

learning machine learning is a technique to implement artificial intelligence that can learn from the data by

themselves without being explicitly programmed so machine learning is all about the data okay so let's try to

consider an example so i want a system to

detect whether the image is the image of iron man or captain america okay so this is the task i said for the system okay

so we need to make this system so when we take a machine learning approach what we will do is

for the machine learning algorithm we will feed numerous images of both iron man and captain america and we will tell

that algorithm that these images are of iron man and these images are of captain america so now the algorithm

without our input it just finds the pattern between the images and when you give a new image it can predict

correctly whether that images of iron man or captain america so this is all about machine learning okay so what we

do is we just give the algorithm a lot of data to learn from okay it is just similar to a child

seeing something and learning from it so we just give it data and we don't need to do anything else so

it can find the patterns and learn from itself so this is called as machine learning and

this is how we implement ai okay now let's try to understand about deep learning deep learning is again a

subfield of machine learning that uses a special type of algorithms called as artificial neural networks to learn from

the data so this is a pictorial representation of the artificial neural network okay so

these artificial neural networks are modeled from our human brain from the neurons present in our human brains

there are numerous neurons in our brain that are interconnected so each neuron process

the information and sends the output to another neuron so this is the same concept used in the case of artificial

neural networks so they are nothing but a mathematical model that are connected like the neurons in our brain okay so

there are different layers in those artificial neural network so as you can see here the first layer is an input

layer then there are several hidden layers and then there will be outer output layer okay so we can discuss

about this in more detail in the future videos but you know in this video the idea is to give you a short idea about

what these three terms means okay so first we see

what is meant by artificial intelligence so then we saw what is meant by machine learning so machine learning is a

technique to implement artificial intelligence and again deep learning is one of the subset of machine learning

which just uses artificial neural networks okay so that's it about the difference between these three terms

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 meant 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

docks 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 mangoes 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 main

group it tells you whether it belongs to the group 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 running 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 concerned 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 chess 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 are 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 label 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 what are the

different types of supervised learning okay so this is the agenda for today's video

and i want this channel to be more interactive so from now on i will give a link for each video in the description

and in that link you will find a google form containing mcqs for that particular topic for example so in the description

of this video you will find a google form link that contains mcq on the topic supervised learning okay so once you

complete watching these videos you can try to answer those mcqs okay so let's get started

surprise learning so supervised learning is a type of machine learning in which the machine learning algorithm learns

from the labeled dataset okay so here the most important thing to note here is the algorithm

learns from the label data okay so what is this label data so in machine learning generally we feed the machine

learning algorithm a lot of data and we tell the algorithm that that this data represents this label okay and the

algorithm tries to map the labels and the data so that it can recognize it okay let's try to understand this more

deeply with an example so we want our machine learning model to see an image and recognize whether it

represents an apple or a mango okay so this is the task for a machine learning algorithm so in the case of super easy

running what we will do is we will feed the images of apples and mangoes and we will tell the machines that these images

represents apples and these images represents mangoes okay and we feed these images to our ml model and what it

does it tries to find the relationship between these images and it maps it to the label which is apples and mangos

okay now it knows uh you know our apple looks and how a mango looks okay so once it learned from the data when you give

an unknown image it can predict correctly whether it is an apple or a mango so this is how supervised learning

works so the important point to note here is we are training the machine learning model with data which is

labeled okay so in case of unsupervised learning we don't give the machine learning algorithm the labels okay so

that is the difference between surprise and unsupervised money okay now let's discuss about the types of

surprise learning there are two main types of supervised learning one is classification and another one is

regression okay so what is mean by this classification and regression so classification is about predicting a

class or discrete value okay so that is just these class or labels okay so there is

not continuous values like numbers so it will predict whether you know the problem statement we have to predict

whether it's a male or female true or false like that okay so there will only be classes and in regression we try to

predict the continuous values for example like the salary agent price okay say for example um

we need to predict salary of a person from his work experience okay so the salary will be in a continuous number

right so those kinds of problem statements are done using regression models okay in classification we just

predict it's you know whether true or false or male or female say for example the example we have seen before where

our model classifies the images into apples or mangos whereas in regression we find a particular number okay so

let's try to understand this with another example so first of all classification so we

want our machine learning model to see an image and recognize whether the image is

you know a dog or cat so what we will do is we will give the labels and the data to our machine learning model so it maps

with that label and the images now we can tell whether it is a dog or cat so this represents classification because

we are just classifying the image into either dog or cat okay so there is no middle values right so there is not any

decimal values here it is just binary in this case now lets discuss about an example for regression lets say that we

need to predict the rainfall in centimeter value for a given temperature or pressure and different factors on

which the rainfall depends okay so what we will do is we will train our machine learning model with this data like for

example so we will tell the machine learning model that for this temperature there will be this much amount of

rainfall and for you know different cases like this and when the model is learned from the data

when we give a new temperature value it can tell you how much rainfall we can expect so the rainfall in centimeter

will be a continuous value it can be a decimal value right so this is called as regression so in classification we try

to you know predict the class or type but in regression we try to predict or we try

to find a number okay so that is the difference between classification and regression so there are various lot of

different types of application on classification and regression so which we will see in our later videos in our

project videos okay now let's see what are some most important algorithms for classification

and regression decision tree classification random forest classification k nearest neighbor

classification so these are some examples of classification algorithms and regression model algorithms includes

