GEN AI & AGENTIC AI with Python - Session -01| Ashok IT.

Yeah, perfect, guys. Good.

So, let's get started with our session

for today.

Good evening, guys.

Welcome to Ashok IT. And today, we are

here to discuss about generative AI and

agentic AI development by using Python

programming language.

So, the main agenda of today's session

to understand what is this generative AI

and agentic AI development and who is

the trainer for this course and what are

the prerequisites in order to attend

this generative AI and agentic AI and

what is our course content and the daily

class timings, what is the course

duration,

how it is going to help you to upskill

yourself and what kind of job roles that

we can apply after attending this

course. Good. So, this is GenAI and

agentic AI development with Python

program. So, this course trainer,

myself, my name is Mr. Ashok.

And total, I'm having 12 plus years

experience in the IT industry, okay? And

coming to my training experience, I'm

into software trainings from last 10

plus years and I'm the founder of Ashok

IT. So, this Ashok IT Institute I have

started in the year of 2020. We

completed 6 years with Ashok IT.

So, this is very quick introduction

about your trainer, that myself. So, my

name is Ashok. I'm having 12 plus years

of IT working experience. I'm having 10

plus years of training experience and I

have started this Ashok IT Institute in

the year of 2020.

And coming to our course, GenAI plus

agentic AI development. So, what is the

main agenda of this program? What is the

main agenda of this training? The

program overview. So, everybody, every

company currently looking for the

developers who can develop AI systems.

Already AI became very popular in the

market from last 2 years, 3 years.

Everybody using AI tools. But companies

are not looking for just AI users.

Companies are looking for AI developers.

So, ChatGPT 10th class student also can

use, fifth class student also can use

ChatGPT. But companies are looking for

AI developers, not AI users. So, how

many of you AI user and how many of you

are AI developer? Can you respond in the

chat box? So, at present, what is your

skill? Are you just using ChatGPT or are

you able to develop a application like

ChatGPT? Can you just question yourself

and answer in the chat box?

Currently, you are AI user or currently

you are AI developer?

You are just using ChatGPT or you are

you can develop one application like

ChatGPT?

So, nowadays, most of the software

engineers, 99%

of software engineers,

software engineers are just AI users

today.

99% of software engineers are just AI

users today. But what companies are

looking for? Companies are looking for

looking for Gen AI and agentic AI

developers.

Companies are looking for Gen AI agentic

AI developers. That means companies are

looking for the developers who can

develop a software application

like ChatGPT, like Claude, like Copilot,

like Claude, like Copilot. Companies

are looking for AI developers.

Now, so you just question yourself, what

is your current position? You are AI

user or you are AI developer?

You are AI user or you are AI developer?

99% of software engineers today are are

AI users. Are AI users? So, that's where

the GenAI developers, agentic AI

developers are having too much demand in

the market. Too much demand in the

market. So, that's why today you guys

are here. The main agenda of this

course, so if you are really interested,

if you are really interested to build

your career in the fast-growing world of

GenAI, agentic AI, Python, LLMs, RAG

development, agents development,

LangChain, LangGraph, and ML Ops. So,

build your career in the fast-growing

field of the AI. So, the main agenda of

this program, build your career in the

fast-growing world of GenAI,

agentic AI,

agentic AI, Python, Python, LLMs,

RAG systems, okay? Vector databases,

and AI agents,

AI agents development, LangChain,

LangChain, LangGraph,

LangGraph, MCP,

MCP,

okay? And ML Ops.

Or LLM Ops.

ML Ops

or LLM Ops.

So, this is what the companies are

expecting from AI developers today. So,

the main agenda of our program also

same. So, if you are really interested

to build your career in the fast-growing

world of generative AI, agentic AI,

Python, LLMs, rack systems, vector

databases, AI agents development by

using LangChain, LangGraph frameworks,

MCPs, ML ops, and LLM ops. So, this

program is designed to take the learners

from basic level to advanced projects

development by using these concepts.

So, the main agenda of this program to

make you from basic to develop your

skills from basic level to advanced

projects development by using these AI

concepts. Even if they do not have

programming experience, if you don't

have Java knowledge, Python knowledge,

.NET knowledge, no problem. We are going

to teach everything from the basic. Our

goal is very simple. I want to make you

people from zero to hero in the GenAI

and agentic AI development by using

Python.

So, our goal is simple. I want to make

you zero to hero in the GenAI and

agentic AI development by using Python.

That means we can develop our own AI

tools. We can develop our own AI

projects once this training is

completed. At this moment, you are

having a tag called AI user. After this

training is completed, you are called as

AI developer because you can develop

your own AI tools by using all these

concepts which are currently trending in

the market. Now, sir, if I want to

attend this training, if I want to

become AI developer, I don't want to

continue as a AI user, I want to become

a AI developer, then you can stay in

this meeting for the next 1 hour with

me. Okay, so what are the prerequisites

in order to attend this training? To be

frank, there are no specific

prerequisites in order to attend this

training.

There are no specific prerequisites

available in order to attend this

training. This program is a suitable for

beginners and we are going to start from

basics and gradually we will move

towards advanced real-time projects

development by using all these GenAI

agent K concepts. No specific

prerequisites. If you don't have any

programming experience, no problem. Sir,

I am from non-IT background. Can I

learn? Yes, because we are starting from

the zero. Sir, I am a fresher. Can I

learn? Yes. Sir, I am from the Java

background. Yes, I can join you can

join. Sir, I am from .NET background. No

problem. Sir, I am from DevOps

background. Sir, I am completely from

non-IT background. No problem. Because

we are starting from the zero. So, very

beginning, what is AI, how to use AI,

and how to develop the AI, how to deploy

the AI. All those concepts we are going

to discuss as part of this training

program.

Are you guys clear? What are the

prerequisites for this training?

