[music]

Hello everyone and welcome to the

Agentic AI [music] course for beginners.

Your starting point into the new era of

intelligent autonomous [music] AI

systems. Agentic AI is transforming how

machines work. Instead of just

responding to [music] prompts, AI agents

can now reason, plan, take actions, and

work across tools to complete [music]

task on their own. From smart assistants

to automated research agents and

workflow bots, this is the next big

[music] shift in artificial

intelligence. In this course, you will

learn the core building blocks [music]

behind agentic AI from understanding

deep learning, LLMs and transformers

[music]

to exploring lang chain, rag, LLM ops

and the modern [music] agent frameworks.

Whether you're completely new to AI or

already exploring generative models,

this course makes the concept simple,

[music] practical and beginner friendly.

By the end you will understand how

agentic systems work, how to build basic

[music] agents and how to start your

journey in this fast growing fit. So

before we begin, please like, share and

subscribe to Edureka's YouTube channel

and hit the bell icon to stay [music]

updated on the latest content from

Idureka. Also check out Edureka's

[music]

agentic AI certification training. It is

carefully crafted to meet [music]

industry demands and prepare you for the

future of intelligent agents. You will

gain practical [music] skills in lang

chain rag LLM ops and more through live

instructorled sessions and hands-on

labs. Whether you're [music] a beginner

or a tech professional, this course

helps you master the concepts and

accelerate your AI career. So check out

the course link given [music] in the

description box below. Now let us get

started by understanding what [music]

agentic AI is.

Agentic AI is transforming industries by

allowing machines to learn, adapt, and

evolve independently. Similar to live

organisms.

Unlike traditional AI, these intelligent

agents investigate, optimize, and

develop solutions over time without

requiring direct human participation.

Recent advancements include OpenAI's

deep research, which automatically

analyzes massive amounts of data to

provide detailed reports, and Google's

Gemini 2.0, O which improves AI's

capacity to plan and reason across

different data types. Service Now's AI

agent orchestrator is transforming

enterprise automation by coordinating

many AI agents to address difficult

business concepts. As these systems

become more powerful, they have the

potential to unlock ideas beyond the

human imagination ranging from wind

turbine blade design to AIdriven company

management. Let's start with our first

topic. What is agentic AI? Agentic AI

denotes artificial intelligence systems

capable of autonomously executing

actions to attain designated objectives

unlike reactive AI which only responds

to the inputs. Agentic AI is proactive

capable of planning, adapting and making

decisions autonomously. So let's explore

deep into agentic AI and see its

capabilities. Agentic AI is a type of

artificial intelligence that exhibits

autonomous behavior, enabling it to take

actions and operate without continuous

human guidance. It is goal-driven,

actively working towards achieving

specific objectives rather than

passively responding to inputs like

reactive AI. And with advanced

decision-m capabilities, it can evaluate

multiple options, select the optimal

course of action based on current

conditions and acquired knowledge and

adapt its strategies dynamically in

response to unforeseen changes in its

environment. Moreover, agentic AI

demonstrates proactiveness by taking the

initiative to act rather than waiting

for external triggers making it highly

effective in dynamic and complex

scenarios. Now let us see its relevance

in the current AI market. When AI

systems can act autonomously to

accomplish predefined objectives, we

call that agentic AI, making it highly

relevant in the current AI market. Its

autonomy allows it to operate without

continuous human guidance, making

decisions and adapting dynamically to

achieve objectives. This capability is

complemented by its advanced problem

solving skills, enabling it to evaluate

complex situations, strategize and

respond effectively to challenges.

However, the growing adoption of agentic

AI also rises important ethical

considerations such as ensuring

responsible behavior, minimizing

unintended consequences and maintaining

transparency in its decision-m

processes. Now that you know about

agentic AI, so let us discuss how it

differ from other AI systems. Agentic AI

differs significantly from other AI

systems in its autonomy, decision making

and adaptability to achieve long-term

goals. Unlike reactive AI which performs

predefined task only when prompted such

as spam filters or image classifiers,

agentic AI takes the initiative and

operates independently. It also contrast

with the generative AI which focuses on

creating content like child GPT

generating text but it is not

goal-driven by combining autonomous

behavior, strategic decision making and

the ability to adapt dynamically.

Agentic AI stands out as a powerful

system designed to achieve specific

objectives in evolving environments. Now

since we know a bit of differences, let

us see the comparison between generative

AI and agentic AI. Generative AI and

agentic AI differ in several key aspects

that define their functionality and

applications. Generative AI is primarily

focused on creation, excelling in output

focused tasks such as generating text,

images or other form of content. Its

adaptability is limited as it relies

heavily on prompts for guidance and

lacks the ability to operate

independently. In contrast, agentic AI

emphasizes autonomy, making it

goal-driven and capable of dynamically

adapting to changing environments.

Unlike the prompt dependent nature of

generative AI, agentic AI is

self-directed, enabling it to take the

initiative and execute strategic task

effectively. These differences highlight

the complimentary roles of both AI types

in addressing distinct challenges. Now

let us see the impact of agentic AI on

various industries. Agentic AI has had a

profound impact across various

industries transforming operations and

solving long-standing challenges.

Autonomous logistics systems such as

those in Amazon warehouses have

significantly improved operational

efficiency by 30 to 40%. In healthcare,

AI enabled surgical robots like the

Davinci system have performed over 10

million less invasive procedures

worldwide, enhancing precision and

patient outcomes. Scientific

advancements have also been transformed

by systems like deep minds alpha fold

which successfully solved the decades

old protein folding problem. On a global

scale, the World Economic Forum predicts

that by 2025, AI will displace 85

million jobs while creating 97 million

new ones, reshaping the labor market.

And in the energy sector, AI powered

smart grids can reduce electricity waste

by up to 10%. Promoting greener energy

solutions. Additionally, over 90

countries are investing in AI enabled

military technology to modernize their

defense systems, showcasing the

strategic importance of agentic AI in

global security. Now, let us see the

applications of agentic AI. Agentic AI

is transforming various industries by

enabling systems to make autonomous

decisions, adapt to changing

environments, and achieve specific

goals. Autonomous vehicle powers

self-driving cars and drones to navigate

roads, avoid obstacles, and make

real-time decisions as seen with Tesla

autopilot and autonomous delivery

drones. In robotics, agentic AI allows

industries healthcare and exploration

robots to perform complex task

independently as demonstrated by Boston

Dynamics robots used in logistics and

rescue operations. Personalized virtual

assistants like Google Assistant and

Amazon Alexa leverage agentic AI to

predict user needs, manage schedules,

and execute task without direct

commands. And in gaming, adaptive AI

agents enhance the experience by

creating challenging humanlike opponents

such as Alph Go and AI boards into

realtime strategy games. In healthcare,

agentic AI supports personalized

treatments, accurate diagnostics, and

surgical assistance with examples

including AIdriven surgical robots and

systems for remote patient monitoring.

These applications demonstrate the

transformative potential of agentic AI

across diverse domains. Agentic AI is

making a significant impact across

various industries by enabling autonomy,

adaptability, and efficiency in diverse

applications. In finance, it powers

algorithmic trading systems and fraud

detection tools, optimizing financial

operations such as managing investment

portfolios and identifying fraudulent

activities. In smart cities, AI systems

manage energy consumptions, optimize

traffic flow and enhance public safety

with examples like smart traffic lights

adapting in real time and autonomous

energy grid optimization. In space

exploration, autonomous spacecraft and

planetary rovers such as NASA's Mars

rovers perform exploration task

independently. In education, AI powered

tutors like Carnegie Learning provide

personalized instruction by adapting to

individual learning styles. In military

and defense, autonomous drones and

surveillance system improves situational

awareness and decision making such as

AIdriven surveillance drones in defense

applications. Now let us see the

challenges and risk associated with

agentic AI. While agentic AI offers

tremendous potential, it also faces

several challenges and risk that must be

addressed to ensure its safety and

ethical deployment. So one key concern

is misalignment with human goals where

AI system may pursue objectives that

conflict with human intentions due to

poorly defined parameters or intended

unintended consequences such as

autonomous robot prioritizing efficiency

over safety. Ethical questions arise

regarding accountability and decision-m

demonstrated by the challenge of

determining who is responsible when an

autonomous vehicle causes an accident.

The complexity of decision-m in agentic

AI can also lead to a lack of

transparency making it difficult to

understand or explain its actions

particularly in sensitive fields like

healthcare or finance. Ensuring safety

and reliability is another challenge as

AI systems must operate effectively in

unpredictable environments such as

autonomous drones encountering extreme

weather or medical failures.

Additionally, agentic AI systems often

require substantial computational

resources making their deployment costly

as seen in advanced robotics and

self-driving cars. Security

vulnerabilities pose further risk as

autonomous systems could be targeted by

cyber attacks potentially leading to

harmful consequences like the

manipulation of autonomous vehicles.

Lastly, overdependence on AI may reduce

human oversight or lead to skill

degradation in critical areas such as

relying too heavily on autonomous

systems for medical diagnosis without

human validation. These challenges

highlight the need for robust design,

rigorous testing and ethical frameworks

to mitigate risk and maximize the

benefits of agentic AI. Now let's see

the future of agentic AI. The future of

agentic AI is set to be transformative

with advancements across various domains

influencing its deployment. Future

systems will exhibit increased autonomy

and adaptability, enabling them to make

a complex decisions in real time and

operate effectively in dynamic

environments without human intervention.