logistic regression polynomial regression and support vector machine recognition so it's okay if you don't

understand what is meant by these algorithms so we will be working on each type of algorithms once we start doing

the hands-on part so i'll be explaining about them in more detail once we start those all the types of unsuppression so

if you haven't watched those videos do check out them now let's discuss about what is mean by unsupervised learning

and what are the different types of unsupervised learning tasks okay so unsupervised learning non-supervised

learning the machine learning algorithms learn from unlabeled data so this is the difference between

supervised and unsupervised learning where in supervised learning we use labeled data so we will tell our machine

learning model that this data represents this item like that and we won't uh say those things in unsupervised learning so

we will train our model with unlabeled data okay so let's try to understand this with an

example so we have several images of apples and mangoes and so once we feed this data to our machine

learning model so what happens is it can group that group the data so group these images

based on uh you know similar patterns so it can group the apples in one group and it can group the mangoes in the second

group okay so what happens here is we are not telling the model that these images represents apples and these

images represents mangoes so we are not giving that label whereas in supervised learning we will tell the machine that

these images represents apple okay so that's the difference between surprise and unsupervised learning so what

happens is it automatically finds the pattern between those images and it groups the similar items in one group

and uh another items in another group okay so this is the idea behind unsupervised learning so now let's try

to see what are the different uh types of unsupervision and actually one more thing so why we are calling uh this as

unsupervised learning is that in super age learning we are giving a supervision to our machine okay so that is like a

supervisor who gives the machine the labels okay but we are not giving any labels or supervision here means this is

called as unsupervised learning so what are the types of function page learning there are two types of

unsupervised learning so first task is clustering and the another task in unsupervised learning is association

okay so what is will be clustering and association then so clustering is an unsupervised learning task which

involves grouping the similar data points so this is the example which we have discussed right now which is uh the

apple and mango example where we group the similar data points and in association super is

learning test we just try to find some important relationship between data points okay so in a big data set we try

to find which data points are associated so which are similar okay so let's try to understand these two tasks in more

detail first of all clustering okay so let's say that we get a project from from a

mobile network company so they want us to suggest some ways on how they can

increase their user base and how they can increase their revenue okay so they are giving us their user data

and what we find is so we are feeding it to a clustering

algorithm so unsupervised learning algorithm and this model is uh clustering the data into

two clusters so okay so this is one possibility where people who are having i call duration

may have very less internet usage and people who are having eye internet usage may have i call duration okay so this is

a possibility and now what we can suggest that network companies that they can give

offers on internet usage for those people who are having any call duration and less internet usage okay

and we can give offers on call durations for people uh who are having less internet uses and vice versa by this you

know people tend to use both these features more okay so this is one way by which they can increase their revenue by

you know where people can opt for both the plans so this is one clustering example where the machine learning

algorithm can cluster the data based on the user experience based on the user data okay

now let's try to understand about association so let's consider there is a supermarket and there are several

customers who are buying these products specifically so customer base bread milk uh you know fruits and wheat so there is

another kind of customers who are buying bread milk rice and butter okay so the important association

between all these customer is that if someone buys bread that customer obviously is buying milk okay so this is

one of the important uh you know relationship we have found and this is this can be used really well and when a

customer buys a bread we can suggest them that they are they can buy milk so the third customer is most most probably

is going to buy milk also okay so this is one of the method where we can use association so i would like to give you

another interesting example in this case so we are all you know uh used to the famous odt platforms like netflix and

amazon prime so those ott platforms use this kind of algorithms to suggest suggesters movies

okay so let's say that i am watching avengers movie okay so now uh the netflix can suggest me uh movies

uh regarding you know the superhero movies because someone who have already watched avengers may have watched other

superhero movies so it associates those user behaviors and it can suggest me movies watched by that user so this is

one of the interesting [Music] applications of unsupervised learning so

these are some interesting examples now let's see what are some uh important unsupervised learning algorithms so

so apart from these five algorithms there are also several unsupervised moving algorithms but these five are

very important algorithms so we have k means clustering hierarchical clustering which are examples of clustering

examples and there is another algorithm called as principal component analysis which is used to reduce the dimensions

of our data let's say for example we have a data set where it contains 1000 rows and 100 columns so we want to

reduce this dimension okay so the columns represent the features so we can use this principle component analysis

algorithm to find which columns or which features are very important for our application okay so that is where

principal component analysis is used and it is a type of an unsupervised learning algorithms where we won't give the

machine learning algorithm any labels okay and there are other two algorithms apriori and eclat so these two

algorithms are example of association task okay so these are some of the important

unsupervised learning algorithms testing what is meant by deep learning what is meant by neural network what are the

important applications of deep learning and also we are going to discuss about some important events that made deep

learning so much popular okay so these are the topics we'll be discussing so let's get started first of all what is

meant by deep learning so deep learning is a subfield of machine learning that uses artificial neural networks to learn

from the data so we have already seen the difference between artificial intelligence machine learning and deep

learning so we know that artificial intelligence is a subfield of machine learning right so in machine learning we

basically use several data and we feed this data to our machine learning algorithm to make predictions right so

in deep learning what we do is we feed it to a specialized algorithms called as artificial neural networks okay so this

is the difference between machine learning and deep running okay so now let's try to understand how this uh you

know artificial neural networks is inspired so this is the diagram of uh neurons

present in our human brain okay so this neuron consists of it and the it consists of a nucleus okay so this is