What are the prerequisites? No

prerequisites required. This program is

suitable for beginners. We will start

from basics and gradually we will move

towards advanced and real-time projects

development. Sir, what we are going to

learn in this program? So, what you are

going to learn in this program? In this

program, you are going to learn Python

from scratch.

You are going to learn Python from the

scratch. If you want to become AI

developer, the most important language,

the most important skill that is

required is Python. Because currently,

all the AI tools, all the GenAI

applications, large language models are

getting developed by using Python

programming language because that is

very simple, that is very easy to use.

So, I'm going to teach you Python from

the scratch. So, if you don't know

anything about programming, no problem.

We are going to start with the Python

programming. Then after this Python

programming, we are going to understand

Python libraries. In order to work with

the AI applications, we need some

libraries. Example, NumPy required,

Pandas required, Matplotlib required,

Fast API is required, PyTorch is

required, TensorFlow is required. Some

Python libraries are available which we

are using as part of AI development.

Those libraries also we are going to

cover as part of this training. Then

after that, we are going to learn

machine learning fundamentals. How

machine learning projects are working

and how deep learning projects are

working.

Okay? How machine learning fundamentals

are working, how machine learning

projects are working, how deep learning

projects are working. So, earlier, these

machine learning deep learning concepts

are having too much demand. But today,

these concepts became traditional AI

methodologies and today the trend is

GenAI application, agentic AI

application. Before going to understand

GenAI and agentic AI development, I'm

going to cover the fundamental concepts

of machine learning and deep learning

algorithms also. Once it is completed,

then we are going to understand large

language models.

We are going to understand large

language models. Nothing but the

generative AI applications development

we are going to learn.

GenAI applications development we are

going to learn. And we are also going to

learn rag systems, rag based systems.

Rag based systems using vector

databases.

Okay? And we are going to work with AI

agents development. We are going to work

with AI agents development. That is

nothing but agentic AI. Then after that,

we are going to understand agentic

workflows.

We are going to understand agentic

workflows, and we are going to work with

real-time projects implementation.

How to implement real-time project

scenarios by using this GenAI and

agentic AI. Real-time projects

development we are going to implement.

Then after that, we are going to

understand GenAI and agentic AI

application deployments in the cloud.

We are going to understand application

deployments in the cloud. That concept

is called ML Ops and LLM Ops. Okay?

So, when you work with machine learning,

that is called ML Ops. Now we are

working with LLMs, it is called LLM Ops.

Then after that, we are going to work

with interview preparation and resume

building.

We are going to work with interview

preparation and resume building. So,

these are the concepts that you are

going to learn as part of this GenAI and

agentic AI development program. So, the

course prerequisites are nothing. Our

main goal, zero to hero. I want to make

you zero to hero in the GenAI and

agentic AI development with Python.

Currently, it is a trending in the

market. As I told you, in the IT

industry, 99% of the till today, 99% of

the software engineers are just using AI

tools. They are called AI users. But the

demand is there in the market for AI

developers. Even third class, fourth

class students also using AI. But we are

software engineers. We are already doing

the job. We should not stop at AI user.

We should become AI developer. So,

that's where GenAI and agentic AI

development comes into picture. Okay,

now in order to attend this program,

what are the prerequisites? No

prerequisites are there because we are

starting this course from the basic

level. What is Python? How to install

Python? How to work with the Python

variables, data types, methods, oops

concepts, exception handling, file

handling. Then we will start with the

Python libraries, then machine learning

fundamentals, deep learning

fundamentals, LLM's integration, LLM's

development, generative AI projects

development, rack systems

implementation, agents development,

agent workflow automation, real-time

projects development, application

deployments in the cloud, interview

preparation, resume building, everything

we are going to cover as part of this

training. That's why I told you this is

a zero to hero course for people who

want to start their career as a AI

developer. This is

This is zero to hero course for the

people who want to start their career.

Who want to start their career.

Come on guys. Are you clear with my

point? Are you able to follow me?

Are you clear with my point? Are you

able to follow me? So you want to be as

a AI user or you want to become AI

developer?

AI user or AI developer? Who will have

more demand in the future?

Who will have demand in the future? User

AI users or AI developers?

AI developers. All right, good. So this

course content, so no prerequisites

available. These are the concepts you

are going to learn. And this course

content I have divided into multiple

modules guys. This course content I have

divided into multiple modules. So here

first module we are going to cover

Python programming, core Python plus

advanced Python. From the fundamental

concepts, what is Python, why Python,

how to set up Python, how to develop the

applications by using Python. All the

Python fundamental concepts we are going

to cover. Then, as part of this Python

programming, these all the topics I'm

going to cover. Introduction to Python,

Python installation, how to set up our

set environment to work with Python,

variables, operators, conditional

statements, loops, strings, data

structures, functions, packages,

modules, file handling, exception

handling, oops. Okay, working with APIs,

database connectivity, real-time coding

practices. All these concepts will be

covered as part of our Python

programming module. Once it is

completed, then module two, we are going

to start with Python libraries for the

AI development. So, these are very,

very, very important if you want to

become a AI developer. Python libraries.

So, what libraries we need to learn,

guys? Here, we need to learn about to

NumPy, which is used to work with the

numerical Python. And we need to work

with the Pandas, which is used for a

data cleaning activity. And we need to

work with the Matplotlib, which is used

for the data visualization. Data

visualization. And we are going to work

with the some maths related concepts

also, probability and statistics.

Probability and stats concepts are

required. Okay? And we are going to work

with the We are going to work with the

scikit-learn, which is used for

developing machine learning projects.

Okay? And we are going to work with the

FastAPI, which is used to expose our

GenAI application as a REST API. And we

are going to learn Streamlit UI to

develop UI functionality for our GenAI

project. Like these some important

Python libraries we are going to cover

as part of module two. Then coming to

module three, we are going to discuss

about machine learning and deep learning

algorithms. So before this is GenAI and

DKA, the trend was machine learning and

deep learning. Those concepts we are

going to discuss. So what I'm going to

cover in the machine learning?

