AI Engineer Roadmap | How I'd Learn AI in 2026

The field of AI is evolving very fast.

Every month there is a release of new SDK framework. Big tech companies are

SDK framework. Big tech companies are releasing LLMs every few months. And if

you want to become AI engineer, you will have this natural confusion. What skills

do you need to learn and in what order?

Now when you go to YouTube, you will find tons of road map videos. Some of

these road map videos are created by people who themselves don't have any AI experience. When I started building this

experience. When I started building this road map, I wanted it to be based on absolute reality. So we did an

absolute reality. So we did an interesting exercise. We analyzed 700

interesting exercise. We analyzed 700 plus latest AI engineer jobs from different job portals and created the list of hot skills which are in demand.

So these are the skills that employers are looking for as per these latest jobs that we analyzed. We then combined this job analysis with our own experience of

working on AI projects in our company ATL. ATL is an AI and data services

ATL. ATL is an AI and data services company which I and my brother founded and in last 2 years alone we have worked on 25 plus AI projects mostly from small

to mediumscale enterprises based in US.

As a result we have prepared this practical road map with weekby-eek study plan, free learning resources and checklists. I'm not going to say

checklists. I'm not going to say anything unrealistic to increase views of this video. This road map will require 4 hours of study and a time period of anywhere between 6 to 8 months. So if you're looking for a

months. So if you're looking for a shortcut, this is not the right place for you. You can leave this video right

for you. You can leave this video right now. Before jumping into the road map,

now. Before jumping into the road map, let me discuss few things very quickly.

I want to talk about salaries. I want to talk about different category of AI engineers. This information will be very

engineers. This information will be very useful to you before you get into the road map because based on what category of AI engineer you want to become you'll

be learning different skills. Okay. On

the screen I'm sharing uh the some of the salaries for AI engineers. So if you go to no.com you will find this range where the salaries are anywhere from 0

to three lakh all the way till 5 K. You

see this you you get salary up to 5 cr if you are a senior director etc. And this is the histogram that I created of real data. This is the real data folks

real data. This is the real data folks for all the jobs of AI engineer on no.com which is an Indian job portal and you can see that majority of the jobs

are in this range 6 to 10 lakh and 10 to 15 lakh. So you can say that 6 to 15

15 lakh. So you can say that 6 to 15 lakh is a common salary range and we found some 89 jobs which had a salary

range of 1 to 5 cr and these are senior directors etc. But if you start becoming AI engineer, this at least shows you the potential that if you have skills, you

can reach this kind of financial rewards. And then in US, the median

rewards. And then in US, the median compensation is $152,000.

But let's say if you're working in Silicon Valley for some big tech companies, you can get salaries in a range of $400,000 to $600,000 a year. I

have many friends who work in Silicon Valley. So whatever I'm telling you is

Valley. So whatever I'm telling you is based on the real information. If you

want to know how many jobs are available then once again you can go to any job portal and find out. No is showing 37,000 AI engineer jobs right now. Now

they use semantic search for this. So

sometimes you will find some software engineer jobs also in this list but you can rely on this number you know ballpark uh this is a good number. And

then for ML engineers there are 111,000 jobs. So this road map is going to be

jobs. So this road map is going to be valid for AI engineer, ML engineer, there is a IML engineer. You know

companies will post jobs using this different job titles. Even the road map is valid for geni engineer. Okay, genai

is a special category of AI engineer who focuses mainly on generative AI. Now

once you decided that okay AI engineer as a career role sounds very good.

Second thing you need to check is whether your natural skills are aligned with AI engineer role or not. And for

this purpose we have created this uh suitability test. So what this test will

suitability test. So what this test will do is it will ask you bunch of questions on your inclination towards math your learning attitude and so on and based on

that it will tell you the percentage matching. It's like a matchmaking

matching. It's like a matchmaking between you know boy and girl. This will

tell you a percentage matchmaking. It

will tell you whether this career is right for you or not. Now folks, here is the code that we used for analyzing those jobs. And if you look at here for

those jobs. And if you look at here for AI engineer jobs, these are the skills in demand. So, Python, machine learning,

in demand. So, Python, machine learning, AI is a common term. PyTorch, Azure, you can see that Azure is in more demand

compared to AWS. We did this for different roles. AI researcher for

different roles. AI researcher for example is a different role. There are

not that many jobs available for AI researchers but it's a very fulfilling career. If you have interest in