The integration of agentic AI with

advanced technologies like quantum

computing, IoT, the edge computing will

further enhance its capabilities

allowing for faster decision making and

realtime processing at the edge. These

systems will have the widespread

applications in sectors such as

healthcare where they will enable

autonomous medical diagnostics,

personalized treatment plans and robotic

surgery. Climate action with advanced

systems for environmental monitoring and

response and space exploration where

smart rovers and spacecraft will carry

out missions on their own. As these

technologies evolve, ethical concerns

and accountability will need to be

addressed. promoting the development of

regulatory frameworks to ensure

responsive AI usage. Additionally,

agentic AI will foster human AI

collaboration, enhancing productivity

and creativity in the fields such as

education, engineering, and research.

[music]

Imagine asking Chad GP for a poem and it

writes one instantly. Now think about an

AI assistant planning your entire day,

booking meetings, and even handling

emails without your constant input.

That's the difference between generative

AI which creates content and agentic AI

which acts with autonomy making

decisions. In 2025, as AI becomes more

than just a tool, understanding the

shift is very critical. Are we heading

towards just smarter chatbots or truly

independent digital agents? Let's break

it down through this video. To truly

understand the ship, let's first break

down what generative AI is. Generative

AI is a type of artificial intelligence

designed to create content, whether it's

text, images, music, or even code.

Instead of making decisions or even

taking action on its own, it focuses on

producing outputs based on the patterns

it has learned from the vast amounts of

data. At its core, generative AI models

use deep learning techniques like

transformers to generate new content

that resembles human created work. For

example, Chad GPT generates humanlike

text based on prompts. Midjenny and Dali

creates stunning images from simple text

description and GitHub copilots helps

developers suggesting code snippets in

real time. Generative AI has several

strengths. It enhances creativity and

productivity allowing artists, writers

and programmers to work faster and even

more efficient. It scales effortlessly

generating unlimited variation of

content in just few seconds. It also

adapts responses based on user input

making interactions feel more

personalized. But it also comes with few

limitations. Generative AI lacks

autonomy. It doesn't think or act on its

own. It only responds when prompted. It

has no real decision-m abilities and

cannot evaluate consequences or make

even independent choices. Additionally,

it can generate biased or inaccurate

content based on the data that it has

seen. While generative AI is powerful

for creating, it cannot act

independently. And that's where agentic

AI comes in. Let's explore what agentic

AI is. Agentic AI goes beyond just

generating content. It acts autonomously

making decisions and executing tasks

without the need of constant human

input. Unlike generative AI which can

only responds to prompts, agentic AI can

plan, adapt and take initiatives based

on goals rather than the specific

instructions. At its core, agentic AI

combines reasoning, memory, and decision

making to operate more like an

independent agent. It doesn't just

create, it analyzes, strategize, and

acts. Real world examples include

autonomous robots which navigates and

complete the task on their own. AIdriven

personal assistant like those managing

schedules, booking flights and handling

emails without human oversight. Even

self-driving cars which continuously

assess their environment and make

split-second driving decisions. Agentic

AI has its own strengths. It reduces the

needs for manual intervention automating

the complex workflows. It adapts to real

world conditions, learning and improving

over time. It can even handle multi-step

tasks that require planning, execution,

and adjustment. But it also has its own

challenges. Developing truly autonomous

AI requires significant advancements in

reasoning and adaptability. There are

certain risks including unintended

behaviors and ethical concerns around

AI, which makes independent decisions.

And unlike generative AI which focuses

on creativity, agentic AI is limited in

how well it can generate novel content.

So while generative AI creates and

agentic AI acts, the real powers comes

when these two work together. Let's see

the key differences between generative

AI and agentic AI. Generative AI and

agentic AI serve different purposes,

each with unique strengths and

applications. The key distinction comes

down to creativity versus decision

making. As previously discussed,

generative AI focuses on producing

content, whether it's text, image, or

code. It enhances creativity by

assisting writers, designers, and

developers. But it lacks true autonomy.

It only works when prompted and doesn't

make any decision on its own. Agent AI,

on the other hand, is designed for

interactions and execution. Instead of

just generating responses, it can

analyze situations, make decisions, and

take actions. While it may not create

content like generative AI, it can

manage workflows, automate task and

adapt to real world conditions. Another

key difference is user dependency.

Generative AI is entirely reactive,

meaning it requires human input to

function. It waits for prompts before

generating anything. In contrast,

agentic AI is proactive. It can initiate

actions independently, setting

reminders, optimizing schedules, or even

solving problems without human

intervention. The applications of these

AI types also differ. Generative AI is

widely used in content creating,

marketing, entertaining, and software

development. And agentic AI powers

autonomous system like self-driving

cars, AI powered customer service and

personal assistant that can handle

complex workflows. Both AI types are

transforming the industries. But when

they work together, they unlock even

greater potential. Imagine an AI that

not only generates a marketing campaign,

but also launches it, tracks engagement,

and refine the strategy automatically.

The future isn't just about choosing

between generative AI and agentic AI.

It's about combining them two to build

truly intelligent systems. Now that we

understand the key differences between

these two, let's explore the future of

AI by asking, will generative AI be

replaced? As AI continues to evolve, one

big question arises. Will agentic AI

replace generative AI? Right now,

generative AI is everywhere, helping

people write, design, and code faster

than ever before. But it has one major

limitation. It relies entirely on human

input. Agent AI on the other hand takes

things further. It doesn't just

generate, it decides, plans, and even

acts. It's the next step towards the

true autonomous intelligence. Does that

means generative AI will be obsolete?

Not necessarily. The future of AI isn't

about one replacing the other. It's

about coexisting. Generative AI will

keep getting more creative and even

sophisticated, producing even higher

quality content. Agentic AI will become

even more autonomous, integrating deeper

with industries like healthcare,

finance, and robotics.

But this shift does comes with some

risk. As AI takes on decision-m power,

we face new challenges. ethical

concerns, unintended consequences and

the need for accountability. If an AI

agent makes a bad decision, who is

responsible? And how do we ensure it

aligns with the human values? The answer

lies in balance. The real future of AI

is hybrid approach where generative AI

fuels creativity and agentic AI drives

intelligent action. Imagine an AI system

that not only writes a research paper

but also submits it to generals,

responds to reviews and refine it

automatically. And this is where we are

headed. Not just smarter AI, but AI that

truly works with us as both a creator

and an agent. The question isn't whether

agentic AI will replace generative AI.

It's how we'll harness both to shape the

future of intelligence. Now that we have

explored the differences between

generative AI and agenic AI, let's move

on to building an intelligent AI agent

that can interact with our database

using natural language. This means you

can simply ask a question like show me

all the students who have scored about

80 and the agent will automatically

convert it into an SQL query, fetch the

data and return the exact result from

the database. No need to write complex

SQL queries manually, just ask and the

AI response. Let's dive in and build

this powerful system. First, we need to

set up a cond environment to manage our

project dependency. To do this, we open

the terminal and run the following

command. We'll write create p vv

python equals to

3.10 - y.

So, creates a new environment and hyphen

pvnv specify the environment path as

VNV. Python equals to 3.10 installs

Python version 3.10 inside the

environment and hyphen y automatically

confirms the installation without asking

for approval. Once the process is

complete, our virtual environment is

ready and we can move forward with

setting up our agentic AI project. Next,

we'll create a file named

requirements.txt. txt

where we'll list all the necessary

libraries for our project. This will

help us easily install dependencies in

one go. Additionally, we'll create a NV

file to securely store our Google

generative AI API key, keeping sensitive

information separate from our main code.

With these files in place, we ensure a

well structured and organized setup for

our agentic AI project.

First, we will work with SQLite, a

lightweight self-contained database

engine to create and manage a student

database. Let's break it down step by

step. So, we'll create a file named SQL.

py and import the SQLite 3 module which

allows us to work with SQLite databases.

We'll write import SQLite 3.

This module provides all the necessary

functions to create a database, insert

records, retrieve data, and manage

connections. Next, we create a

connection to an SQLite database file

named student. DB. We'll write

connection equals to SQLite 3.

Connection equals to SQLite3.Connect

in the bracket

in double inverted comma student. DB.

If this file doesn't exist, SQL light

will automatically create it. The

connection object will allow us to

interact with the database. Now we

create a cursor object which is used to

execute SQL commands in Python. We'll

write cursor equals to

connection.cursor.

Think of the cursor as a tool that helps

us send queries to the database and

retrieve results. Now we define a SQL

command to create a table named student

with four columns. We'll write table

info equals to triple inverted commas.

Next we'll create a table.

For that we'll write create table. Then

student we'll write in the bracket name

type vcar and we'll have 25 characters.

Comma class type vcar and the same 25

characters. Comma section type var with

25 characters and marks type integer.

Then we'll write cursor.execute

in the bracket table info. The name

stores the students name string up to 25

characters. The class store the class's

name and the section stores the section

of the student and lastly the mark

stores the marks obtained as integer.