where the information is processed in our brain okay so once the information is processed it passes through the

neuron body through the axon and from there it is transferred to another neuron and the pathway goes on like this

so this is how the information is processed in our brain and it is transferred to some part of our body

okay so this is the exact principle that inspired artificial neural networks as you can see here this is the

diagrammatic representation of the artificial neural network code so basically what happens is we have

individual neurons connected to each other which forms the neural network okay so each neuron has a mathematical

function assigned to it so this neuron processes the data say for example we want to you know recognize what is the

image represents so we want to basically do an image recognition task so we feed the image to this neural network and

in the input layer this image will be splitted into it its respective pixels so there will be a lot of pixels and

each of these pixels should be given to several neurons and in the input layer this uh

information this pixel value will be processed and it will be transferred to the heater layer and then uh again there

will be some processing happening in the even layer and then it will be transferred to the output layer where

the image is predicted okay so this is how the neural network works so as i told there

are three main layers in the neural networks so first one is the input layer then is the hidden layers and finally we

have output layer okay so there can be any number of hidden layers in a neural network depending on the

task we are doing doing okay so each neuron has a mathematical function so as i have told you and this process

information and each neuron in the input layer is connected to each neuron in the hidden layer okay and this is how the

information is passed and the respective prediction is made okay so this is all about artificial neural networks

now let's try to understand what is the difference main difference between machine learning algorithms and deep

learning okay so rather than defense it is you know deep learning has one main advantage over machine learning so that

you know difference is feature extraction okay so what is meant by this feature extraction let's say that we

want a machine learning model to predict whether a image represents a car okay so when you are giving it to a machine

learning model we need to tell the model that these features are important for a car okay for example if it is a car it

should have four wheels and it should have a shape like this and all that right so we need to give those features

to our machine learning model we have to manually tell them that these features are important but we don't need to do

that in the case of deep running because the neural networks are so much powerful than any machine learning algorithms

they can determine those feature by themselves okay so that is the main advantage of deep learning or machine

learning where we don't need to extract the feature manually okay now let's uh try to understand the

events that made deep learning so much popular okay so there is a famous deep learning

company called as deep mind so it is based in uh united kingdom so it was started around 2010 so deep

learning was there you know from that point of time and even before that so what happened is in 2014 google acquired

this company so deepmind basically made game playing artificial intelligence system okay so

there is this famous game called as go so this go this board game like just but it is so much complicated and so much

you know deeper than just because in just there is limited number of uh moves one can make but the possibilities

in go is so much more so there are several moves one can make based on the configuration of the game okay so

in 2016 they made a machine learning sorry a deep learning system that can play this go game and

they challenged the world champion lee sedol so he is 18 time world champion and they challenged him for a five match

tournament okay so that game playing system was made based on deep learning okay so they have been developing this

uh over you know five or six years and uh they challenged him and what

happened is in the tournament of five games so alphago which is the system deepmind

build has beat lee sedol for forest one okay so it has won four matches and lee sedal won one matches and that is where

people started to look at deep learning and realize that deep learning is so much powerful than any other algorithms

in machine learning okay so after that several researches were made and several modifications have been done to the

neural networks and several different types of neural networks have been uh you know invented after that so this is

the point from which deep learning got so much you know popular and it was used in several kinds of fields after that

okay let's try to understand one such example for this so diabetic retinopathy so diabetic

retinopathy is a condition where a patient may lose his eyesight lose especially due to diabetes meditates

okay so this deep mind developed a system uh based on deep learning that can determine whether a person has

diabetic retinopathy from the eye scans okay so how this is basically made is the deep learning

model will be trained with several normal eye images which does not have any dc's and the model will be again

trained with several images which has diabetic retinopathy okay so once it trained when a new image is fed to this

model it can predict whether the person has diabetic retinopathy or not so the interesting thing that happened here is

it doesn't only predict whether the person has diabetic retinopathy or not it also predicted the gender of the

patient whether you know the image of the is of a male or a female it also predicted whether they have some some

other medical conditions or not so this is one of the fascinating thing that happened where the deep learning also

predicted several other things rather than only the patient has diabetic retinopathy or not

okay so these events led to the boom of deep learning after that it was used in several other fields

now let's discuss about some of the important applications of deep learning first one is healthcare okay so the

example which we have seen now is an example of healthcare applications apart from this

deep learning is used in several diagnostic departments where it is used to predict whether a person has a

specific image specific disease or not based on their scans images and other data so another example where deep

learning used is the field of autonomous cars so autonomous cars like this lab doesn't need much

driver input to drive the car right so they can drive the car by themselves and it is powered by deep learning models

then we have computer vision so computer vision is one of the important application of deep learning so it is

based on image processing techniques where the neural network is trained with several images so one such example is

face recognition system in our phones so it is based on computer vision then there is natural language

processing so natural example of natural language processing is chatbots which we most of us would have

come across and other virtual assistants like google assistant siri and alexa all these technologies are powered by deep

learning or neural networks okay so these are some of the important applications of deep learning videos the

first video in our second module which is python basics for machine learning so in our machine learning course the first

module we have discussed about the machine learning basics and this module is all about python basics for machine

learning and now let's see how we can access google collaboratory so to use google collaboratory you don't

need to install any software so you don't need to install any python software or other applications you just

need to have a good web browser so you know google chrome is better suited for this one so just go to