career. If you have interest in algorithm, you like to write CUDA kernel, C++, you care about performance etc. then this is a right career role

for you. Then a IML architect. Then we

for you. Then a IML architect. Then we

have uh let's say we have different roles. See data engineer all kind of

roles. See data engineer all kind of roles. data scientist. By the way, in

roles. data scientist. By the way, in this road map, we are not talking too much about data scientist. Maybe we'll

create a different road map for that particular role. All right. So, I'm

particular role. All right. So, I'm

going to share this code repository with you. And what you can do is let's say

you. And what you can do is let's say you are watching this video 2 years after this posting you know or after few months. And if you want to do latest

months. And if you want to do latest analysis, then just use this code and find out the answer yourself. So, here

are the tech skills and core skills that you need to learn. As you can see in the picture, there are many things that you have to learn. But after all, AI engineer career is very fulfilling and

they pay you high. So of of course they will demand all these job skills. And

then there are optional skills. For

example, cloud AI, reinforcement learning. These are optional skills.

learning. These are optional skills.

Now, if you are a fresher, they will not probably ask you about cloud AI. But

let's say if you are an experienced person, let's say you are a software engineer, backend engineer, and you want to learn AI and let's say you learned all these skills. Okay, you also need to have knowledge on at least one cloud

either AWS or Azure. Okay, so now let's talk about AI engineer categories. So

although you find tons of jobs with a title AI engineer, when you look at the actual work, it can be divided into three categories. Integrator, builder

three categories. Integrator, builder and allrounder. Allrounder is a mix of

and allrounder. Allrounder is a mix of integrator and builder. So let me show you one job post for the first category which is integrator. So here is a job

post from Deutsche Bank for AI engineer and if you read the description it says that you will collaborate with data scientists to deploy machine learning models which means data scientists will actually train the model and then you

are involved with deploying those models. Then you will uh design and

models. Then you will uh design and manage the infrastructure required for hosting ML models. Okay, including crowd resources. you will do CI/CD, docker

resources. you will do CI/CD, docker things like that. So essentially

somebody else is training a model and then you are responsible for deploying it into production integrating it with rest of the systems and of course you

need to have knowledge on DevOps, MLOps, variety of things. The second category is builder category. Now in builder category there are multiple job roles.

For example, AI research engineer job role. Now this is the job role for which

role. Now this is the job role for which big companies right let's say open AI Google all these companies will have

this kind of job post they are building this new LLMs they are building novel approaches for training these models so they need research engineers even the

big academic institutes like like for example IIT Stanford they will need AI research engineers some of the startups which which are doing some innovative

work they will need this AI research engineers. So if you have lot of

engineers. So if you have lot of interest in math uh performance algorithms C++ etc then you can go for this. The

second category of builders is applied scientist. Now applied scientist is a

scientist. Now applied scientist is a person for example you're working in Amazon as an applied scientist you will be building the recommendation algorithm for Amazon products. when you go to

Amazon website when you're buying any product you see recommended products. So

there is an algorithm ML program that runs behind the scenes and these programs are designed by applied scientist. So for applied scientist they

scientist. So for applied scientist they are very much product focused. Okay. So

you can say this one is a product focused ML. Then there is third category

focused ML. Then there is third category data scientist. So data scientist is

data scientist. So data scientist is mostly business focused ML. Okay,

business focused ML. What I mean by that is data scientists will be hired by even small companies, banks or healthcare institutes. They want to build custom

institutes. They want to build custom statistical models. Okay. So data

statistical models. Okay. So data

scientists mostly build statistical or deep ML models. They are good in math, statistics, etc. and they build ML solutions which are business focused

whereas applied scientists build ML solutions which are product focused. So

these are hired mostly by product companies. So I'm showing you a job post

companies. So I'm showing you a job post from Reuters. So Reuters has this senior

from Reuters. So Reuters has this senior applied scientist position and if you look at the job description see they will usually require PhDs. Okay, you

need to have they will demand a lot of things actually. Okay, you need to be very deep

actually. Okay, you need to be very deep into it. You need to be in a position

into it. You need to be in a position where you can publish findings on top tier conferences like new IPS. Okay. So

you need to have a deep curiosity, deep knowledge of math, statistics, algorithm etc. And then you will find this kind of job post also. This almost sounds like a data scientist job where you are

designing and developing a IML models.