Supervised learning, unsupervised

learning, reinforcement learning,

regression algorithms, classification

algorithms, clustering algorithms will

be available. Decision trees, random

forest will be available. Coming to deep

learning, after back propagation will be

available. Optimizers, CNN, RNN,

transformers, all those concepts comes

into picture when we talk about machine

learning and deep learning. Okay? So

those concepts, if you want, I can paste

here. In the syllabus document, those

are available. Machine learning, deep

learning algorithms, what I'm going to

cover. Introduction to machine learning,

types of machine learning, how to work

with these algorithms in the machine

learning, and how to work with the deep

learning algorithms. Those things we are

going to discuss. Those things we are

going to discuss. Okay? Module one is

the Python programming, fundamentals of

the Python. Even if you are from non-IT,

no problem. We are going to start from

the scratch. Then Python libraries which

are required for the AI development.

Module three, machine learning and deep

learning algorithms we are going to

discuss.

And coming to module four, coming to the

module four, LLMs and prompt engineering

we are going to discuss. So currently,

we are using ChatGPT. How the ChatGPT is

working in the background, guys? ChatGPT

is a UI application. In the background,

ChatGPT is working based on one LLM. So

every every AI application is working

based on one LLM concept. So in the

background, one LLM will be available.

So what are all the LLMs available in

the market? Here, OpenAI company

developed a LLM called the

Okay, guys? And Google developed one LLM

called the Gemini. Gemini. Anthropic

company developed one Anthropic Cloud.

Cloud is one Cloud application is using

one LLM which is called Sonnet. Sonnet.

Meta company, Facebook Meta people

developed one LLM Llama. So, like this

there are so many LLMs are already

available in the market. So, this LLM is

a trained this LLM is a big project.

Companies are spending lot of money,

billions of dollars they're spending in

order to develop the LLM, train the LLM,

test the LLM. So, LLM nothing but which

is a pre-trained program. Now, here

today we are using ChatGPT. We are using

Copilot. And we are using Cloud. So, how

all these applications are working? So,

how this is ChatGPT is working? How this

Cloud is working? How this a Cloud tool

is working in the market?

Okay? And how this

How this Copilot is working in the

market? So, all these are front-end

applications only. This ChatGPT, Cloud,

Copilot. In the background, these

applications are having a LLM. So, the

LLM nothing but large language model.

So, why it is called as a large language

model? They trained that model with

billions of parameters. That's why it is

called as large. So, what they have

done? So, the companies developed the

companies developed a model machine

learning model. And they trained model

model trained.

Trained. After training, they tested

that model. They fine-tuned that model.

They fine-tuned that model. And they

have deployed model. That's why we are

able to use them.

Developed a model. They trained trained

trained with trained with a billions of

parameters.

Trained this model with a billions of

parameters. That's why it is called

large language model. And they tested

80% of data for the training, 20% of the

data they use for testing. 80/20 formula

they are going to follow. Whenever a

company, anybody, when they are working

on machine learning project or any AI

project, they will follow 80% 20%

formula. What is that 80% 20%? So,

whatever the data they collect, 80% of

the data they will use for training the

model, 20% of the data they will use to

test the model. As part of the testing,

they will check the accuracy of the

model. For the given question, the AI

tool is able to generate the response

correctly or not. If it is responding

correctly, no problem, training is

success. If it is not able to respond

properly, that means training is

incomplete. They need to train the model

with new data set. That is called model

fine-tuning. Once the model fine-tuning

is completed, they will deploy the model

in the cloud. So, that model we are able

to access today. Those models we are

using today. Right? So, ChatGPT is

working based on the GPT model developed

ChatGPT is an OpenAI company project.

So, OpenAI company developed one LLM

called GPT. And Google Gemini is

available. Google developed their own

LLM. Claude developed their own LLM

called Sonnet. Meta Facebook company

developed their own LLM called LLaMA.

LLaMA. And here, Microsoft company

developed this Copilot. GitHub Copilot

tool is available. It is working based

on the LLM. So, currently, the people,

the software engineers are depending on

these tools in the market to complete

their activity. That means you are using

a ChatGPT, you are using Claude, you are

using Copilot. In the background, these

tools are connecting to the LLM models

developed by those companies. As those

companies invested lot of time, lot of

money for the model development, for

model training, model testing, model

fine-tuning, model deployment. Now, they

are charging money from the AI users

based on the tokens. Now, you send me a

request to the ChatGPT, internally

ChatGPT will connect with the LLM, it

will get the response from the LLM, that

LLM response will send it to you. So,

whatever the question that you are

giving to the ChatGPT, that question is

called as a prompt. It's not like a

request. You are giving a prompt. When

you are giving that prompt, that that

tool, that AI tool, is providing the

response for you in the form of tokens.

So, token is nothing but the number of

words or number of sentences or number

of characters that you are getting. So,

number of tokens, how many tokens that

you are receiving, based on these number

of tokens, they are charging. So, how

this OpenAI company getting the money?

They are giving ChatGPT. So many people

are using ChatGPT. So, you are using

free version of the ChatGPT, you will

get the less number of best outputs.

Best outputs will not come. When you go

for free version of ChatGPT. When you go

for commercial version, when you take

the purchase, when you purchase the

license of the ChatGPT, then it will

give you better results because when you

take the license, they will use the bet-

better LLM to respond for your question.

Okay? So, now people are just using the

tools. Now, companies are looking for

LLM developers, generative AI

application development. Okay? So, if

you want to become AI developer, you

should understand what is this LLM, how

the LLMs are working, how to integrate

the LLMs in our application, how to

develop our own LLMs. So, these things

we need to understand. And how to give

the prompt, how to give the better

prompt, how to use the tokens in the

simple manner, how to save our money

when we are using these AI tools. So,

all these a if you know, then you can

reduce your AI tool billing also. Proper

prompts if you use, better outputs you

will get.