google and search google collab so here you will see this web page called as research.google.com so go to this

welcome to collaboratory so this is where we are going to do our python programmings in most of our projects in

this channel so here you can give this new notebook so you can also see this you know topics

here so your google drive will be connected to your google collaboratory account so you can access your google

collaboratory files from google drive as well so i create a new notebook from here so if you are starting new with a

project or with a program you can go to this new notebook and this will take you to the site

so this is the interface of google collaboratory so yeah so first of all let's change the name of

this file so you can see here it shows untitled zero so this is where you know this is the name of the file and let me

change this to google collaboratory basics

so we call this in a short form as google collab

so you can see this here i p y n b so p y and b means python notebooks there is

another type of a notebook files for this uh jupyter notebooks you can also download this google collaboratory files

so you can see this download option here so you can download this as either python notebooks or dot py so dot py

means python files so you can open this uh python notebooks in your jupyter notebooks as well so after this we need

to connect our system so you can see this connect option here so google collaboratory is basically a

cloud-based you know application where we can run python programs so what happens is when we connect our system

our you know python environment will be connected to google's backend server and that is you know in that service our

codes will run so this is how it works and you will be allocated a ram and a cpu so you can check the details of your

ram here so it says 12 gb of ram and we have about 100 gb of storage so 12 gb of ram is really good for us so good for

doing several machine learning and the deep learning projects so now let's see what are the different features of this

so you can add text in this google collab so this is called as a cell okay so in

this cell we run our ports so you can also create text here so in this text you can give the description about your

code so here i'll just give the code like now we are going to check the

specifications of the system allocated to us so i'll just mention this as

system specifications okay system specifications and uh in order to run

one cell and go to the next one you can press shift plus enter so

here let's see the system allocated to us for this i'm going to use a system

command which is cat forward slash proc slash cpu info so basically you know google collab runs on

unix so you can run unix commands here and whenever you you are running a system command we need to proceed it

with an exclamatory mark okay if we are running python programs we shouldn't include any explanatory mark

so that is one important thing to note here so when you run this cpu info it will tell us the details of the cpu

allocated for us so you can see you see the processor details here so you know we have this intel xenon processor and

this is our first processor with index 0 and this is the second processor and you can go through all the details here

and then you can also check the ram allocated to you by using this command which is

that proc slash mem info

okay so let's run this so you can press this which is run cell or you can press shift plus

enter to execute the save okay so you can see here 13.3 which is about 13.3 gb of ram allocated to us okay so

this is how you can check the system specifications now

you can go to this files option here so in this files there will be an option called as upload

to session storage so we can upload some files here so this is a example of a data set file which is you know boston

house price data so i'll upload this to my google collaboratory environment so this will upload this file here so

you can use this upload to session storage or you can right click here so there will be this upload option okay so

you can click this to view the file in a preview here so this will give you the preview of the file and this is how you

can upload a file to your google collaboratory so in machine learning we often deal with data sets right so this

is how you can upload a file to your google collab and to start working so there will be another option called as

mount drive so if you give this mount drive your drive will be connected to your google collab and you can access

all the files in your google drive from your google collaboratory so the importance of this is in some cases we

may need to work with data sets that is large so that data sets with size of about 1gb or 2gb or even more than that

so in that cases uploading it to google collaboratory takes a lot of time so in that cases what we do is upload it to

our google drive account and uh we need to mount this drive after that and then we can access the file from google

collaboratory like this so this is how you can access the files from our drive to your google collaborate okay

so the interesting thing and very important thing in google collaborated is that

most of the libraries in python most of the machine learning and data science library in python are already

pre-installed and we don't need to install it separately so you know one or two libraries may be missing and now

i'll show you how you can install libraries in google collab so i'll just make a text here as

installing libraries let's say that we want to you know install pandas library so we know that

pandas library is used in python to make data frames right so if you want to download or if you want to install any

libraries in collab just go to google and search as pandas pypa so pypa means python package installer so you can see

this pandas pypi so this is the command to install the library pandas so you can copy this library

copy this command come to your google collaboratory paste it and you need to precede it with exclamatory mark as this

is a system command so let's run this shift press enter so here you can see your requirement

already satisfied so that means the library is already installed in our google collab environment so some of the

libraries may not be installed so in that cases this is how you can you know install your libraries and

now we can just import our library pandas as import pandas pd so this is how you import your library

and after you have imported it you can load the dataset file to a pandas library so i'll just copy the path so i

just i'm just giving you a demonstration on how you can run python programs so this is an example of you know python

syntax right so i have copied this path now let's load this file to a pandas data frame so i'll name this data frame

as df so df is equal to pd dot read csv

okay so inside this parenthesis in the codes we need to paste the path of our data set file okay

so this pd dot tcs we read csv function will read the csv file and unload it to a pandas data frame here i have uh

imported the pandas library in a short form as pd and now i am using it in a short form so that's why we are

importing it as pd so i'll run this now it will load the data from this csv

file to a data frame so here you can see a data set file is a csv file which means comma separated values now you can

you know print the sample of this data frame using df.get so this dot yet function will print the first five rows

of our data frame so this is how you can run python program so these are all python programs importing pandas and

loading into a data frame etc so let's run some simple uh programs like print we know that print is the key word right

for python so in the coming videos i have explained about several python programs and what are the important data

types and other things that are important for us to know in python for machine learning so let's try to print

something so i'll print machine learning shift plus enter okay so that will print your line so this is how you can use