Okay, you are doing processing, cleaning, analyzing data. Okay, then you are developing end toend AI pipelines which means take the data clean it

pre-process it feature engineering train the model evaluate it etc and once the model is ready you collaborate with software engineers or other integrators

to integrate these models into productions okay so this sounds similar to data scientist but they are posting using AI engineer job post so folks all

lot of these jobs will have multiple expectation okay and sometimes employer is also not very clear when they are posting these job post. So when you start working in these companies you

will get diverse set of work especially if you are in a startup the work you will get will contain lot of diversity.

Now let me play a clip from my conversation with Karan who is an AI head and CEO at Atl Technologies. What

are the top three skills you look for when hiring an AI engineer?

>> That is a good question also. We of

course we at Technologies we hire AI engineers. We have been actually hiring

engineers. We have been actually hiring one or two right now as well. So if you talk about three top skills that we would look at while hiring. So the

number one would be what is the breadth of the AI knowledge that they have. I'm

not talking about them knowing geni. Of

course everybody right now is knowing geni for some reason. But how good are they at the basics? So do they know statistics probability math basic math of course. Do they understand how

rulebased systems work? Can they study the data and create their own rulebased systems? Can they like deploy basic

systems? Can they like deploy basic machine learning models like uh like XG boost decision trees or or even simpler models like that? Can they work on their own deep learning model? Can they

fine-tune? Can they train? So what I what I look for usually is are they having depth of AI knowledge that is needed in the industry or they are just like a gen expert. If they're a gen expert, I'm not actually looking to hire

them. If they know things, if they have

them. If they know things, if they have curiosity to learn more about things, that's a good sign. But only a geni is going to always be a red flag for me.

The second thing that I always look at is prototyping. So right now there is a

is prototyping. So right now there is a lot of hype in AI and a lot of use cases fail as well. So prototyping is actually a very useful skill for all the AI

engineers out there. So what what's needed is given a problem, given a use case, you should be able to do like a quick prototype in a couple of days. It

could be 1 day, 2, day 1, 5 days. But

the prototyping would result into uh like knowing is this a appropriate problem to solve or is this something which is going to like return any results if we solve it or not or how

would it like even feel while we are using AI. So it is an immensely useful

using AI. So it is an immensely useful skill. We actually train people who like

skill. We actually train people who like people and expect this out of all our existing employees as well that you should be good at prototyping when it comes to AI. Otherwise it's a red flag

again for us. U the third thing which is countering more towards hype is the communication skill. You would say that

communication skill. You would say that communication is needed in all the roles but in AI the need is even increasing.

Why? Because when when you're at crossroads with client when you are asking them certain things or if they're explaining them certain things they might have already used chat GP for asking those questions or like

understanding what we are saying. Now

this creates a divide. Why? Chad Gypy

could give you a hallucinated answer or it could give you something which is not taking like all the context in the consideration.

Now if you look at this you need your AI engineer you need your AI team to convince clients to negotiate with them to come to a similar page. Of course the product team would do that but you also

need your engineering team your AI engineering team to come on the same page and do help your product teams. So if you're hiring for an AI engineer or

let's say if you're an engineer aspiring to be an AI engineer you should look at three basic skills the breadth of AI knowledge prototyping and how good are

you at communicating things. Whatever

Karan said is reflected in the AI engineer job that we have in technologies. So if you look at this job

technologies. So if you look at this job you will find in the description that we believe that not every problem needs geni hammer. You need to have a

geni hammer. You need to have a knowledge on diverse set of skills.

Statistical ML deep learning u see here you will see agentic AI system ML and DL frameworks pytor statistical ML ability

to prototype quickly and so on. Now this

represents this third category which is allrounder. Allrounder is a person who

allrounder. Allrounder is a person who knows statistical ML, deep learning, NLP, geni they know everything. Now you must

be thinking that okay you expect too much. Uh how is that? Well this is true

much. Uh how is that? Well this is true for small companies. Okay, small

companies usually need allrounders and small companies don't expect the person to be very much like highly skilled in in all of this. Okay, it's

like a you are jack of all trades.