Executing this commands creates the

table in the database. Next, we insert

five student records into the student

table using SQL insert statements. I've

already created and inserted five values

in the table. You can create as much as

you can. Each insert commands adds a new

role with the students name, class,

section, and marks. Now, we retrieve and

display all records from the student

table. For that, we'll have to write

print in the bracket. Print in the

bracket the inserted records are. In the

next line, we'll write data equals to

cursor do.executed in the bracket three

single inverted comma select star from

student closing the inverted commas in

the bracket. Then we'll write for row in

data colon print in the bracket row. The

select star from student query fetches

all the data from the table. The for

loop iterates through the records and

prints them one by one. And finally we

commit our changes and close the

database connection. For that we'll

write connection.

And then connection dot close. The

dotcommit function ensures all the

changes are saved in the database. The

dot close closes the connection freeing

up the system resources. And that's it.

We have successfully created a student

database inserted records and retrieved

them using SQLite and Python. Now let's

build an interactive streamlit app that

converts natural language questions into

SQL queries using Google's Gemini model.

It then retrieves data from an SQLite

database and display the result. Let's

break it down step by step. But before

we start, we have to activate the

environment. For that we'll write

activate venv forward slash.

And here our environment is activated.

First we'll create a file named app. py

and load environment variables using

env. For that we'll write from env we'll

import load env. Next we'll write load

env. It will load all environment

variables. This ensures that sensitive

information such as API keys is securely

stored and accessed. Next we import the

necessary modules. For that we'll write

import stream lit as st. Then import OS.

Then import escalite 3 and then import

Google.generative AI as genai.

Streamlight here powers the web

interface. OS helps access the

environment variables. SQLite 3 allows

us to interact with the database and

Google generative AI enables the

conversion of natural language into SQL

queries. Now we configure the Google

Gemini API key. But before that we'll

have to create a API key through Google

studio itself. I've already generated

one. You can create yours through Google

studio itself.

Then we'll write genai.configure

in the bracket API_key

equals to os do.get env

key. This allows the app to use Gemini

1.5 Pro to generate SQL queries. Then we

define a function to generate SQL

queries from natural language input

using gemi. For that we'll write defaf

get_jemni

response in the bracket question,

prompt. Next we'll write model equals to

genai, generative model in the bracket

we'll write models/jna

version 1.5 pro. Then we'll write

response equals to model.generate

generate underscore content in the

bracket and in square brackets prompt in

the square bracket zero and comma

question and then we'll write return

response text. The function initializes

the Gemini model. It takes a question

and predefined prompt as input and the

AI model generates an SQL query as

output. Next, we define a function to

execute SQL queries on the database and

retrieve results. For that we'll write

def read_sql_query

in the bracket sql comma db. Next we'll

write con equals to skqite 3 dot connect

in the bracket db. Then cur equals to

con.cursor

and then cur equals to execute in the

bracket sql. Then we'll write rows

equals to cur do fetch call. then con

dot commit and then con.t close

and then we'll create a loop by writing

for row in rows and then we'll print it

and then return rows. The function

connects to the student db database. It

executes the given SQL's query and it

fetches all the retrieve records and

prints them. Now we define the AI prompt

that instructs Gemini on how to convert

the questions into SQL queries. As you

can see, I've already created a prompt

for my own and you can create yours

according to how you want your model to

function. If you want the prompt which

I've used over here, you can just

comment on the video and I'll send it to

you. This prompts ensures the Gemini AI

generates SQL queries accurately without

unnecessary text. Now we'll set page

configuration with a title and icon. For

that we'll write st set_page

configuration in the bracket page title

equals to SQL query generator edurea

comma page icon. Then we'll display the

edureka logo and header. For that we'll

write st dot image in the bracket

123.png png comma width equals to let's

keep it as 200

st dom markdown in the bracket logo plus

ederica's gemini app/ your AI powered

SQL assistant

next we'll write next we'll write

st.mmarkdown

then the logo and ask any questions and

I'll generate the SQL query for you the

page title and the icon are set a logo

is displayed at the top and the app's

purpose is to introduce to the user. And

before we import the logo, just make

sure that you have the logo in your

folder. We take user input for a natural

language query.

For that, we'll write question equals to

st.ext_input

in the bracket enter your query in plain

English colon, key equals to input. This

allows users to type their questions

such as show all students with marks

above 80. A submit button triggers the

SQL generation process and for that

we'll write submit equals to ST dot

button in the bracket generate SQL

query. When clicked the app processes

the query and retrieves the result. Now

we define what happens when the submit

button is clicked.

For that we'll write if submit in the

next line response equals to get gemini

response in the bracket question,

prompt. This is to convert the question

to SQL. And then we'll print the

response.

Then we'll write response equals to

read_sql_query

in the bracket response, student db. And

this is to execute SQL on the database.

Then we'll write ST dos subheader. In

the bracket the response is brackets

close.

Next we'll include a loop for then row

in response. Then we'll write st. dot

subheader in the bracket the responses

and then we'll include a loop for row in

response. Then we'll print row and then

st dot header and in the brackets row.

The user's question is converted into an

SQL query using Gemini AI. The SQL query

is executed on the student DB database

and the retrieve records are displayed

on the streamllet app and that's it. The

AI powered streamllet app allows users

to ask natural language questions which

are automatically converted into SQL

queries and executed on a student

database. Now let's open the terminal

and run our streamllet app. To do this,

we simply type streamllet run app. py

and hit enter. It's running. And as you

can see, our agentic AI is up and

running, ready to interact with our

database. Let's test it by asking a

simple question.

We'll ask, give me the names of all the

students. The AI processes our request,

converts it into an SQL query, and

retrieves the student names from the

database. Perfect. As you can see, the

response is generated. Now, let's try

another query. We'll say, give me the

average of marks.

And just like that, the AI calculates

and returns the average marks. The

response which is provided is 72.2. So

in this video we successfully built an

agentic AI that can understand natural

language, generate SQL queries and

interact with our data seamlessly.

Think about this. Instead of you doing

all your work, you have a machine to

finish it for you or it can do something

which you thought was not possible. For

instance, predicting the future like

predicting earthquakes, tsunamis so that

preventive measures can be taken to save

lives, chat bots, virtual personal

assistance like Siri in iPhones, Google

Assistant and believe me, it is getting

smarter daybyday with deep learning,

self-driving cars. It will be a blessing

for elderly people and disabled people

who find it difficult to drive on their

own. And on top of that, it can also

avoid a lot of accidents that happen due

to human error. Google AI eye doctor. So

this is a recent initiative by Google

where Google is working with an Indian

eye care chain to develop an AI software

which can examine retina scans to

identify a condition called diabetic

retinopathy which can cause blindness.

AI music composer. Who thought that we

can have an AI music composer using deep

learning? And maybe in the coming years

even machines will start winning

Grammys. And one of my favorites, a

dream reading machine. With so many

unrealistic applications of AI and deep

learning that we have seen so far, I was

wondering that whether we can capture

dreams in the form of a video or

something. And I wasn't surprised to

find out that this was tried in Japan a

few years back on three test subjects

and they were able to achieve close to

60% accuracy. And that is amazing. But

I'm not sure that whether people would

want to be a test subject for this or

not because it can reveal all your

dreams. Great. So this sets the base for

you and we are ready to understand what

is artificial intelligence.

Artificial intelligence is nothing but

the capability of a machine to imitate

intelligent human behavior. AI is

achieved by mimicking a human brain by

understanding how it thinks, how it

learns and work while trying to solve a

problem. For example, a machine playing

chess or a voice activated software

which helps you with various things in

your phone or a number plate recognition

system which captures the number plate

of an oversp speeding car and processes

it to extract the registration number

and identify the owner of the car so

that he can be charged. And all of these

wasn't very easy to implement before

deep learning. Now let's understand the

various subsets of artificial

intelligence. So till now you'd have

heard a lot about artificial

intelligence, machine learning and deep

learning. However, do you know the

relationship between all three of them?

So deep learning is a sub field of a sub

field of artificial intelligence. So it

is a sub field of machine learning which

is a sub field of artificial

intelligence. So when we look at

something like Alph Go, it is often

portrayed as a big success for deep

learning, but it's actually a

combination of ideas from several

different areas of AI and machine

learning like deep learning,

reinforcement learning, self-play, etc.

And the idea behind deep neural networks

is not new, but it dates back to 1950s.

However, it became possible to

practically implement it only when we

had the new high-end resource

capability. So I hope that you have

understood what is artificial

intelligence. So let's explore machine

learning followed by its limitations.

So machine learning is a subset of

artificial intelligence which provide

computers with the ability to learn

without being explicitly programmed. In

machine learning, we do not have to

define all the steps or conditions like

any other programming application.

However, we have to train the machine on

a training data set large enough to

create a model which helps the machine

to take decisions based on its learning.

For example, if we have to determine the

species of a flower using machine, then

first we need to train the machine using

a flower data set which contains various

characteristics of different flowers

along with the respective species. As

you can see here in the image, we have

got the sele length, sele width, petal

length, petal width, and the species of

the flower too. So using this input data

set, the machine will create a model

which can be used to classify a flower.