google collaborating and you know that we have already uploaded this file right let's say that we want to print all the

files that are present in this environment so for that we need to you know type

exclamatory mark ls this will list all the files that are present in our environment so you can see here boston

house dot csv sample data so these are the files and folders we have in our in this files section so

this is how you can print this files so this is a basic introduction on what is google collaborating and how you can run

this in the previous video we have seen how to use google collaboratory for python

programming in this video we are going to discuss about the most basic concepts in python such as constants and

variables data types print function and input functions okay so if you have any doubts on how to use google

collaboratory you can watch the previous video so the index of the that video is 2.1 okay let's get started

so when it comes to machine learning and data science two programming languages are widely used

they are python and our programming language so r is mostly statistical based against python is preferred over r

because python is a general purpose language where we use it in other applications also such as web

development and other cases it also has several ready-made libraries for machine learning and it is also very easy to

understand and easy to use these are the reasons we use python okay now let's try to understand the basic

concepts in python so i have connected my google collaboratory

so first let's discuss about the print function so i'll make a text here

so i will be giving the link for this collab file in the description of this

video so you can download it from there and uh once you complete watching this video

do practice these codes in google collaboratory okay so print function print

so if you are an engineering student in your first year in your c program class you would have came across the function

printf okay so in c program this printf function is used to you know print some text in your screen okay so print some

message on the screen so this print is also similar to that okay so in c program we use the keyword

printf in python we use the keyword print okay so now let's try to print a string

let me print machine learning so now we are going to print the text machine learning okay so as you can note here i

have enclosed this uh two text machine learning in quotes right so you can use either codes

either double quotes or single quotes okay so all the strings in your code should be enclosed in double quotes

strings are nothing but text and sentences okay so either you can use double quotes or

single code but you cannot start with a single quote and end with a double quote like for example you can use single

quote here and a single code here okay so this will print that text in your screen

or you can use double quotes so

this tells our python interpreter that this is a string okay but you cannot use single quote and

endless end it with a double quote this will throw a error as you can see here so we have to use

either single quote or double quote so to run this particular cell you need to press

ctrl plus enter or shift plus enter so shift plus enter will run this cells and automatically goes to the next cell

okay now let's see how we can join two text so print parenthesis

so again we have to put the text in quotes machine learning

plus projects okay so this will concatenate these two terms the case of the machine

learning and projects so concatenates means joining so as you can see here it prints machine learning

projects so this is how you can join several strings in a print function okay so you can note here that i have

given a space here if you just bring this without a space there won't be any space between them okay so that's why i

have made a space here so this is how you can join multiple strings okay now let's try to print some

numbers so i'll just type print parentheses on eight okay so

this will print the number eight so as you can see here i haven't enclosed it in codes because only the

strings need to be enclosed in quotes so this will print that number okay we can also

do some arithmetic operation so if you give print but inside the parenthesis if you give 8 plus 3

so what you will get so as you can see here if we use plus sign between two strings it will

join the two strings but in the case of integers if you put plus so it will add the two numbers okay so it will add the

two numbers and it will uh print the sum of those two numbers okay so these are some basic things we use spring for

apart from this error there will be a lot of places where we need to print something to uh in the screen so

sometimes uh we need to print the entire data in our screen and uh there will be several other cases where

this is very useful okay so these are some basic uses of a print function okay

let me put this in a single section okay so in order to make a section in this text

you just need to precede it with a hash so this will make a section and if you click this down arrow

it will enclose all these cells in this section okay so if you press this again so it will expand it

okay so we have five cells under the text print function

okay now let's discuss about some very basic data types

in python okay basic data types

so the three basic data types in python are integers so we

represent integers as int and floating points so floating points are nothing but

okay floating points are nothing but decimals and we have strings so strings are

represented by str so str represents strings which are nothing but text and sentences okay

so let me clear this cell so now let's try to understand about these

data types so apart from these data types there are also several other data types but these are the most basic ones

so example for integer is you know the numbers like 8 10 or 19 so things the your numbers without any

decimal points okay so let's make a

variable called as okay let's try differently type it okay so here what i have done is

i have used the keyword called as type and i have enclosed the number eight here so what happens is sometimes if you

are not sure about what is the type of the data you can use this type keyword and this will show you what

is the data type so as you can see here it shows int okay now let's try it with a floating point

so as i told you floating point are nothing but decimals so let me put 5.3 here

so it will show float and now let's try with strings so type

so i hope you remember that we need to enclose the strings in double quotes

so let's put english what is this type

so this is called as string okay so these are the most basic data types in python so

apart from this there are also other data types like list sets dictionary etc so we will be uh discussing about that

in a later video so but by now you just need to know about these three basic data types okay

now let's discuss about another topic which are constants and variables

i'll create another section for this data types okay

so we have two sections as print function basic data types now let's discuss about the

constant and variables so as the name suggests variables are something whose values can be changed but we cannot

change the values for constants okay so constant is constants are not much used in python but we have a lot of usages

for variables okay so now let's try to understand more about variables so

let me create a variable name as superhero okay so this is called as a variable and

let's give a name for this superhero variable let me put iron man here

so basically variables are like a container in which we store some value and these values can be changed in case

of variables but we cannot change these values for constants okay so that is the important point to note here as you can

see here here superhero is that container so superhero is that variable and iron1 is the value in that container