Whereas if you're a builder, let's say if you're working for Amazon and if they have ML in their job post, they want you to know ML in depth. You should be in a

position where you can write even custom algorithm for the ML model. Okay. So the

expectation the skill expectation for each of these categories is very high.

Whereas if you work for small companies you need to know this but even if you're average it is going to be good enough and they do that because I will give you example of ATL sometimes we have short-term project. So let's say there

short-term project. So let's say there is a 3mon project where you have to use statistical ML and we have one AI engineer let's say moan is the AI engineer working for ATL. Now for 3

months he's working in statistical ML.

After that we may not have statistical ML project. we may have another six

ML project. we may have another six month project which is purely in jai at that time we are not going to hire one more engineer and and moan will sit idle right that that's not going to work

therefore in small companies mostly they will look for allrounder whereas in big companies they can have these two different roles okay so integrator and builder is like a two different roles

builder kind of roles you will mainly find in product companies like Amazon, Google etc. Integrated roles are more common in consulting companies like okay

you are working in Accenture okay or cognizant in these companies or let's say big four right PWC etc they will be using readym made model let's say you

are using readymade LLM you're using claude or gemini and you want to integrate it to solve some real business problem okay so here these integrators

are good software engineers they know certain ML skills they have good business understanding, they are good in communication etc. When it comes to

allrounder role, you are a jack of all trades. And I compare it with this

trades. And I compare it with this analogy of a handyman. Let's say you are a handyman. Now you know that if you

a handyman. Now you know that if you want to put a nail in the wall, you need to use this hammer. If you want to remove the bolt from your car tire, you

need to use this particular tool.

Similarly, a skilled allrounder AI engineer will have knowledge of all these different tooling including nonML approaches such as rulebased system and

they will have this judgment to figure out what tool to use at what time. Let

me share again a real experience at Atlake. We had a client and we had this

Atlake. We had a client and we had this project where one aspect of that project was doing a log classification. So this

client is a US-based client in finance domain and at first we thought we can use jai for classifying all the logs and

that solution was actually working but it was not the optimal solution because it will incur higher API cost for the client also there is less AI

explanability. So what we did is we

explanability. So what we did is we identified category of logs where we can use regular expression. So around 80% logs had fixed patterns. So if you use

Python regular expression you will be able to classify those. Then for the remaining categories we found 15% of logs such that they had some patterns.

They did not have fixed patterns which can be captured by regax but they had some patterns which can be captured by statistical machine learning. So we

generated BERT encoding and then we used statistical ML approach let's say XG boost or naive base to classify those and for remaining categories remaining

five person where we did not even had enough training samples okay so for those we used LLM classification now as

you can see clearly in this picture we used rulebased system here right rule or traditional programming so this is your tool number one. Then here you use

statistical ML, statistical ML. So this is as a handyman if you're

ML. So this is as a handyman if you're thinking this is your tool number two and then generative AI. So it's a hybrid solution and when you work in any

industry project you will not find a case where you build one fancy model and your entire problem is solved. Usually

you have to subdivide the problem into multiple subtask and use different solution for each of these subtask. So

usually you will end up in a hybrid solution. Therefore a good allrounder AI

solution. Therefore a good allrounder AI engineer will aim to become a problem solver with strong basics wide tool sets and the judgment to pick the right tool

at the right time. Okay. So here when I say why tool set I I'm not saying okay you learn langraph and then crew AI you just accumulate this bunch of tools you

need to have strong fundamentals and you need to have this judgment on what tool to pick at what time. Now in our road map you will see all these skills and I

have built this AI engineer skill matrix where based on the category of the AI engineer that you are aiming you can focus more or less on these skills. For

example, if you are a builder let's say you are building models from scratch.

It's okay if you don't know SQL too much. It's okay if you don't know MLOps

much. It's okay if you don't know MLOps or DevOps too much because you're mostly building the new models. Now, if you are an integrator,

you have to know MLOps. See, this green means high level of skill. Medium is

orange, red is low. But as an integrator, if you don't know statistical ML or deep ML, it is okay because you will not be building these models from scratch. Now this category

jai I'm saying green because you are using LLM then other than LLM see majority of the companies they don't train LLMs okay