Proper prompts if you give, better

outputs you are going to get.

So, all these things you need to

understand as part of this AI

development. So, I know you already

using AI tools, but I think most of the

people don't know how to use the AI tool

effectively. How to give the prompt to

the AI tool, most of the people don't

have that clarity. Have you know about a

concept Do Do you know a concept called

zero-shot prompting?

Do you know a concept called one-shot

prompting?

Do you know a concept called few-shot

prompting?

Do you know a concept called chain of

thoughts in prompting?

Are you following these principles when

you are using AI tools?

How many of you using these concepts

when you are asking a question to the

ChatGPT?

If you don't know these concepts when

you are using the ChatGPT or Claude,

that means you don't know prompt

engineering concept.

You don't know how to use the AI tool

properly. Randomly you are asking

question, they are giving some answer,

that's it. That is not the good way.

That is not the good prompt. Basically,

you are using ChatGPT with bad prompts.

So, I know how people will use ChatGPT.

Suppose if you want to know about a

concept, what is the DevOps?

Simple.

You will give this question to the

ChatGPT, it will give some answer for

you. Am I right?

Are you following the same process?

Explain Java. Explain Python oops

concepts. Explain what is the decorator

in the Python. So, a single-line

question people are giving to the

ChatGPT. That is called bad prompt.

That is called bad prompt. Your tokens

will be wasted, and you will not get the

best output from the AI tool. If you

want to save the tokens, if you want to

get the best result from the AI tools,

you need to follow these concepts.

Zero-shot prompting, one-shot prompting,

few-shot prompting, chain-of-thoughts

prompting, step-by-step prompting. These

techniques you need to follow when you

are using AI tools. So, how to know

these techniques? As part of prompt

engineering, we are going to learn. What

is prompt engineering? How to write the

prompts? How to write the effective

prompts? All those things we are going

to discuss.

All those things we are going to

discuss.

Are you clear with my point?

Are you guys So, you are currently AI

user, but you are not perfect AI user.

You are AI user, I agree. You are using

ChatGPT, you are AI user, but you are

missing some concepts to get the better

results from the AI.

What you are going to miss? What you are

missing? That I will cover in the prompt

engineering concept. Then you will

realize how to use the AI tools

properly. So, this module is going to

help you to understand how AI tools are

currently working. Okay? How to

integrate the existing AI tools in our

project? What is the prompt engineering?

So, these all topics I'm going to cover

as part of this. What is the LLM? How

the AI tools are working? What is a

token? What is a context window? What is

a model temperature? What is model

parameter? Prompt engineering, advanced

prompt engineering, zero-shot, few-shot,

chain-of- thoughts, role-based

prompting, prompt templates, how to

integrate open AI model in our project,

how to integrate Google Gemini in our

project, cloud integration, building our

own AI application using APIs, best

practices for the prompt design. All

these things we are going to learn as

part of our module four.

So, then once it is completed, then in

the module five, our actual application

development will start. Generative AI

and application development by using rag

system. This module focus on building

real world gen AI applications. So, what

topics I'm going to cover in this module

five? Introduction to the generative AI,

how to implement a text generation

application by using this AI, how to

develop our own chatbot, how to work

with a document question answer system,

how to work with PDF based question

answering, how to work with embeddings

and vector databases in the gen AI

project, how to work with the rag

architecture, how to implement rags,

okay? and how to build a rag

applications with PDFs, websites and

databases, AI chatbot with a knowledge

base, knowledge base like LangChain

pipelines we are going to implement. How

to give the knowledge base to the LLM

and real time gen AI projects

development we are going to discuss.

Then, once these gen AI concepts are

completed, the next one will move to

agentic AI development by using

LangChain, LangGraph, MCP and agents

development. So, this is one of the most

important module of this program. So,

here you are going to learn how to build

intelligent AI agents. That agent can

plan, that agent can think, that agent

can use the tools, that agent can

complete the task on its own. Currently,

the companies are looking for agentic AI

developers. That means you should be

able to develop the agent on your own.

You should be able to develop the agent

on your own. So, what is the

responsibility of that agent? That agent

should be able to think, that agent

should be able to plan, that agent

should be able to complete the task on

its own. So, that is called intelligent

AI system development. So, here we are

going to understand what is the agent AI

and how to develop AI agents by using

agentic AI agents architecture, agent

workflows, agent planning, tools usage

in the agents, how to work with

multi-step task execution. Then we are

going to work with LangChain. By using

LangChain recently in my current journey

AI project batch, I am covering

LangChain for them. So how to work with

agents development by using LangChain.