Next, we'll pass on a set of

characteristics as input to the model

and it will output the name of the

flower. And this process of training a

machine to create a model and use it for

decision making is called machine

learning. However, this process had some

limitations. Machine learning is not

capable of handling highdimensional data

that is where input and output is large

and it is present in multiple dimensions

and handling and processing such a data

becomes very complex and resource

exhaustive and this is termed as the

curse of dimensionality.

So to understand this in simpler terms,

let us consider a line of 100 yards. And

let us assume that you dropped the coin

somewhere in the line. You'll easily

find the coin by simply walking on the

line. A line is a single dimension

entity. Now let's consider that you have

got a square of side 100 yard each and

you dropped a coin somewhere inside the

square. Now definitely you'll take more

time to find the coin within that

square. A square is a two-dimensional

entity. Now let's take it a step ahead

and consider a cube of side 100 yards

each and you dropped a coin somewhere

inside the cube. Now it is even more

difficult to find the coin. So if we see

that the complexity is increasing as the

dimensions are increasing and in real

life the highdimensional data that we're

talking about has got many dimensions

which makes it very very complex to

handle and process. The highdimensional

data can be easily found in use cases

like image processing, natural language

processing, image translation etc. And

machine learning was not capable of

solving this use cases and hence deep

learning came to the rescue. So deep

learning is capable of handling the

highdimensional data and is also

efficient in focusing on the right

features on its own and this process is

called feature extraction. Now let's try

and understand how deep learning works.

So in an attempt to re-engineer a human

brain, deep learning studies the basic

unit of a brain called a brain cell or a

neuron and inspired from a neuron an

artificial neuron or a perceptron was

developed. So if we focus on the

structure of a biological neuron, it has

got dendrites and these are used to

receive inputs and these inputs are

summed up inside the cell body and using

the axon it is passed on to the next

biological neuron. So similarly a

perceptron receives multiple inputs

applies various transformations and

functions and provides an output. As we

know that our brain consists of multiple

connected neurons called neural network.

We can also have a network of artificial

neurons called perceptrons to form a

deep neural network. Let's understand

how a deep neural network looks like. So

any deep neural network will consist of

three types of layers. the input layer,

the hidden layer and the output layer.

So if you see in the diagram, the first

layer is the input layer which receives

all the inputs. The last layer is the

output layer which gives the desired

output. And all the layers in between

these layers are called hidden layers.

And there can be n number of hidden

layers thanks to the high-end resources

available these days. And the number of

hidden layers and the number of

perceptrons in each layer will be

entirely dependent on the use case that

you're trying to solve.

And there is mechanics to decide the

number of hidden layers. However, we'll

not get into that in this session. Now,

since you have a picture of deep neural

network, let's try to get a highle view

of how deep neural network solves a

problem. For example, we want to perform

image recognition using deep networks.

So, we'll have to pass this

highdimensional data to the input layer.

And to match the dimensionality of the

input data, the input layer will contain

multiple sub layers of perceptron so

that it can consume the entire input.

And the output received from the input

layer will contain patterns and will

only be able to identify the edges and

images based on the contrast levels. And

this output will be fed to hidden layer

1 where it will be able to identify

various face features like eyes, nose,

ears, etc. Now this will be fed to

hidden layer 2 where it will be able to

form the entire faces and sent to the

output layer to be classified and given

a name. Now think if any of these layers

is missing or the neural network is not

deep enough then what will happen?

Simple we'll not be able to accurately

identify the images and this is the very

reason why these use cases did not have

a solution all these years prior to deep

learning. So just to take this further,

we'll try to apply deep network on an

MNEST data set. So the MNEST data set

consists of 60,000 training samples and

10,000 testing samples of handwritten

digit images. And the task here is to

train a model which can accurately

identify the digit present on the image.

And to solve this use case, a deep

network will be created with multiple

hidden layers to process all the 60,000

images pixel by pixel and finally will

receive an output. So the output will be

an array of index 0 to 9 where each

index corresponds to the respective

digit. So index 0 contains the

probability of 0 being the digit present

on the input image. Similarly, index 2

which has a value of 0.1 actually

represents the probability of two being

the digit present on the input image. So

if you see that the highest probability

in this area is 0.8 which is present at

seven index of the array. Hence the

number present on the image will be

seven. So this is how the handwritten

image processing happens. Let me

practically execute this use case for

you. So this is my PyCharm IDE. First of

all, let me show you the data set. So

this is my emnest data set and it has

got four GZ files which gets extracted

when my program gets executed. Now the

program or the deep neural network using

which I was able to create a model to

process all these images and train my

machine is this create model_2.

py and it's a good lengthy program. So

I'll not be explaining the entire

program for you. But let me tell you the

technology or the framework with which I

was able to implement this. So I've been

using TensorFlow which is one of the

open-source Google libraries for deep

learning. And right here I have imported

TensorFlow and then I'm using this Mnest

data and finally going ahead and

creating a deep neural network. So these

all things are here. It is creating a

deep neural network and the hidden layer

that is required to process all these

images. And finally, I'm creating a

model and I'm saving this model with

this name right here, model 2. CKpt.

Now, if I run this code, it is going to

take a very long time. So, give it some

time.

It has extracted all the files and it

has started its training. It is at step

zero now. So in order to completely

train this model, it is going to take

20,000 steps. Let me show you in the

program as well. So here it is. So in

here I've set the steps to 20,000. But

you can always configure it to a number

that is,000 2,000. However, you'll have

to run this code for n number of times

so that you can achieve a particular

accuracy.

So after executing for 20,000 times what

happens is a model is created with an

accuracy of 92%.

So what does it mean? It means that if

you pass a particular image out of 100

images of the model 92 predictions will

be correct. 92 of times this model will

be able to tell you the exact number

that is present on the image. Let's now

wait for this program to execute

completely otherwise we have to wait for

ours. So I've already executed it once

and the model is already created. So

what is a model? A model is nothing but

a set of files and these three files

along with the checkpoint files. Now

there is another code which is

predict_2.

py in which I'm restoring the model and

let me show you the line where I'm

restoring it.

So it's right here saver.restore

model 2.CAP.

So this is the name of my model. So I'm

restoring my entire model that was

created after training of 20,000 steps

on MNEST data set and I'm passing an

image that is 7_o.png.

So it is in this folder in the test

folders. I've got other images and I've

got here 7_o.

Now this is an image. It's the name of

the image. I'm not telling the program

what is the number. So I'll just stop

this training now and now we'll execute

the prediction part where I'm restoring

the model and this model will tell me

the number the handwritten number that

is there in the image. So this was the

image 7 O and the prediction for this

image is 7. So my model was accurately

able to identify or predict the number

that was there on the handwritten image.

So let me change the image now and let

me execute this again just to show you

the image.

All right, there's one good question

that I would like to take. So Akil asked

that how are you saving the weight for

the neural net? Can you show us? Sure,

Akil. So in the previous file that I

executed, I showed you that I'm using a

object called saver. And using the saver

object I can save the entire model and

weights are also automatically saved

along with this model in the checkpoint

file. So using checkpoint we can

actually reach the final state of the

training and then we can use the

prediction model. I hope that is fine.

All right. So if you see this seven,

this is a handwritten image. This is

somebody who writes seven like this with

a strike in between. And now I'm passing

a different image of 7. It's 7_1.

So it's different from the first one.

And I'll run this code again.

Now this time the seven is different.

And my machine learning model should be

able to predict that this is a seven

because people write seven in different

ways. Somebody likes seven like this.

Somebody writes seven and makes it look

like a one. They make the top part very

small. So there are different ways of

writing seven. So however a machine

learning model should be capable enough

to find out that as well. So let me just

close this and see the prediction.

Our model was able to predict the seven

as well and predict the value is seven

as well. So let me execute it again for

you.

So both the sevens were different but

still the prediction is correct for both

of them.

So now let us go back to our

presentation. So after the MNEST

application, let me show you a few more

applications of deep learning. The very

first is face recognition. Let me give

you an example. So all of you are using

Facebook and you do spend some time on

it. So if you remember a few years back

when you used to upload pictures with

your friends, it makes a box around a

human face with a box appearing at the

bottom to ask you to type the name of

the person to tag him. So it was able to

identify that it was a human face. But

now it is able to autotag. It is not

only able to detect faces but also

identify who it is. And how is this

possible? It is only possible using deep

learning. And Facebook also has a deep

learning library called cafe 2 using

which they have applied all these

things. The next use case that is

implemented using deep learning is

Google lens. This is one of those

applications that has been recently

launched for smartphones by Google. What

does this app do? You just have to

install it, open it, point your camera

on a particular thing like this flower

over here and in real time image

processing happens and Google will get

back with the entire details of the

object like the name of the flower where

it is found etc etc. So if you point it

at a building or any shop it will tell

you what kind of a store it is. If it's

a restaurant it will show you reviews

the ratings menu etc. So what is

happening here is that in real time you

are able to use a deep learning net and

get all the information you want and

these applications are really amazing

because it directly brings deep learning

to the end users or the common people.