LLMs are trained by only few companies in the world it's a whether anthropic or open AAI or Google etc majority of the companies when they build geni solution

they are using rag they are using vector databases therefore I have green okay so here this ji skills is not building LLMs from scratch but knowing the

technologies of geni which is rag vector databases uh agentic AI and so on when it comes to optional skills see cloud AI is a skill that even if a builder

doesn't know it's okay but as an integrator and allrounder you need to be good in it now if you're a fresher it's okay if you don't know cloud AI but if you're good in cloud as a fresher it

will give you unfair advantage okay so you can pause this video you can look at the skills. Uh these are the core skills

the skills. Uh these are the core skills right? Communication, math, statistics,

right? Communication, math, statistics, business understanding and so on. Here

is the actual road map PDF. In week one we will learn AI basics and beginners Python. We first need to understand what

Python. We first need to understand what is the difference between statistical ML, deep ML, NLP, genai, agentic AI all of that and we have this single YouTube

video in which this entire AI landscape is covered. So for example statistical

is covered. So for example statistical ML, deep ML is part of ML. Then you have few things such as rulebased system etc

which comes under AI. Jai, agent AI are the fields which mainly utilizes deep learning. Okay. So this is a 1hour long

learning. Okay. So this is a 1hour long video where we have gone over different concepts right neural network CNN LLM

and after going through this video you will have good clarity on the AI landscape then you will start learning the fundamentals of Python now why

Python well didn't you look at the hot skill diagram most of the jobs AI engineer jobs demand Python Python is the top skill when it comes to AI

engineer role. So you need to go over

engineer role. So you need to go over all these concepts and learn basics. We

have a YouTube playlist for this. So let

me just show you that particular playlist and you need to go over uh certain videos here like the first 16.

You can learn Python from other resources as well. And then by learning Python fundamentals eventually in the future you'll be able to write this code. So this is the code snippet of

code. So this is the code snippet of training a statistical machine learning model using Python. So this is all Python code. So you probably now

Python code. So you probably now understand that whether you are in genai, deep ML, statistical ML, you will be using Python everywhere. Python is like God. It's

everywhere. Python is like God. It's

everywhere. Now during the same time period, you will start building your LinkedIn profile. We are not going to

LinkedIn profile. We are not going to wait till the end. LinkedIn is extremely crucial for your online credibility and we have provided a checklist. So if you

click this particular link here, you will find a checklist. So just go through this checklist, act on all these items and in the end you will have a professionallook profile. Now why is

professionallook profile. Now why is LinkedIn important? Let me give you

LinkedIn important? Let me give you analogy. Let's say you know how to make

analogy. Let's say you know how to make samosa really well and you have this shop on the right hand side in a village. Now in a village there is

village. Now in a village there is hardly any footfall. So you are not getting any business. But somebody

advises you to move your shop in a busy street in Delhi and you do that and all of a sudden your business grows 10x.

Why? Because you are on a street where many people are passing. So LinkedIn is that street where there are many recruiters, many managers who who who are present and they will notice your

profile. So LinkedIn is like a busy

profile. So LinkedIn is like a busy street where you will get an opportunity to showcase your skills to a wide range of people. And folks, building online

of people. And folks, building online credibility, building relationship takes time. Therefore, you should not wait all

time. Therefore, you should not wait all the way till end. Many people are like, okay, let me learn technical skills first and I will do LinkedIn later on.

No, you should do it from day one. In

week two, you will learn data structures and algorithm. And in data structures,

and algorithm. And in data structures, uh it's okay if you learn these many and you can skip the rest. In the algorithm, I think if you learn one search and one

short algorithm, it is good. uh as of this stage and later on as in when you need to learn new algorithms you can learn it in terms of learning resources

I have this playlist here so let me show you that playlist and it has got lot of views see millions of views and just look at all the topics which I have mentioned in the road map for example

you don't need to go over merge short insertion short share sort etc right I have mentioned the videos that you can skip here then in terms of core skills

you need to start working on your communication. For this, the excellent

communication. For this, the excellent resource is Toastmasters. So, this is a free resource. They have clubs

free resource. They have clubs everywhere. So, click on find visit a

everywhere. So, click on find visit a club and enter your uh city. Let's say

you are in Hyderabad. And in the Hyderabad, see there are so many clubs which are present. I have attended these sessions. Here you can practice your

sessions. Here you can practice your verbal skills. Okay? you will practice

verbal skills. Okay? you will practice your negotiation skills. It is totally free folks. So use this platform to

free folks. So use this platform to improve your communication skills. And

in this same week, I will advise you to watch this conversation which I had with a senior director of fractal. Okay. And

here in this conversation, we even discuss what are the skills that Rishi looks at when he's hiring people. See,

he emphasized on three qualities.