So let me show you. Here, I am teaching

them how to develop a agent tool. How to

develop the agent by using LangChain AI

chatbot. So currently, the if some

people will integrate the LLMs in their

project, but just getting the data from

the LLM is not sufficient. We need to

fetch the data by using DB call. We need

to fetch the data from the API call. We

need to integrate the rag. Then we

should prepare a prompt. Then we should

talk to the LLM. Then we need to pass

the response by using parser. So this is

one generative AI project by using

LangChain. This is called LangChain

pipeline. See here. So in my currently,

I am running two batches for generative

AI. One is running at morning 7:30. One

is running at evening 8:00 p.m. This is

another batch which is started today at

7:00 p.m. In the morning, and a 9:00

p.m. batch is there. So in that, we are

teaching this LangChain concept. They

are working on the agentic AI now. So in

the agentic AI, we are working with this

LangChain integration. So get the

LangChain nothing but set of steps. The

knowledge base that we need to fetch, we

need to fetch the data from all these

components, and we should give to the

LLM. That is our agent development, our

chatbot development. LangChain. So if

you want to implement complex

applications, then we need to go for

LangGraph. Okay? If your pipeline is a

simple and straightforward, then you can

go for LangChain. If your pipeline is

complex, the decision-making, looping,

conditionals are required, then we need

to go for LangGraph to develop our

application. Then we need to work on MCP

servers. How to integrate multiple

systems, MCP server, MCP client in our

agents development. Okay? Then after

that, we are going to work on our own

agents development by using all these

concepts. As part of the module six,

Agent DKA, LangChain, LangGraph, MCP, AI

agents we are going to develop. Then

once this GenAI and Agent DKA is

completed, the last module, the as part

of our syllabus, that is going to be

module seven. As part of the module

seven, we are going to understand MLops,

MLops, and

LLMops. So this module is going to help

you to understand how to deploy our

applications in the cloud as a

production grade application. This

module is going to help you to

understand how to deploy and maintain AI

applications in the production. Sir, can

you share these notes? Yes, ma'am. After

this class, I will share the notes for

you. So I have created one Google

Classroom for this batch. So I'm giving

Google Classroom link in the chat box.

So please click on that link and join.

Then after this class is completed, this

class video, this class notes, I will

share in the Google Classroom.

After this class is completed, this

class notes and class video, I will

share in the Google Classroom. Google

Classroom link I'm giving in the Zoom

chat box. So I request you people to

click on that link and join. So after

this class is completed, you no need to

ask anybody for the video and notes.

Immediately after the class completion,

in front of you, I will share the video

and I will share the notes also. And I

will give you detailed syllabus PDF

document also for this training.

Is this clear? Good. So, we are trying

to understand who is your trainer for

this GenAI and Agent AI course. Okay?

And we are going to understand what are

the So, I mean we discussed about

already who is the trainer and currently

what is the problem in the market and

what companies are expecting. Currently

in the market, people are just using AI

tools, but companies are looking for AI

developers. So, in this program, I'm

going to cover all these concepts. So,

if you are really serious to build your

career as a AI developer, then by using

all these concepts you need to

understand. You need to learn all these

concepts in order to become AI

developer. So, this program is designed

from a zero to hero course for the

people who want to become GenAI and

Agent AI developers with the Python. So,

this main goal This course's main goal

zero to hero in GenAI and Agent AI

development. No prerequisites, guys. As

I told you, I'm starting from the

scratch. Okay? So, we are going to

discuss Python programming. The course

will start with the Python programming,

the first module. All the Python

fundamental concepts you will learn.

Then, the most important part here,

Python libraries. Without knowing these

libraries, you cannot develop AI

systems. You cannot develop GenAI

projects. You cannot develop Agent AI

projects if you don't have clarity on

these concepts.

Okay, guys. Right? Next one, in the

module three, machine learning and deep

learning algorithms we are going to

touch. Some fundamental concepts we are

going to learn before GenAI how ML DL

projects are used in the market. But,

currently trend in the market is GenAI.

GenAI. So, once these concepts are

completed, then we will start start with

the LLM concept and prompt engineering.

As I told you, what is LLM? So, so many

AI projects are already running in the

market. How those AI projects are

working, those AI projects are working

based on LLM's concept. So, we need to

learn how to use the existing LLM's and

how to develop our own LLM, how to

deploy our own LLM. So, that's why we

need to learn what is LLM and what is a

prompt engineering. Okay? Then,

generative AI applications development,

agentic AI applications development,

LangChain, LangGraph, MCP, agents

development we are going to learn. The

next one, ML ops and LLM ops also we are

going to learn. So, as part of this I'm

going to cover

So, how to integrate our code in the

GitHub. When you are developing GenAI

project,

whenever

Yogesh Yogesh is asking, "Sir, is this a

live session or recorded video?" Mr.

Yogesh, this is a live session. After

this class is completed, I will give the

time for you people to interact with me.

So, anybody who is having a question,

you can send me in the chat box. And at

the end of the session, you can uh talk

to me if you are having any question.

Okay, guys? So, post your question in

the chat box now.

Good. So, what are the topics we are

going to cover? Git and GitHub, version

controls, how to dockerize our

applications, how to use Kubernetes, how

to work with the CI/CD pipelines, how to

work with the cloud deployments, model

development, model deployment, how to

implement logging in our GenAI

applications, production-ready

configuration. So, deployment's

automation by using N8N architecture.

All those things we are going to

discuss.

All those things we are going to discuss

as part of this training.

Okay, guys? Then, last one, interview

preparation. Sir, if I learn this

program, then how to prepare my resume?

Sir, already I'm a Java developer. How

can I add these skills in my Java

development resume? Sir, I'm a DevOps

engineer. How can I add these GenAI

skills in my DevOps resume. Sir, I am

from automation testing. How can I

switch my career from testing to the AI

development? So, that interview guidance

will be available, resume preparation

concepts also will be available, and we

are going to give you interview

questions as well. I hope you are clear.

What are the prerequisites and what is

our course content? Sir, then who can

attend this training? So, people are

joining from different different

locations, and people are coming from

different different backgrounds. Sir,

who can attend? Today, can we say AI is

optional for anybody?

In the today's market, can we say AI is

optional for developers? AI is optional

for the testers? AI is optional for

students?

So, is there any industry which is not

using AI today?

Guys?

Is there any industry currently which is

not using AI? So, then you tell me, who

can attend this AI?

Who can attend this AI?

If you are a fresher,

yes, you have to attend. If you are a

developer also, you have to attend. If

you are working as a DevOps engineer

also, you have to attend. If you are a

tester also, this is very important for

you to switch to the AI development and

AI testing. Non-IT students also can

join for this course. Final year

students also can join for this course.

If you are already working in the IT,

and if you want to switch to the AI,

then you have to join for this. If you

are having some career gap also, no

problem. So, if you want to learn AI

applications development from the

scratch, then this course is going to

help you. So, students, freshers,

developers, operations team, cloud

engineers, testers, non-IT people,

anybody. If you are If you want to

become AI developer, then you can join

for this course. I can't say AI is

optional for anybody. AI is mandatory

for everybody today.