So they can easily use the benefits of

deep learning without worrying about

what is happening at the background. The

next use case is the machine

translation. This is again a very

important use case and there is also an

app in play store and this is called

translation app. So here is an image

that says more chocolate.

I don't know what it means. I don't even

know which language it is. But with this

app what you can do is that you can

capture the picture of the packet and

this app will first detect the text in

the image then extract the text like

this and then translate it for you. So

for example it has detected the text

extracted it like this and here it has

translated morg which means dark and

then it writes back again on the image.

So what is happening in this particular

use case is that first an image is

captured. Image processing takes place.

Text is extracted through processing and

once we have the text we translate it to

the desired language which is English in

this case and then again image

processing happens where we are writing

the text on an image again. So more

chocolate means dark chocolate. So this

is a really great use case because this

is a combination of multiple learning

algorithms like CNN and RNN.

[clears throat] So these were a few more

applications of deep learning. I hope

that you found them interesting.

[music]

So first thing which comes to our mind

there have been lot of emphasis on this

term called artificial intelligence. So

let's first try to understand that what

is artificial intelligence on a very

high level and why we may need it in

first place for solving a problem. So

let's try to understand with an example.

Person goes to a doctor and he wants to

get checked at whether he got diabetes

or not. And what doctor would say is

okay there are some tests which you need

to get done and based on the test

results doctor would have a look and

from his experience from his studies and

previous examples the patients he had

seen he would be able to evaluate the

reports and say that the patient has

diabetes or not. So if you just take a

step back and think I said the doctor

has experience. So what do we mean by

experience? The doctor has learned what

are the characteristics of somebody

having diabetes. Will it be possible if

we can provide this experience in the

form of data to a machine and let

machine take this decision whether a

person has diabetes or not? So the

experience which doctor learned through

his studies and his practice what we are

doing is we are taking customers data

who have with different reports and

different parameters on different things

like the glucose count in blood or the

weight and height and all these

parameters about a human being and based

on that we have fed it to a machine and

tell that what are the characteristics

of a person who has diabetes and from

this let's say we have 1 million

customers data we have given to a

machine and let machine do this stuff

from his experience which comes from the

data or historical data to be precise

and do the same task which a doctor is

doing. So what we have done is if you

see from this example what an artificial

machine artificial intelligent machine

is doing that it's learning from the

historical data and trying to do the

same thing which an experienced and

intelligent doctor was doing

this kind of area or domain activities

which human beings were doing. If we can

make machine intelligent enough to do

the same task, why we should create

these artificial intelligence

on a very high level. There may be a lot

of points but if you just discuss points

that human beings have limited

computational power and we guys may be

good in terms of classifying things like

you know you can see a friend in a group

photograph and easily can say who is

your friend and who are others. You can

easily listen to a language and

comprehend what a person is doing. But

human beings are not very good in doing

lot of mathematical. If you try doing

good amount of mathematical computations

probably not very easy and second is

that it's not possible for human beings

to work continuously let's say 24 by 7 a

day for 30 days continuously if we can

make a machine do such stuff one they

would be able to kind of do these

computations very fast and like we spend

a good amount of time in discussing the

GPUs and I also mentioned that Google is

talking about a TPU machine transfer

processing units which would be hugely

changing the entire paradigm of

computations and machine would become

more and more competitive or even better

than human beings in some of the fields.

This is the formal definition but if I

loosely translate it's basically that

artificial intelligence machines are

those machines which can do tasks which

human beings can easily do. So things

like identifying what's written let's

say in a license plate or playing games

and I'm sure some of you would already

heard that machine have defeated the go

champion and the chess players. uh now

we have digital agents like Siri and

others which can understand what we want

them to do and can take intelligent

decisions from the text or from the

voice itself. Basically these are very

high level and some of the fields where

deep learning is made great inroads

something like game playing expert

systems self-driving cars robotics

natural language processing so there may

be different and new areas where we are

implementing all of you know that

everything every experience of human

beings is getting digitalized the kind

of things you buy kind of things you

watch and what your preferences are who

you like on Facebook who you don't like

what kind of movies you like and all

these things in terms of reviews being

captured online.

>> So once your data is going and captured

online there are systems which can

analyze this data. So given this huge

data generation as well as now we have

machines which can process it and make

some intelligent decisions are

available. So that's why you will see

there have been lot of emphasis now in

last couple of years lot of new things

are coming in. Some of you who have been

reading these papers on different

subjects, different architectures would

know that most of these architectures

are not very old.

>> It's a very dynamic field. Every day in

fact, on a weekly basis, you will be

hearing about a new API or a new kind of

architecture being developed by

somebody. Most of the stuff we will be

studying in these classes are not very

old like convolution neural network and

recurren neural network. Some of their

variants are as new as as last year. If

you guys follow TensorFlow closely, they

introduced a library called object

identification. Object identification,

object detection API which TensorFlow

has made available for everybody. You

would be able to see to yourself that

this API works. There are five different

options of selecting different deep

learning architectures or convolution

neural networks for this API. But it's

been able to identify human beings and

all other 90 objects there with almost

99% accuracy. In some cases from even

human beings would be finding it

difficult to kind of see and predict

what the object is. But this machine has

gone even beyond a human capability in

terms of identifying sending emails.

Given that we have a fair understanding

or very high level understanding we

haven't got details of artificial

intelligence basically from a loose

understanding that artificial

intelligence of making decisions or

machine making decisions which human

beings were earlier doing the task

something like

language and driving of car let's

understand how this machine learning and

deep learning related to artificial

intelligence given this learning that

now your machine is able to understand

and learn from the data. We can solve

multiple business problems with the help

of this.

>> So let's take a very small example. It

was like whether a person has diabetes

or not. And I was mentioning that this

kind of decision being taken by the

doctor based on the reports he has got

and these reports have some numbers like

number of time a patient a particular

kind of issues or what is the glucose

count and what is BMI and what the

person's a and bas of these numbers the

doctor was able to make this kind of

decision. We can take the same analogy

where we were trying to predict which

species of FL it is. We can take

information of patients and different

attributes on different features of a

report and the patient and the machine

would be able to learn from these data

sets and for a new customer or a new

patient it would be able to classify

whether the patient has diabetes or not.

So there are two sections of it. First

one is the information about the patient

and different characteristics of his

health. So from this which is number of

time and glucose count till age. These

are the information points about the

patients and the last column is the

information whether the patient has

diabetes or not. this kind of problem

where we have some information points

where they explain what the situation is

and other in the last column or the

information of output is in some kind of

classes. There's a specific type of

machine learning problem it is but as of

now the characteristic is that we have

some information about the patient and

the last column is telling me whether

the patient has diabetes or not. So what

a machine basically learns it that it

learns all those rules in the example

which I was quoting that earlier cases

people used to create these handcoded

rules to predict whether an event will

happen or not. But in machine learning

your [laughter] algorithm will learn

from the historical data and see what

are the combinations which decide

whether a patient has diabetes or not.

And these combinations would be of

something of this type. It is only for

illustration. It's not the real numbers

but it's for illustration that your

machine or your machine learning

algorithm has been able to identify

these rules based on the historical

data. So after learning it has created

the glucose count is less than 99.5.

If yes [snorts] then go to next one. If

no the person does not have to face if

glucose count

the person has diabetes and if no then

there are further drill down of rules.

So all these combinations or rules are

dynamic in nature and what I mean is

that these rules would be changing if

your data says changes and you can take

the same model can do the work whether a

patient has diabetes or not and you can

take the same model and make it learn on

a new data set let's say flower species

it would be able to learn the new rule

from itself. So the intuition like human

beings were learning from examples. Your

machine learning algorithms also learn

from examples. But just to frame our

problem statement that machine learning

we know it learn by experiences and from

the data from the historical events

there are three kind of problems which

may be interested in solving. First one

is called a supervised learning problem

and supervised learning problem is

basically occurs when you have some

input variables and one output column.

So both the examples which we discussed

till now one was on the flower species

where we are taking data on different

features of a flower and then which

species of flower it was. So the last

column is the dependent variable or

output variable we are trying to

predict. And all the information

variables are called input features or

input variables.

>> So input features or input variables

it's kind of interchangeably been used

in different input and output. These are

the two different sections of supervised

learning process why it is called

supervised learning. Another take if I

need to explain it we have a column to

guide the algorithm whether it's making

the correct decisions or not. So let's

say your model says person has diabetes.

The actual data says the person does not

have diabetes. So you have some kind of

correction mechanism within your data

itself which can help your model tune

itself to make better predictions. So

this kind of output variable some text

has also been called a teacher variable.

So it's guiding the algorithm to decide

those rules which I just go through.

Another type of machine learning is

called unsupervised learning. Best of

all in unsupervised learning we have

only the input features. customer

enjoyment

>> and our objective is that we should be

able to identify the patterns within the

data itself. So some of you who are

working in telecom domain or marketing

campaigns you would be very much

familiar with segmentation analysis or

cluster analysis where our objective is

to identify coherent groups within the

larger population. We take the customers

as it is the whole population and based

on different parameters and variables we

identify some of the groups of customers

or products whichever the business

problem is to identify which are similar

in nature so that we can take either

marketing campaign or develop new

products for those specific groups.