Humble, hungry, and smart characters. So

looks like these companies they often focus more on soft skills than the hard technical skills. In the age of AI

technical skills. In the age of AI coding is done by AI. So the importance of soft skills have gone up. In terms of assignments, you will find uh the

assignments in this same playlist. Okay.

So whatever playlist I have here uh I will have a corresponding GitHub repository and that GitHub repository you will find in the video description.

So let's see if I go to video description here. See I will find this

description here. See I will find this and there will be an exercise. Okay. So

I have an exercise for Python pretty much everything. See these are the

much everything. See these are the exercises for Python. Let's say read and write file. Okay. So there are nice

write file. Okay. So there are nice exercises which are given here. You can

also use chat GPT by the way to practice your concept. Let's say you are learning

your concept. Let's say you are learning Python dictionaries. Okay. You can say

Python dictionaries. Okay. You can say I'm learning Python dictionaries. Give

me one simple coding exercise. And Jet

GPT is like this one-on-one tutor that you can work with. Okay. So you can write a code then you can give that code to Jet GPT. You can ask it to evaluate it and that way you have a conversation

and you can work on variety of assignments. In week three after you

assignments. In week three after you have cleared the fundamentals of data structures and algorithm you will learn some advanced concepts in Python. Okay.

So you will refer to the same playlist and in that go to video number 17 to 27 where you will learn all these concepts

and then there are exercises in the same playlist. In terms of core skills see as

playlist. In terms of core skills see as a first step you created a LinkedIn profile professional looking LinkedIn profile as a second step you will start

following some of the influencers. Yan

Leon for example he's the person who invented convolutional neural network okay and he writes many good post so what you can do is you can read through

his post and you can also start engaging you can start commenting etc and you will realize the benefit of this commenting process later on but let me tell you it has helped me tremendously

in my career and it will help you too you will find many prominent AI influencers on Twitter actually so there are more active on Twitter than

LinkedIn. For example, Andre Karpathi.

LinkedIn. For example, Andre Karpathi.

Okay. So, Andre Karpathi writes a lot of useful post. So, you can read through

useful post. So, you can read through it. You can comment on it. Sometimes

it. You can comment on it. Sometimes

these guys will even respond to your comments. Remember that online presence

comments. Remember that online presence is a new form of réumé. Then to improve your business fundamentals, see many companies if you look at job description, you know, I will advise you

to look at all these job post just spend one or two hours just reading through various descriptions that will give you the reality of the job market. Okay?

What uh employers are looking for. One

common mistake that people make is they learn some skills by following some random YouTube video and then in the end they go to these job portals. you should

first go to this job portal and understand what employers are looking for and then you prepare for those skills which are relevant. Okay. So

business fundamentals I have seen that I was talking with a director of data engineering for a big pharma company here in New York and what he told me is

that they look for people who have domain knowledge in pharma. So domain

knowledge is very important and you can build a domain knowledge and business understanding by going over some business case study YouTube channels such as think school they have all these case studies and in that case studies

you will understand accounting principles, business principles and so on. Then you should also start

on. Then you should also start participating in prominent discord servers. For example, for code basics,

servers. For example, for code basics, we have this particular discord server where you will find there are like 50,000 people, 51,000 people here. And

let's say if you have SQL question, you ask here, you have a question on Python, you ask here. Okay? So by interacting with people, you are developing your communication skills, which is going to

be very crucial because when you work in any company, you'll be working in a team and you are interacting with people. So

you need to have good communication even you're interacting with business stakeholder you know sometimes there is this uh discipline to communication how you answer how you become polite how you

have empathy towards other people and so on by participating in this discourse server you will build another great skill which is the teaching skills. I

many times see people that they themselves are learning concepts but somebody else when they ask a question they are ready to explain that concept.

Now this explanation skills are going to be super important. I have looked at some of the job post and they specifically ask for explanation skills because when you start working in a company if you are dealing with let's

say non- tech uh business stakeholder now you want to explain certain concept to them. If you're good at explaining

to them. If you're good at explaining things you will be able to convince them and that that skill matters a lot and this is something you have seen in the video clip of Karan. Okay, at at when we

hire people, one of the skills that we look at is is the person good at explaining things? Is the person good at

explaining things? Is the person good at communication overall? When you post

communication overall? When you post question in discord, there is some discipline, you know, some mannerism that you need to follow. For example,

you are facing an error in Python code.