AI is mandatory for everybody today. So,

all you people should learn the AI.

Everybody should learn the AI. If you

want to survive in the IT industry, then

definitely you have to learn this AI

development. AI development. So, you

tell me, earlier, what was the most most

difficult task in the IT industry, guys?

Why I'm saying this course is important

today? So, before this ChatGPT, Copilot,

Cloud, what was the most difficult task

in the IT industry? Coding. But today,

today what is the most easy task in the

IT?

Coding only.

So, why this course is important today?

Earlier, one of the most difficult roles

in IT was the development role because

developers had to write the most of the

code manually. Today, software industry

is changing because of AI tools.

So, earlier, every developer should

write each and every line of code on

their own. But today, the AI tools have

changed completely. So, earlier, manual

coding process, but today, wipe coding

process is available. Now, let me show

you one example for the wipe coding.

Let me show you one example for the wipe

coding. I want to develop one Spring

Boot REST API with a JWT security, guys.

Okay? Act as a Act as a Spring Boot

developer.

Act as a Spring Boot developer.

Develop Spring Boot REST API

with a user register

login functionalities

login functionality using JWT security.

Make a production ready.

Now, so earlier, if I want to implement

this requirement, minimum 2 days of time

is required. If I want to develop this

task in my project earlier before this

AI, if I get this task in my company, I

need minimum 2 to 3 days of time to

complete. I need to create the project.

Everything I need to do on my own.

Security I need to implement. Logging I

need to implement. Exception handling,

JWT token. I need to make my application

production ready with the docker. But

today, you see what is happening in the

market.

So, 2 days of work

earlier. So, if you give this task for

one ex-3 years experienced developer,

they will take minimum 2 to 3 days of

time to complete this coding. And that

too, there is no guarantee that code

will work as expected or not.

And there is no guarantee that the code

is going to work as expected or not when

a person developed that logic in the

project. But today, whatever the

requirement that I'm having, I'm giving

my I'm giving my requirement to the

ChatGPT, one of the AI tool. How does

the ChatGPT is working? Internally,

ChatGPT tool is connected to one LLM.

So, whatever the question that I asked

to the ChatGPT, this question is called

as a prompt. And whatever the response

I'm going to get from the ChatGPT LLM,

that is called a token. Based on the

number of tokens they are giving to me,

the charges applicable.

The charges applicable. They It is

thinking. It is analyzing my

requirement, and it is going to write

the code line by line for me.

It is analyzing my requirement, and it

is going to write the code line by line

for me.

So, this is called wipe coding. So,

earlier, the most difficult job in the

IT industry was development. Today, that

the development process became very

easy.

Today, the development process became

very easy because of this white coding.

So, ChatGPT is thinking, it is analyzing

my requirement, and it will write the

code for me. You write the code for me.

Let's wait for some time. So, earlier,

people are following manual coding

process. Today, people are following

white coding. White coding means you

don't need to write the code line by

line on your own. You just need to tell

your requirement, then ChatGPT is going

to do that code. Not only ChatGPT, there

are so many tools already available in

the market. There are so many tools

already available in the market. Now,

see here, it started writing the code

for me. I created I just given my input

in two lines, guys.

I have given my input just in two lines.

That is a prompt. So, what this AI tool

is doing? So, it is creating a project

with all these functionalities. User

registration, login, JWT token, refresh

token, logout and token revocation,

bcrypt bcrypt password hashing,

role-based authorization, Postgres

integration, okay? Flyway database

migration, global exception handling,

request validation, Docker and Docker

Compose, actuator health endpoint,

externalized production

Oh my god.

So So many things it is implementing,

guys. Project structure it is creating

for me. And it created the actual

endpoints for my application, API

endpoints, JSON request, JSON response,

okay? Refresh token logic they have

provided. And all these things it is

implementing for us.

All these things it is implementing for

us. This is how download the complete

Spring Boot project. They have given the

code in the downloadable format.

So, if I click on this link, the project

will be downloaded as a zip file, guys.

If I click on this now, you see the

project is getting downloaded as a zip

file for us.

Now, if I want to develop the same

project on my own,

if I want to develop the same project on

my own, how much time it will take for

me being a developer?

If I want to develop the same kind of

project, how much time it will take for

us guys? Now, whatever the task I have

given, it will develop the project and

given a zip file for me. Just I can

extract and import and I can run. So,

maximum it will take 5 minutes of time

to complete the task now.

Three days of task we completed in the 5

minutes. This is AI revolution.

Now, tell me, still do you write the

code line by line in your company?

Tell me, still do you

still do you think that companies are

expecting a developer who can write

everything on their own?

No. Companies are looking for white

coders.

Three days of work is reduced to 5

minutes of time.

24 hours of work is getting completed in

5 minutes of time. That is the change in

the IT industry today.

Now, how can you skip the AI? Is AI

optional for anybody now?

You tell me.

Is AI optional for anybody now?

Come on guys, please respond.

Is AI optional for anybody now?

AI is not optional for anybody today.

AI is not optional for anybody today.

Are you getting my point?

Are you guys getting my point?

Are you getting my point?

Very good.

Very good. So, earlier the coding was

one of the most difficult task, but

today the coding is one of the most

easiest task because of this white

coding. Somebody is asking, are you

going to cover NLP? Yes, sir, NLP will

be covered.

NLP will be covered.

Okay, good. So, here here as the white

coding is becoming very popular in the

market, you should not be as a AI user,

you need to become a AI developer. So,

that's where we are starting this Gen AI

as in TK development with the Python.

Sir, coming to our course details,

coming to our course details, what we

are going to cover, what is the

information regarding this course. So,

this course is starting from today,

guys. Today is our first session.