okay so now you can try to print this term superhero

so this won't print just superhero in your screen this will print ironman because we have given this value to this

variable okay so this will print ironman so we can use this as our variable name or

you can also use an underscore so if you have multiple words in a variable like you you want to

represent this as let's say marvel superhero marvel

superhero okay so if you run this you will encounter a error because the variables should be a single name or it

should be connected with underscore so let me try to run this

marvel superhero so this will throw a error because it is not a single word right so

if you want to have multiple words consider as a single word you just need to include

underscore between them so marvel underscore so now

you can print this so this will work fine okay so i'll print this

so we got iron man okay so as i told you for variables we can change these values okay now let's try to change that

so marvel superhero so i'm using the same variable name which is equal to now i am

mentioning captain america okay so let me print that print this node so marvel superhero so as you can see

here we can change the value of these variables okay so but in the case of constant we cannot change that

so these are about constants and variables we can also give

the values for multiple variables in a single line of code okay let's try to understand this

let me create variables as so you can also use digits in the variable names so i'm

using 01 but there should not be any gap like this so there should be no gap between them so i am creating 0 1

0 2 0 3 okay so we have three variables here the first variable is 0 1 second

variable is 0 2 and the third variable is 0 3 so we need to enclose the strings in codes

the first era will be iron man second will be let's say captain america

and third include dc superhero

batman okay so what happens here is

this 01 will take the value iron man and hero2 will take the value captain america and 03 will take the value

batman okay now let's try to print this print 01

and 0 2 and print

okay so as you can see here i am not using codes here because these are not strings these are variables we just need

to uh put codes for strings and not for variables so let me print this now as you can see here the hero one is iron

man and the name for hero2 which is a variable is captain america and hero 3 is batman okay so here you can just

include a space before or after after this come up or you can just leave it as such so there

is not any much differences in python so in some programming languages you would uh encounter some error but

python doesn't give errors for much spaces okay so if you run this code it will also give us uh the correct output

and we won't get any errors for this spaces okay now let's try to see

how we can give multiple variables the same data let's say for example let's take these variables

x is equal to y is equal to 0. so it is not that the variable should be a word it can also be let us okay so here we

are taking three letters x y and z x y and z is equal to let's say 23 and i'm going to print

all of them so print x print y and print

is it okay so this line will give the value 23 to x y

and z now we will get all this as 23 okay so this is how you

can give a single value to multiple variables and another important point to note here is you cannot just give a

capital x here and try to print a small x here okay so python is k sensitive and you have to use the same uh

you know either caps or small letters okay so python is case sensitive and that is

one of the important point okay so these are some basic things about

variables and constants now let's try to understand another function which is input

okay input function now

so in the previous code we have discussed about the print function okay

and now we are going to discuss about input function so what is this input function so

in c program you would have came across the function called as scanf okay so print is a an output command where

something you know some we get some output out of it and input is our scanf is an input

command where the user will give an input to it okay so let me try to explain it to you with an example so i'm

creating a variable called as number one so number one is equal to input so i'm using the keyword input

here so input so you need to open a parenthesis and let's say enter the first number

okay and let's say number two

is equal to input enter the second number

so what happens is this will ask the user for their input so

now we need to give the input to this command let's say i'm giving a number 23 so i'm pressing enter now we need to

give the second number let me also give it as 23 okay so

if you're aware of or if you have practiced c programs so you would have came across that scanf function and it

is very much similar to this input function okay so we use this input function where we need

to get some data or some value from the user okay so this is why input function is used now let's try to do some more

things now what we are going to do is we are going to get two numbers from the user

and we are going to add the two numbers and we are going to print the sum of those two numbers so i am creating

another variable called as sum so sum is equal to number one

plus number two okay so what happens here is so this will get two numbers from the

user and it will get the sum of these two numbers and now we need to print this sum

so print sum so what do you expect the output of the

code would be okay so if i'm giving the two numbers as 23 and 23 so the sum of these two numbers is 46 right but we

won't get it we won't get 43 as the 46 as the answer okay let's see let me give 23 as the first number

23 as a second number so we expect that these two numbers should be added and the output should be 46 but it won't be

the case as you can see here it is not the expected value so we get 2

3 2 3 as our output why is this case okay so this is one interesting thing to note here so what happens is

so when you use the input function it assumes that this value given by the user is a string okay it doesn't know

whether it is an in integer value or float value okay it just assumes that it is a string that is why

it uh let's say for example when we give 23 as the value it thinks that 23 is a string and the 23 is also a string and

it just try to concatenate the two two strings as we have seen in the print here so plus will join the two strings

so that's why it has joined these two numbers which is you

know strings for the for that program and it will print that number okay so we get two three two three

now how we can rectify this you can include in tier okay

so hint and another parenthesis and so this will

convert this string value to an integer now let's try to run this

i'm giving 23 again and 23 now as you can see here the output is 46

now so the important point to note here is so the input function will assume that the

value is a string and we need to convert this value to what it is actually okay so here we are expecting the value to be

integer so i am using this integer value so you can change the data type by using this uh method okay let's say for

example changing the data type in python

okay let me show you how you can change these data types

let's say that num is equal to 5

okay um now let's try to print the type of this

num so this will output that it is

an integer so as you can see here now what you can do is you can change this integer to a float

by mentioning that keyword and giving that variable name inside this

parenthesis so what happens is the value of num is 5 and this will create this 5 which is an integer value to a float