Final is called a reinforcement

learning. A reinforcement learning is a

kind of learning where the agent learn

from the environment. So it works kind

of reward and penalty. You can think of

a self-flying helicopter. So you leave

it in the environment and it'll be

deciding based on the wind speed and

other parameters in the environment that

how much it should fly and the reward is

that the fuel should be efficiently be

spent and more time it should be

spending in the environment. So

supervised learning as I said that the

objective here is that we have some

input features and input features would

be holding information about different

aspects of a given problem or customers

and we have an output variable which

would be explaining whether the event

happened or not some kind of output

variable. So there are two types within

supervised learning. One is called as

regression, second is classification.

And the differentiation happens only

because of the type of output column. If

your output column is the type of

numerical values or continuous values

like numbers, so that would be a

regression kind of problem, a very high

intuition level. For example, you're

working on a problem where you need to

predict how much would be the sale of

your company given the information that

how much they're spending on marketing,

how many employees are working, which

month it is of the year. And if you have

this information, you are going to

predict what is the million dollar of

sales your company would be doing. So

these kind of problems where your output

variable is of numerical values, then

it's a regression problem. On the other

hand, if the dependent variable is of

categorical nature or of discrete

values, it signifies that it's a

classification problem. Given that it's

a categorical values, your objective is

that how you can put the different

customers or products into different

classes. So that's why the name suggests

classification problem. So as I said

there are two kind of supervised

learning problems. One is regression and

another one is classification. So let's

take a use case where we need to predict

the housing price of a particular

locality. And we have information about

these houses on different parameters and

these parameters are like these. So

let's say what is the crime rate in that

area? How old is the home? The distance

is how far it is from the city. This is

from Boston housing data. So this is if

I'm not wrong it's percentage of black

population or some variable. We have the

description later on and what is the

actual price of the home and all these

features from crime to iset is the

information about the house. So these

are my input features and the output

feature is the price of the house. This

is in million dollars. And our objective

is that we should be able to fit an

algorithm that it should be able to

learn from all the historical homes

which were sold based on all the

features and what was the price it was

sold for. And once the model is trained,

it should be able to predict that how

much should be the price for a given

home. Let's take an example. Let's say

one of you is interested in buying a

home in the Boston area and you would

like to know that what is the ballpark

figure for a two-bedroom flat which is

of some square ft and let's say 20 miles

from a specific location what should be

the price. So one way would be you go

and talk to people and try to understand

that what has been the average price or

if you have this kind of algorithm

available which can help you understand

that given these features that was the

price and if you can create a regression

model it would be able to help you that

given some features of the new home

which you are interested in buying what

should be the price of it. So as I said

there are two sections. One is the

independent variables. All these

information about the home and the last

variable is dependent variable which

would be information about what was the

price. You see this is a kind of scatter

plot between the distance from the city

and price for the home keeping all other

variables constant. We are not looking

at the influence of other features. But

we are looking if you just need to model

or if you need to find a relationship

between the distance from the city and

what is the price from this graph you

can make out that further the city

houses the lower the price would be if

you keep all other things constant. So

here it's like if you can identify this

kind of relationship that's called

linear regression. But for a given

distance from the city you would be able

to predict what should be the ballpark

figure for a house. If you just have

this information not all other

information which we have talked about

in a similar fashion how it's been done

is that there would be a relationship

between the price of the house and all

the features which we discussed. So this

is only a relationship between one of

the variables distance to the city and

the price of the home. In a similar

fashion we would be able to find

relationship between the price and all

other features. So all of the features

if you know like how old is the home,

how big it is, what is the crime rate

and all. So this kind of model is called

a regression model and it's a very basic

equation of a straight line. Y here is

called the dependent variable. A is

intercept and B is called slope and X is

called independent variable. If you go

deeper and try to understand what it's

basically doing is this equation is

trying to tell me that if I already know

the relationship if I know the value of

a and b from my historical data which is

about different homes given the value of

x I should be able to calculate the y in

our particular example is price of the

home. Let me try giving an example what

slope means. So slope is the change

independent variable. If we change x

which is the independent variable by one

unit. So let's say if I change x by one

unit how much change in happen in y.

Help me understand what is the kind of

relationship between x and y. And a is

the value which tells the value of y

when the x is zero. And you can think of

it something like that. If you put the

value of x equal to0 whatever the value

is y then that's the intercept but

basically from intuition perspective you

can think that an intercept is the value

which is there even though you don't

have any information about x. For

example we were discussing the

relationship between the distance from

the city and the housing price. Even

though the house is exactly in the city

then there would be some value and even

though the house is 100 miles from the

city there would still be some price. So

it help us kind of intuitionally

understand the relationship. It also

help the line to understand where it

start whether it start from the origin

or some place within your axis. And this

kind of equation is called equation of

linear regression because if you see

here the power of x is one. So that's

why it's linear in nature. And it is

also that we are fitting the

relationship between x and only one of

the variables. Multiple regression where

what we do is that instead of finding

the relationship between only one

variable and the dependent variable in

most of the practical scenarios the

dependent variable Y is dependent on

more than one features. So for an

example, your house price is dependent

on all these features, all these

information points available and all you

want is that your regression model would

be able to identify relationship giving

all the information together and then

predict what is the price. The equation

becomes y equal to b1 x1 b2 x2 or b3 x3.

this kind of equation where B, B1, B2,

B3 and all these coefficients help us

understand that what is the contribution

of a single or of a given variable into

your regression equation. So let's have

a look that how you can fit this model

in Python. So if you look at the first

block of the code where we are saying

import panda as pen pd, import numpy as

np and import num plot lib as plt. This

is the convention in Python to import

some of the libraries which we'd be

using. So these are the libraries which

are required for running this module or

this this regression model. So once we

import these all the functions available

in these libraries we can call them very

easily and we will be seeing that how

you can call them. So once you have

imported these libraries if you see that

we are loading the data called Boston

and that this is the same data set which

we have been discussing in terms of a

use case here. So next line of code. So

here we are importing the data and

loading the Boston data set. The next

line of code we are calling the pandas

library because we have imported the

pandas library as pd. Then we are

calling a function called dataf frame so

that we can you know create a data frame

in python and we are creating it Boston

data. So it will be creating boss as a

data frame. So data frame on a very

loose term you can think of kind of

spreadsheet kind of format where your

data is being put in rows and columns

and you can think of an excel file kind

of framework for a data frame though it

will be different but just for intuition

purpose and after importing I'm calling

dot head. So what do head does it'll be

giving top 10 rows of my data set. So

there are all the 13 columns in Python

index start from zero. You can see that

index started from 0 1 2 3. So you can

see what this data is. This line of

command which says dot columns. So dot

column gives you all the features

available in your data set. So these are

the different feature names. So these

are the different column names for the

data and the price. In the end of the

code, I have written actually one line

of code which can give you all the

details that what the target variable

is. What was the history of data where

it was recorded and all. So you can

easily look at this. We are calling this

Boston.target and we are calling it as a

boss. So we creating a column in our

data frame which was boss from

Boston.target. So there is another data

vector available in the Boston data set

itself. And now specifically we are

saying y is equal to this particular

variable y we will be representing our

dependent variable all the features plus

the Boston price. So boss dot drop price

x is equal to 1. It means that we are

dropped the price variable from the

overall data frame and xis one specify

that we are removing the column. So x's

0 represent the rowle operations in

python and x's one represent the column

level operations. Print statement we are

just printing now the x. So this x is

all the input features of our data set.

So all these columns which we will be

using for predicting the housing price

and how it will be working. So it's not

an actual model but what actually

happening is once we have created the

model it will be doing that it'll be

fitting a line which would be going

through the actual data set would be

something like this that price is equal

to some intercept term plus b1

multiplied by crime b2 multiplied by

another variable z and then b3

multiplied by another variable and so on

and so forth and this intercept and b1

b2 and bn would be the coefficient which

your model would be learning from your

already available data and here we are

showcasing top five values of our

housing price. So y is the dependent

variable and we are looking at what is

the top five values. So this was only a

very brief and very basic introduction

that how do you import our data how you

can see what are the different columns.

It has nothing to do with machine

learning but it is only for people who

are new to Python and for people who

have been out of touch in Python if just

want to brush up skills and this line of

code if you have a look which I'm

highlighting now it is we are using a

scikitlearn model for test train and

split and what it's doing is for both

because we have already x and y the test

sizes we are saying 33 so it's basically

we are randomly selecting 33% of the

data for we putting it sep separate in

the test bucket so that we can test it

later on. And this random state five

means because we'll be randomly

selecting if you specify a random state.

Every time you run this code, you will

be selecting the same set of elements

from your data set. It help you

understand that the variation if you run

the code multiple times by changing the

variation is not coming because of the

selection of sample. It should be

because of the different model changes

you are making. This dotshape function

in Python specify that what is the

dimensions and if I mention the first

one X train.shape is giving me 339 and

13. Basically it's telling me that there

are 339 rows and 13 columns in the data

set. X test there are 167 rows and 13

columns. And your Y test is just 339

rows and there's just one column or it's

just one vector. The number of rows in X

train and Y train are same. In X test

and Y test the number of rows are same

because they have been selected for the

same combination. So same houses we have

selected the input features as well as

the corresponding values of output and

same has been done for the test section.