Today is our first session. Course is

starting from today. And what are the

daily class timings? Daily class timings

will be there from 7:00 p.m. to 8:15

p.m. IST. Daily 1 hour 15 minutes of

session will be available. And weekly

weekly the classes will be five. Weekly

five days will be available. Monday to

Friday the classes are available for us.

And the course duration, it is going to

take three months of time. Three months

of time. And the mode is online classes.

Daily online live classes will be

available. As I told you, for this

complete training the trainer is myself.

My name is Ashok. And the coming to the

course of fee structure, this course

fees is 25,000 rupees for three months

of time.

And what you are going to get as part of

this course, guys? What students will

get? So, you are going to attend daily

live classes. You are going to attend

daily live classes and you are going to

get the soft copy materials. You are

going to get the class recordings and

you are going to get the real-time

projects development and the recording

access will be there 1 year after course

completion.

So, daily live classes you are going to

attend. Soft copy materials will be

available. Class recordings will be

available. Class recordings will be

available.

Okay, guys? Recording access will be

there for 1 year after course

completion. Real-time projects

development will be available for you

and interview preparation will be

available for you. Resume building will

be available for you. Okay? Practical

coding sessions will be available.

Practical coding sessions, AI tools

exposure will be there for you and GenAI

and AgentDKI projects development you

are going to do.

GenAI and AgentDKI projects development

we are going to do. So, this is what you

are going to get as part of this

training.

Okay, guys? So, live classes, soft copy

materials, class recordings,

live classes, soft copy materials, class

recordings, recordings validity will be

there 1 year after course completion.

Recordings will be there 1 year after

course completion. Real-time projects

development, real-time projects

development, interview preparation,

resume building, practical coding

sessions, AI tools exposure, GenAI and

AgentDKI projects development.

Okay?

Are you clear with my point?

Are you clear with my point? Now, sir,

after attending this program, what kind

of job roles you can apply?

What kind of job roles you can apply

after attending this program? So, if I

join for this GenAI and Agent-DKI

development with Python, what job roles

I can apply?

After completing this program, students

can prepare for the roles such as data

scientist.

You can apply for the job as a data

scientist. You can apply for the job as

a AI engineer.

You can apply for the job as a ML

engineer.

You can apply for the job as a GenAI and

Agent-DKI developer.

You can apply for AI application

developer.

And you can also apply for AI tester.

You can You are learning complete

Python. You can go as a Python

developer, LLM application developer,

rag developer, AI automation developer.

So, these kind of job roles that you can

apply.

You These kind of job roles that you can

apply after learning these eight modules

or seven modules that I have mentioned

in the course.

Whatever the seven modules, eight

modules that I have mentioned in the

course, you can apply for all these job

roles.

Data scientist, AI engineer, and machine

learning engineer, GenAI developer,

Agent-DKI developer.

Okay? And AI application developer, AI

tester, Python developer, LLM

application developer,

rag developer, AI automation developer.

Okay? So, if you are really interested,

you can also go for ML Ops engineer.

You can go for ML Ops engineer. So,

these kind of roles that you can apply

after attending this training.

So, final message, guys. So, from my

side for today, the final message that I

want to give you. So, you join for this

GenAI and Agent-DKI development with the

Python and start building your career in

the next generation of software

development. So, you need if you want to

survive in the market, if you want to

get more opportunities in the IT

industry today, you need to learn

Python, AI, ML, deep learning, LLMs,

prompt engineering, rag, LangChain,

LangGraph, MCP, AI agents, MLOps from

basics to advanced level. So, start your

career start your career journey with

Ashok IT. Start your AI career journey

with Ashok IT from today onwards. So,

this is what the final message that I

want to give you. So, don't be as just

AI user.

Don't be as AI user.

Okay? Become GenAI developer.

So, this is my final message for you.

You don't just be as a AI user because

already in this world everyone is using

the AI tools. Everybody is a AI user.

So, you don't stop at the AI usage only.

You become AI developer also.

You become AI developer also.

Are you guys getting my point?

So, don't be as just AI user, you become

AI developer.

Don't be as AI user, become GenAI

developer.

Are you clear with my point?

Are you guys clear with my point?

Don't just be an AI user. You become

GenAI developer, AGI developer. Good

guys, so a quick summary. So, today's

class notes and class video I'm going to

share in the Google Classroom. Google

Classroom link I posted in the chat box.

Okay? So, all of you who are part of the

Google Classroom, can you check it?

Today notes I'm uploading right now.

Whatever the notes I have prepared

today, I'm uploading in the Google

Classroom. Could you please check it and

let me know are you able to access?

1 minute.

1 minute. Today notes I'm uploading and

today class video also I'm going to

provide.

Yeah, so now the people who are having

the questions for me,

the people who are having questions for

me in the Zoom, there is the option

called raise hand.

In the Zoom there is the option called

raise hand.

Then click on the raise hand.

Then you can talk to me.

Okay? So, check in the Google Classroom.

Are you able to access notes now?

Are you able to access the notes now?

Today's notes I have posted already.

In the Google Classroom I posted today's

notes.

Today's notes and today's video link

also I'm posting. We have given YouTube

live YouTube live video link I'm

posting. In the Google Classroom I

posted that.

Okay, guys?

Today's notes I posted. Today's video

also I posted in the Google Classroom.

Anybody who is not part of the Google

Classroom, you please click on that link

and join.

Very good. So, what we discussed today?

Today we discussed about our journey as

an DK course overview, who is the

trainer for this course, and currently

what is the market trend, and what

companies are expecting. What is this

program overview? What is our main goal?

And what are the prerequisites? And what

you are going to learn? And what is our

course content? What topics I'm going to

cover? And when is the batch start date?

What is the regular class timing? weekly

how many classes will be available, what

is the fee structure, what is the course

duration, and after joining this course,

what you are going to get.