value okay and now let's try to print this number okay let me include that in the print

function itself as you can see here this integer 5 is converted to a float value 5.0 so this

is how you can change the data types in python and it is the same thing i have used here so i am converting a string to

a integer okay so these are some very basic things you need to know in python okay so let's do a fast recap now so

first of all we have uh seen some basic functions in print okay so how we can print strings so we have

seen that we need to include the strings in double quotes or single quotes so we have seen how we can join two strings

and how to print numbers and sum of two numbers inside the print function so and then we have

seen some basic data types of python which are integer float and string okay so then we have seen how to

find the data type of a value so we need to mention the keyword type here okay then we have discussed about constant

and variables so we have seen that we can change the value of a variable and we have seen how to

give multiple values for multiple variables and how to give the same value for multiple variables then we have

discussed about the input function which is used to get input from a user okay then we have discussed how we can change

the data type of a value okay so i'll give this collab file link in the description of the video in the next

video we will be discussing more basics of python like data types operators and other python and the second module of

this course is python basics for machine learning and this is the third video in this second module so let's get started

with today's video so i'll be doing my python programs in google collaboratory i have already made

a video on how you can access google collaboratory so you can check that out so let's get started so these are the

five data types in python okay so they are integer floating point complex boolean and string so these are the

basic data types we know that integer is a real number right so integer are numerical values and floating points are

decimal numbers and we know that complex number is something that has both real number and an imaginary number and

boolean is nothing but true or false values so it contains only two type of values one is true and the other one is

false and string is nothing but text or statements okay so these are the five basic data types in python apart from

these basic data types we have other complex data types such as list tuples etc so we will discuss about

that in a later video so these are the basic ones so let's get started and let's try to understand each of these

basic data types first let's discuss about integers so in python you can write comments

using you know this ash symbol okay so i'll make a comment here as integers okay

so integers i'm going to create a variable as a okay so in the previous video

with index 2.2 i have explained what is meant by variables so we are just declaring a variable here as c8 and a is

equal to 8 and now let's try to print the cl we know

that 8 is a integer right so let's print a so in order to run the cell you can

press shift plus enter so it will execute the cell and go to the next one so you can see here we have printed a

and it has printed eight because we have given the value for a as eight we know that eight is an integer right so let us

say that in some cases you are not sure what is the data type of a particular variable in that case you can use the

keyword type okay so type is a keyword in python which tells you the data type of a particular value here let's mention

a in this parenthesis so this will return uh what is the data type of va okay so

let's run this press shift plus enter okay here you can see here int which means in change represents integer

values right so this is how you can print integers values so you can give an integer value to a variable and you can

print it now let's discuss about floating points so i'll make comment here as floating point

and let's take the variable as b and let's give a value like 2.3 so we know that 2.3 is a decimal value so we know

that floating points are nothing but decimal values and now let's try to print this b

and let's also try to find the data type of b so type b okay so let's run this

so you can see here 2.3 so we have printer b whose value is 2.3 and we have found the data type of b as float okay

so short form for integer is in and for floating point its float now let us discuss about the third data type which

is complex numbers so complex numbers so we know that complex numbers has both

a real term and imaginary term so let's name this variable as c and i'll give a complex number as 1 plus

3 j here uh one is the real term and j is the imaginary term so j square will be equal to minus 1 so that is meant by

complex number where you know we can also use i so i or j is you know square of this j is equal to minus 1

that is the imaginary path and you can print c okay so let's also try to print the

data type of c okay so this prints the complex number which is one plus three j and also it

prints the data type of c which is complex so these are the first three basic data types in python as we have

discussed about these three data types there is another interesting thing that we can do we can convert one data type

to another okay so i'll mention a text here as conversion

of one data type to another so here i'm going to convert

two data types so let's try to convert an integer into a floating point and let's convert a floating point into one

integer so this will be integer to float

so let's name the variable as okay so i'll give some other variable name which is x so let's say that x is equal to 10

and let's try to print x first and let's also try to print the type of x the data type of x so i'll run this

okay so you can see here we have printed x the value of x is 10 and it is an

integer now let's try to convert this to a floating point so let's name this variable as y

and to convert integer into a floating point we need to mention the keyword float

and inside this parenthesis mention the term which you want to convert here x is an integer right and let's convert this

x into a floating point so you need to put x in this parenthesis so this will convert

okay one second so this will convert x to a floating point value and that floating point will

be value will be stored to y and let's try to print both y and the data type of y

so type y here you can see here now we get the

value as 10.0 so we know that 10 is an integer and 10.0 is a floating point or decimal value and we have successfully

converted this integer value using this keyword float and we have tried to you know print the data type of y and it

has printed float so this is how you can convert an integer to a floating point value now let's see how we can convert

a floating point value to integer okay so float to in and

uh i'll take the variable as x is x in this case let's give some random floating point value 5.88

and let's print x and also the data type of x so type

ex so the value of x is 5.88 and the data type is float now let's see how we can

convert this so y is equal to so previously we have used this keyword float right in this case we are going to

use the keyword in which will convert the value to an integer value so now mention x in this parenthesis okay so x

and once we have converted it let's try to print y

and also the data type of y so we can see here this 5.8 decimal value is now converted to an integer

value which is 5 and we have found the data type as integer so there is one main thing which you need to take note

of in in this case so when you convert this uh floating point into into an integer value it doesn't round the value