What we are doing here as I was saying

that we have imported a library called

scikitlearn and scikitlearn has

different modules for different machine

learning algorithms and linear

regression is one of the modules in

scikitlearn. So we can call this

scikitlearn module from linear

regression called lm and this equal to

and in python it's called assignment

variable. So we are assigning LM as a

linear regression module in the

scikitlearn. And now what we are doing

is if you look at this line only lm.fit.

So basically we are telling that use the

linear regression module from

scikitlearn and fit the model between x

train and y train. So basically what we

are telling the model that you learn

those coefficients for different x

values given y values in the training

data set. Basically what the fit

function does it calculate the values of

your intercept B1 B2 B3 for all the

features in your input features for a

given Y variable. So once we have fit in

the model and once that has been fit we

can use the same model for doing the

predictions. So let me remove it. It

should be like this. So lm.fit fit we

have fit in the model and once there has

been fit we can use the learned model

which is lm with a function called

predict x train. So what it will be

doing is once it has learned those

coefficients B intercept and B1 B2 B3

for all the features and input you can

use the predict function for making the

predictions for your training data set

and you already have actual values as Y

train and then you can compare that how

good your model is doing and how you can

compare it that if you look that we are

put together same thing we have used for

the X test data set LM.predict predict X

test and again the prediction has been

done. So if you see here I have put

together as a data frame Y test and Y

test bread and the difference look like

this for the first value which was 37.6

and the actual value was 37 this value

then the predicted value this actual

value is this and this difference

between the actual value and the

predicted value signifies that how much

is the error in your data set. So had it

been that your model is giving the same

prediction as it was the actual value

you would say there is no error. Your

model is 100% accurate and all the

predictions being made by the model is

absolutely you know bangon but normally

doesn't happen. You end up having

predictions which are a bit off from the

actual value and we measure the

difference as one of the characteristics

or one of the parameters to identify how

correct your model is. In this

particular statistic there are two

metrics being used for identifying but

the most uh basic one used is called

mean squared errors. And what mean

squared error is it is basically the

difference between actual value and the

predicted value by the model. And what I

mean by this is that let's say this is

your predicted value 37.6 and this is

your actual value. What you do is you

take the difference of these two and

then take the square of it. Why we take

the square of it? Because this in some

values the difference may be negative or

positive and if you sum it up the

difference may come to zero and you may

end up thinking that okay model is doing

really good stuff. To avoid it, what we

do is that we take the square of it so

that the difference between actual and

the predicted becomes positive and you

can sum it up to showcase that how far

your predicted values are from the

actual value and then you take a mean of

it to showcase that what is the mean

difference between the actual value and

the predicted value. It can also be used

for model comparisons. Here I can show

you that how it is working that let's

say you have some actual values

something like this and let's say you

fit a model I fit a model so there is

one model prediction one another model

is prediction two so what you can do is

you can take the difference between the

actual and the predict so 10 minus 2 is

2 and then you take the square of it

which is four 23 and 21 again two square

of 2 4 then third one the difference is

five and then square of is 25 and so and

so forth for all the values You get the

total value of sum of squared errors and

you divide it by the number of inputs

which is five and you get the mean of

the squared errors and you do the same

thing for the second one and if you see

it is very less five. So probably it

would be able to help you understand

that which model is doing a better job

in terms of predicting the housing

prices or any other numerical variable.

And there is another statistic which has

been used for identifying how good your

model is which is called mean absolute

percentage error. And that's basically

the absolute difference between the

actual value and the predicted value

absolute terms. and you sum it up all

the values for all the entries and

divide it by number of all the value of

absolute value of your actual values and

it can help you understand that what is

the average percentage your predicted

values are different from the actual

values. So sometime if you see it will

be somewhere in percentages. So what

I've done is I've taken the absolute

difference between this value and the

predictions I sum it up and divide it by

sum of my input values. So whatever

value comes in you can say okay it's 5%.

So it'll be fair to say that your model

is 5% off from the actual values or the

error term in your model is let's say 5%

or 6%. And whatever predictions you're

making from the model you can keep a

buffer of that percentage when you share

it with the team. And what I mean is

that let's say your mean absolute

percentage error is around 10% and it's

about the sales of a company. So when

you share this forecast you say that my

predictions are around 90% accurate they

may be actual sales may be plus - 10

percentage. So this can help you giving

this kind of variability in your

predictions. However you have

implemented code in python itself or the

scikitlearn library you can call mean

squared error the function from skarn

and it can help you calculate the MSC

for a given model. So basically there

was a very quick introduction to linear

regression. Though there are different

applications but one thing remain common

that we are trying to predict the

dependent variable whose nature or the

type of dependent variable is a

numerical or continuous data. Some of

the applications like predicting life

expectancy based on these features like

eating patterns, medications, disease

etc. You can predict housing price. We

have already seen the example on that.

We can predict the weight on different

features like sex, weight, prior

information about parents and all. And

you can also predict the crop yield of

crop based on different parameters like

rainfall and all. And as I said, this

like very limited uh use case uh list.

I'm sure people who are working with

sales department, you have to make

predictions how much would be the sales.

People who are working with call

centers, you need to predict what would

be the number of calls for next month.

people who are working with marketing

you need to predict what would be the

footfall in a given company or a mall.

So there are different applications of

regression models but one thing is

common across all these applications is

that the dependent variable which we are

trying to forecast is of continuous data

type. So let's get moving to the next

agenda for logistic regression. So at

the time of the introduction to machine

learning, we discussed there are two

kind of supervised learning techniques,

one is regression and other one is

classification. And the major

differentiating factor between the two

were that in regression we held a

dependent variable of continuous values

and in classification problems we had a

dependent variable of categorical types.

So let's take an example of how we can

do it. So here let's take a use case

where we have got some information about

some customers and the data set looks

like this that we have some customer ID

or user ids gender of the customer or

the user his or her age estimated salary

every month so you can think of in any

one of the currency either INR or

dollars and whether this user purchased

an SUV or not. So as I was saying

earlier the dependent variable here is 0

and one. So it's a discrete value or

categorical value which we need to

predict and the features which we'll be

using in the model are age and

estimation.

Why we would need a logistic regression

kind of algorithm? It would be a

straightforward process that if I take

purchase as a numerical value 0 and one

and I take some input features like age

and estimated salary and you will be

right in saying to some extent that this

is a possibility of doing it. So there

are two major problems coming if we

follow this and some of you can help me

what may be the problems if I try using

the linear regression for solving this

kind of problem. But one limitation I

can think of is that here I'm looking

for an output which can give me some

kind of [snorts] probability that how

likely I am to buy a product or service.

So one thing the limitation or the

restriction with probability is that the

probability term should be between zero

and one and zero signifies that there is

no probability or there is no likelihood

of event happening and one means that

it's certain that the event will happen.

There is no possibility that we can have

probability values less than zero or

greater than one. So if I'm fitting a

linear model taking the purchased column

as my dependent variable, my values

because the linear regression has no

such limitations can pass these values

beyond one or less than zero. So what I

require is that I fit the model in the

similar fashion like I did the linear

regression the equation I used earlier.

that I want that information would be

coming from my features in the similar

fashion. But what I want actually is

that this y should be mapped to the

values between 0 and one. And given the

limitation we have just talked about

probability that it should be between 0

and 1. It should not go beyond one and

less than zero. I need to find ways if I

can kind of force fit or kind of force

this y value which would be coming from

this equation and I force fit into a

values between 0 and 1. So to solve this

problem there was a function called

sigmoid [snorts]

activation function which would be

extensively being used in our uh deep

learning as well at different places.

But logistic regression comes from this

activation function itself which is a

function looks something like this that

output value would be 1 / 1 + e ^ - x

and x is not actually the one of the

input but any value we are giving it.

And if you fit any value into this

particular equation it can convert any

value between minus infinity to plus

infinity. It will map it to between 0

and 1. If your value of x which you are

putting in here, I could have selected a

different value, different name at

least. But if you give the highly

negative value, the output would be very

very close to zero. If this input is

positive, then the output would be close

to 1. If the value of x the input here

becomes zero, what would be the output?

1x2 because any values power 0 is equal

to 1 and 1 / 1 + 1 would be equal to 1x

2 or half. So logistic regression is

nothing but an extension of your linear

regression itself with one additional

fact that you want to force fit your

output between zero and one and for that

you are using activation function called

sigmoid activation function or sometime

it's also been called logistic

activation function to do the same task.