What you are going to get, and what job

roles you can apply once you learn this

GenAI and Agent AI development with

Python. And being a trainer from last 10

years, I'm in the software industry, and

I'm giving my final message for you.

Don't just be an AI user, become a GenAI

developer. So, that role is currently

having too much demand in the market.

Good. Thank you, guys. So, with this we

are done for today, and you guys can

attend next two three classes for free

of cost with the same Zoom link. So,

next two three days, you can attend the

class at the same time, 7:00 p.m. With

the same Zoom link, you can attend. In

the Google Classroom, in the Google

Classroom, I'm going to give you all the

updates. In the Google Classroom, I'm

going to give you all the updates. Notes

I posted, and video also I posted. Next

two three days, you can attend the free

classes with the same link. Okay? So,

from tomorrow onwards, we are going to

understand LLM architecture, data

science, GenAI, Agent AI, NLP, ML, DL,

all those concepts we are going to

discuss in depth from tomorrow.

Good. Thank you. With this, I'm stopping

recording. Anybody who is having

question, they can raise the hand, and

they can talk to me. The people who are

watching live in the YouTube, thank you

for Thank you, guys. Thanks for

watching. Please subscribe to our

channel, and we'll see you in the

tomorrow's session.

So, for you also, Google Classroom link

I posted in the chat box. So, click on

that link and join. You can access notes

and video.

Thank you.

Have a great evening. We'll meet again

tomorrow.

So, payment details you can do after

three days of time. Next three classes

free of cost, same time, same link.

Yeah.

Guys fine, who are having questions,

they can raise the hand and they can

unmute. Meghana, yeah, can you speak

out?

Hi Meghana.

What is your doubt? Meghana, Bhushan,

Ravi,

Parvinder, Gafur, Tejas, Sonal, Yogesh,

Madhusudan,

Vinayak, Madhu Kumar, Saurav, Jeddu.

>> Sir, Gafur here.

>> Hi Gafur, please go ahead, sir.

>> Sir, are we going to cover the

probability and statistics also over the

years, sir?

>> Yes, yes, yes. As part of the libraries,

we are going to discuss.

>> Okay, as part of the Python libraries,

sir.

>> Correct, sir.

>> Okay.

Sir, are we going to cover

NLP also is part of the price, sir?

>> Yes, sir. Yes, sir. When I'm When I'm

talking about ML DL, we are going to

learn that also.

>> Okay. So, are you providing the

real-time project also, sir?

>> Yes, sir. For every concept, it will be

available, sir.

>> Okay. And one more thing,

do you have EMI options, sir?

>> Sorry?

>> EMI options.

>> Yes, sir. We do have that, sir. We do

have.

>> Okay. Thank you, sir.

Sir, sir, will you cover the agent

integrate with cloud environments like

Azure and

AWS?

>> Cloud deployment is covered, sir. Yes.

>> For integration with all the cloud

environment, right?

>> Yes, yes, yes, yes, yes.

>> And one more thing, are you able to

cover Anaconda in Python?

>> Yes, sir. We will cover that as well.

>> Okay. And lastly, will you able to I

mean, every week we can do I mean, like

any assignments, will you give the

assignments or not?

>> Daily tasks will be available for you

people. Daily we will give the tasks.

>> Sir, and you said real-time projects and

real-time projects, how many projects

you will give?

>> So, approximately 10 projects we are

going to develop.

>> Okay, that that is the important enough

to drive in the real job?

>> 100%

>> Sir, one more thing, I'm experienced

guy, I'm more than 15 years. So, after

this course, how many years of

experience I can put into my resume?

>> Sir, so your 15 years experience you can

say the last 2 and 1/2 years, 3 years

you can keep on the journey, yeah.

>> Okay, sir. And educate recently came to

1 and 1/2 years, 2 years, right?

>> Right, right, right.

>> Okay, sir.

>> Good, sir.

>> Yeah, hello sir, am I audible?

>> Madhusudan

>> Hello,

Madhusudan here.

Uh yeah, nice talking like today's

session is very nice. Uh we understood

what are the things Yeah, what are the

things we have to we are covering here.

So, my doubt is like if we have some

doubt, uh so can we have any Are we

creating any uh WhatsApp group or like

>> So, as of now, currently I will I will

give my WhatsApp number, sir. Every day

in the class, last 15 minutes will be

there for the doubts clarification. So,

if you have any problem, you can

directly ping me. I will give my

personal WhatsApp number.

>> Okay. Okay.

>> Sir, any offline classes, sir?

>> One by one.

>> Yeah, hello.

Hello.

>> Yes, sir.

>> Yeah, hi Ashish sir. Ravi here.

>> Yeah, hi.

>> Yeah, I just want to means I want to

talk with you before joining the class.

>> Mhm.

>> So, we did try to connect to call me at

6:00 p.m., but

because

I'm working

>> I have given my WhatsApp number in the

chat box, guys. So, you can ping me in

the WhatsApp, so that we can connect.

>> Actually, I

I am from production support background,

okay?

So, I I want to uh means switch So, is

it possible after completing the course

means

>> Yes, sir. Yes, yes, yes, You can do

that, sir.

>> And means how much coding is required

for this course?

>> How much coding? Like coding only, sir.

Not real coding, so only like coding.

Basic Python is required that I'm going

to cover in the course.

>> Okay.

>> Basic Python is required that I'm

covering in the course. We are going to

spend approximately 20 days to learn the

Python fundamentals, then 15 days we'll

spend for Python libraries.

From the Python libraries, we can say

that our data science is started.

>> Uh because means

I am the means

means basic is also not clear, concept

is not clear. No, that's why I just

>> No, no. No problem. I am I am the main

my job is to make you to understand this

Python and all those things, no?

>> Okay.

>> Sir, how many days we have offline

classes, sir?

>> Sorry?

>> Any offline classes?