it doesn't convert this 5.88 to 6 it just removes you know this decimal values and it will give only this

integer value so this 5.88 won't be converted into six but this you know this 0.88 will be removed

and we will get only five okay so this is what happens when you convert a floating point to an integer

now uh we have the fourth data type which is boolean right so here i just mention a text as boolean

so as i have told you earlier boolean has two values one is true and the second one is false so i'll mention it

here true and false okay

now let's create the variable as ca and let's put a is equal to true so this is a boolean value okay so there

is another main thing here so this t should be in uppercase letters so it shouldn't be in lowercase letters so

this will give us an error so this is a predefined value so it should be you know as such it is in this case so the t

should be in upper case letter and let's try to print this a now so print

a and let's try to print the data type of a as well so type of a

sorry so type of a so this is printed as true and we got the data type as b so bolt represents a boolean okay so let's

create another variable as b and b is equal to false and

let's print b now also the data type of b

so you can see here so this is how you can uh you know assign a boolean value to a variable and you can print it and

find the data type of it and the final thing which we have is string right so

before that let's discuss one more thing about boolean so let's see where we can use these booleans so i'll create a

variable as ca and let's say that a is equal to 8 is you know let's say that 7 is

less than 3 so we are just you know checking whether 7 is less than 3 so you know that 7 is

not less than 3 so this is this statement is actually a false one right so this will return a boolean value when

you just compare two values so here we are checking whether it is less than 3 so we are checking whether 7 is less

than 3 when you run these kind of codes it will return a boolean value now you can try to print

a okay so let's also try to print the data type of a

okay sorry so p shouldn't be in uppercase letter

okay so you can see here 7 is not less than 3 so it will give a boolean value which is false and

we have tried to print print the value of a which is false and we have also tried to find the data type of a so

similarly i'll just copy this and let's change this symbol okay

so let's say that 7 is greater than 3 and this condition is true right so let's call

this as a condition so let's check whether this condition is true of course 7 is greater than 3 so this will return

the value as true so let's see so yes so true and it is a boolean value so we use these kind of uh

you know conditions in loops and in the case of if statements etc so in that cases uh we will get a boolean value so

this is one main application of boolean data type and the final data type which we are going to discuss is string

so strings are nothing but text and statements uh you know and let's try to print some statements

so i'll print machine learning okay so machine learning is a string so these

two words represents a string and let's try to print this so this will print you this string

the main thing about the string is the strings should be enclosed in ports okay so here i have used double quotes right

so instead you can also use single quotes so i'll just copy and paste this here

and replace this double quotes with a single quote and i'll run this this will give you the

same result okay so the main thing here is if you start with single quote it should in in you know single quotes as

well if you start in double quotes it should end in double quotes as well so the code shouldn't change but you can

use either double quotes or single quotes so let's discuss some basic operations in strings that we can use

so i'll give a variable name here as my string okay so let's say that my string is equal to

so as i have told you earlier strings should always be enclosed in codes whereas we don't have to enclose you

know values this integer values and floating point values in quotes so only the

strings should be enclosed in codes and here boolean is a different data type hence we do not enclose it in ports only

the strings should be enclosed in ports either single quotes or double quotes and i'll give the values the same

machine learning and i'll print this my string here we have

created the variable as my string and i am printing it

let's try to find the data type using the type keyword this type function so my stream

so let's run this now you can see here our string which is machine learning has been printed and we

found the data type of string so str represents string and let's try another thing

print hello and

okay so i'm using this symbol here this uh you know star symbol let's see what

happens we know that this represents multiplication symbol in python right so when you use this line of code and let's

try to print this what happens here is your uh string will be printed five times if you

mention five so if you mention four your string will be repeated four times so this is uh how you can replicate your

string using this line okay so now let's see how we can slice a string this process is called as

slicing i'll just make a text here as slicing okay

let's create a new string as let's name this variable as my string

and let's give the value as programming so this word programming has totally 11

letters okay and i'm going to slice this string slice means getting you know only a particular

portion of this string this particular word so let's print

my string but i don't want to print the entire thing i just want to print a part of this word

and you can mention the values such as 1 s 2 5 okay so let's understand indexing in python

so indexing or the numbering in python starts with 0. so the index of this first letter p is 0

and the index of the second letter r is 1 and 2 and so on so p will be 0 r will be 1 0 0 will be 2 and so on so 0 1 2 3

4 and it goes on and we can mention the letters based on their index so 1 means the second letter r right because the

indexing starts with 0 and 1 represents this second letter which is r and i want to print all the words from the first

index are to the fifth index five let's see which is the fifth index so p is the zeroth one so r o g r a

so uh zero one two three four and five so here is the index five so when you use uh this particular line 1s to 5

the values from this first index one will be printed all the way to this fifth index

but this fifth letter won't be printed this fifth index value which is va won't be printed so let's run this

and try to understand this okay so we got the values as rogr so this first index value will be printed which is r

and this second index value phi won't be printer so you will get the values from r o g r so this second

index value will be uh you know neglected so this is how you can slice a string so the main thing to note here is

the first value up to the last value minus one so here the value should be printed so i will

just make a note here so the values from index

1 to 5 minus 1 so 5 minus 1 is 4 right so

will be sliced will be sliced so in every indexing cases so this second index won't be

included so that is what you need to take note of in this case and there is another process called as step so we can

also you know slice the words having a step value here let's print

my string and my string and mention 0 to