So this is an intuition behind your

logistic regression where you take

values of your equation from intercept

and different coefficients for your

input and you map these outputs between

0 and one. So once we have understood

that logistic regression is nothing but

the extension of your linear regression

only with a restriction on the output

being mapped between 0 and one. We are

shifting had we are fitting the

regression equation we would be having

scenarios where the value would be going

beyond one or less than zero and to

avoid this scenario we fit in logistic

regression with the help of sigmoid

activation function which looks like

this and if you see as I was saying when

your value of your model go beyond let's

say this is R0 so all the values which

are positive and greater than zero the

curve goes and tangential towards one

and for all the values which are less

than zero it goes towards zero and at

the place of zero the probability is 0.5

so it's a 50% probability if your output

is very much close to zero or it's zero

it can be used for multiple scenarios

one of the example we are taking is the

example whether somebody will buy an SUV

or not but if you're trying to solve

problems like somebody will say yes or

no to a product or service or whether

something is true or false or high low

or any different categories theories but

logistic regression can easily be put in

for multiclass classification problems

and basically if I just give you a very

quick introduction how it works is that

in multiclass classifications it kind of

does mapping that one class versus rest

of the other classes and then same

analogy follows that which class a

particular event would be associated

with but end of the day for whichever

class or category the probability is

highest the model will predict that uh

it should we belong to that particular

class like MSC. We have a statistic or a

parameter to evaluate how good your

model is doing and that was a parameter

to check that what is the difference

between the actual value and the

predicted value and how we were doing

it. We were taking the value which was

actual subtracting the predicted value

taking square of it. Do it for all the

examples and divide it by number of

training examples we have and then it

gives you some number and I was also

saying that this number is helpful in

kind of comparing different models. So

let's say you fit a model, I fit a model

and we compare MSC for both of them.

Whichever model is giving me a lesser

MSSE, it is kind of an indication that

probably your model is doing a better

job in terms of prediction than mine. In

a similar fashion, we needed a kind of a

statistic to see how good your model is

doing when your model is doing a

classification problem. So here there

are four categories that let's say we

have only two classes good and bad

actual values good and bad and what your

model is predicting good and bad. So

four examples which belong to good

category and your model is also

predicting them good category. So this

type of events or examples are called

true positives because your model is

doing correct prediction on positive

examples. Another category which is your

actual value for those examples is bad.

They belong to bad category and your

model is also predicting them bad. These

are called true negatives and these are

correct predictions because whatever the

actual value is your model is also

predicting the same thing. However,

there are two categories where your

actual value was bad but your model is

predicting good. These kind of examples

are called false positive because your

model is falsely predicting then these

are good examples. And another category

or last category is called false

negative where actual value were good

and your model is predicting bad. So how

do you learn or how does your model say

that which model is doing good job. So

what we do is we calculate what is the

percentage of values examples have been

predicted correctly. These sections in

blue true positive and true negatives

these are the examples which your model

has been able to predict correctly. And

these two groups false positive and

false negative are the incorrect

predictions. So what we do is we just

want to take what is the percentage of

correct predictions. And this matrix is

also called confusion matrix.

Let's say there are some examples out of

which 65 examples were there where

actually they were good category

examples and your model is also

predicting them as good class good

category examples. 44 are those where

they belong to the bad category and your

model is also predicting them bad. This

is 44 and eight are uh actual bad and

prediction is good and four are actually

good and uh predicted bad. What you do

is you sum up all the correct examples

64 and 44 and divided it by all the

examples in your data set all correct

and incorrect ones and here you get 89%.

So all you can say that your model is

being able to predict 89% accurately or

if you want to explain it to your

business team and say that whenever I

give you a prediction that uh 100

customers will churn and I give you a

list of 100 customers I can say with

certaintity that at least 89 will churn

from them with some certaintity. So

because your model has given you 89%

accuracy. So that that's how it's been

kind of communicated to business teams

that we are thinking that our model is

99% accurate and whatever prediction we

are giving we are very very certain but

if your model accuracy is 70 or 60% then

when you give the predictions to your

business team you say that okay though

we are giving you the predictions but we

are not very certain whether it'll work

correctly or not. So this accuracy

percentage is in a similar fashion like

we did for linear regression as MSC to

identify how good the predictions are.

Your accuracy percentage is another

metric to see how close or how correct

the predictions has been. So now we can

see the implementation of logistic

regression in Python. So first few lines

if you see we are importing the

libraries or the machine learning

libraries which we require to do the

data manipulations. We are importing the

data which is a CSV format and this data

is already available on your LMS. If you

want to import you can easily import

from the LMS itself unlike the Boston

data set which we were importing from

the library itself. Here we have got a

flat file as social network ads.csv and

you can call read csv function of pandas

library to import the data. So you are

importing the data as data set and as I

said head showcase the top five rows of

your data. So here we have only five

columns. One is user ID, gender, age and

salary. And the last column is our

dependent variable which signifies

whether a customer or a user bought the

SUV or not. So it's 0 and one and one

means the person bought. In the previous

code, we used one convention of

selecting X and Y. Here we are

showcasing another way of selecting

that's called eyelock. So we are looking

for the location and this convention if

I go through what we are doing here that

this is the data set within the data set

we are specifying the locations. This

colon means that we want all the rows

and as I was saying earlier that in

Python the index start from zero. So

what we are saying we want column 2 and

three. So what we mean that this is zero

this is one this is 2 and three. So we

want as our input features two and three

and the values. So it will be creating

an array of these two columns. We could

have used gender but I will leave it to

you that first we need to create the

gender as a vector of 0 and one. So you

can create a function which will say

okay if gender is equal to male then one

else zero or you can create dummy

variables there are function available

in scikitlearn. So it's an exercise for

you that this is the code already

available but I would encourage that if

you can also include gender information

into your model. The next line of code

why we are saying the dependent variable

is all the rows and column number four.

So column number four is your purchase

information whether a customer bought

the SUV or not and again the values to

convert into a kind of list format. So

we have specified two things. The two

columns is the information about the

input features or the information about

the user in terms of how much money they

make and what their age is and

information of why whether a customer

bought the SUV or not. And the next line

we are doing the train and test split

for the same stuff to evaluate whether

the predictions been made by the model

on the training data set on which the

model was learned is still doing the

correct classification on the data set

or the test data set which was not

involved at the time of training and

this 0.25 means that we are selecting

25% of the data for test and remaining

75% for our train. What is the correct

split of train and test? Normally it is

correct to choose between something like

6040 or 7030 or 80/20. If your data set

is big enough then I think having 80/20

kind of split is good or whatever you

can try these different combinations but

as a rule of thumb most of the time I

have seen people taking something like

6040 or 7030 kind of distribution

between actual value and the predicted

value. Now there is one important thing

for data prep-processing and this

selection [snorts] which I have made for

doing the data prep-processing and some

of you who come from the machine

learning background will already know

that how important it is to kind of

scale your data and what do I mean by

scaling that if you look at the data set

which we are using for input one is the

age column and second one is the income

column age can be somewhere between

let's say 1 to 100 or 120 20 at max and

your income is in like some thousand and

some 100,000 numbers. Both these values

are on different scale. Scaling your

data on let's say all the values between

0 and 1 will help me understand that

what is the importance of each variable.

For for example, if you look at a

regression equation and you see those

coefficient B1 B2 for all the input

features, these feature or these

coefficients can give you kind of

indication that how important a

particular variable is. But this

intuition will only be correct if all my

features were on the same scale. If

these features like age and salary when

they are on different scales, you will

not be able to compare what these

coefficient really mean because there

are two different scale your values come

from. So it is always a good idea to

have all your features on the same

scale. There are multiple ways of doing

it and there are multiple type of

scaling parameters. The simplest one is

called minmax standardization. And what

does it mean is that for a given column

let's say we are talking about age

column which is 19 35 26. If I need to

do minmax standardization what do I mean

is that I take the value it is let's say

19 and minimum value here is let's say

19. I have only these five values. So

how it works is that this is the

formula. This is the value or how it's

being presented. X I minus the minimum

value of the column. So let's say age

divided by max of age minus minimum of

range as well. But basically what this

formula will do if I do it for all the

values in the age column, it will be

converting all the values between 0 and

one. And there are other ways. I also

said that there are normalization

process which is like you take the value

minus the average value divide by the

standard deviation if I call it

correctly. So whichever method we apply

all I saying is that these values of age

and salary should be brought to the same

scale. So if I'm applying this minmax

standardization I'll apply to both my

columns so that both these variables are

on the same scale and I should be able

to use them in my model. And this is

again a very important thing that

whenever you do standardization you will

be using this process that you fit the

normalization or standard scaler on the

train data set and you use the same

learned standardization from the train

on the test data set. But basically how

it helps that your data set on which

your model is being trained it will be

converting the values between 0 and one

based on the minimum and maximum values.

If the test data set have different

minimum and maximum values, it can have

different value for the same number. So

that's why the process is that we make

our standardization fit on the training

data set and use the same minimum and

maximum value for test normalization as

well. It gives the same scale for all

the values and for model predictions.

It's very helpful. In a similar fashion

like we called a linear regression

object from scikitlearn in the previous

example in exact same way we can call a

logistic regression function from the

scikitlearn. So it's a scikitlearn

linear model and we are importing

logistic regression. Now um we are

fitting the logistic regression between

x train and y train the same way we did

it earlier for the linear regression.

And once it has been fit we can do the

prediction for test data set. We are

also doing the same thing that we are

calling the function which was

classifier for the logistic regression

and we doing it on the X test data set.

And here the default probability is 0.5.

So what your algorithm is doing in the

back end for all the examples wherever

the probability in X test became greater

than.5 it was tagged as one and for all

the examples where probability was less

than equal to.5 it was tagged as zero

and now we are calling this function

called confusion matrix between y test

and yred. So we are comparing that what