Don't just copy paste the error in discord and say that okay, can you help me answer that that question? You have

to be polite and you have to uh showcase yourself as somebody who is looking for troubleshooting not the spoon fitting.

Okay. So you can say that I'm stuck here despite XYZ and can you provide me tips for troubleshooting? Instead of saying

for troubleshooting? Instead of saying that oh I get this error can someone help. Okay. Now you can also use chat

help. Okay. Now you can also use chat GPT if you're facing error go to chat GPT and say explain me this error step by step and try to understand what it is

saying. Okay, in terms of assignment,

saying. Okay, in terms of assignment, you will write 10 meaningful comments in AI related post and then note down your key learnings from the case studies at

think school or some other uh YouTube channel. In terms of motivation, I have

channel. In terms of motivation, I have this video that you definitely need to watch. So this person is an uh ML

watch. So this person is an uh ML engineer at April right now. He has a mechanical engineering background and when he was in college he used to

participate in kaggel. So using kegel contribution he built this online credibility and he directly got ML job without any formal education in ML.

Okay. So this interview was recorded 4 years back. I met him uh during our AI

years back. I met him uh during our AI fest when I went to India last time and I learned that he's now working at Apple as an ML engineer. So just imagine how

important is your kegel contribution.

Okay. So you can go to kegel just in case if you don't know what kegel is. It

is a platform where you can practice different machine learning competitions.

Okay. So if you go to competitions there will be AI competitions. For example,

let's say house prize. Okay. Llm

classification finetuning and then you will come to this leaderboard etc. Okay.

So it's like you're playing a game.

Okay. And they also have data sets.

Okay. They have variety of useful resources. So please explore that. So

resources. So please explore that. So

that is the end of week three. In week

four you're going to work on version control. See when you collaborate on

control. See when you collaborate on code you have to use this platform GitHub. And underlying GitHub is this

GitHub. And underlying GitHub is this version control system called Git. you

need to know basics of uh version control system, basic commands, uh pull requests, etc. And I have provided some playlist here so you can refer to those.

In terms of core skills, I think presentation skill is probably one of the most important core skill and I have this video death by PowerPoint and it's

like a bible of presentation. So if you see the video and if you just follow the guidelines in that video, you'll be amazed at the result. So say you are in

college and if you are presenting okay if you have worked on project and if you are presenting use the principles which are stated in this particular video okay

so I think he gave a TED talk uh I think this is a TED talk yes excellent folks I mean he has said some simple techniques but I see majority of the people they

don't follow it when they are creating presentation in terms of assignment you will write two meaningful blog post on AI topic. Okay, for example, how CNN's

AI topic. Okay, for example, how CNN's work. Now, there are many different

work. Now, there are many different platforms where you can write blog post.

One of the guy I know that he got a huge success by writing blog post is Himmanu Dubet. Okay. So, he was a student and he

Dubet. Okay. So, he was a student and he had this habit of uh being active on Twitter. So, let me show you his Twitter

Twitter. So, let me show you his Twitter profile as well. Once again folks, many of the tech people, good tech people in AI industry, you will find on Twitter.

Twitter is like the de facto platform.

So it will help you if you are interacting with those people via Twitter. So Himmanshuh is writing this

Twitter. So Himmanshuh is writing this blog post. Okay. So he has his own

blog post. Okay. So he has his own website and if you look at this post, he has post on a IML etc. Right? Like

initial thoughts on llama. So whatever

article you are reading, you can even explain okay how CNN works. I think he has explained some of those things right CNN from scratch with pure mathematical

intuition. So now when you write this

intuition. So now when you write this blog post and if somebody notices your work they will approach you. In case of Himanchu he was approached by some

startup in the US and I think recently he visited San Francisco also. So he's

not from like IIT or some big college.

Okay. He's from I think he's probably from a small town and he uh just focused on building his

online credibility and how did he do it?

He started writing blogs. Blog writing

is easy. You can use even AI tools for your English correction. Definitely add

your thoughts, okay? Don't just copy paste. And then Twitter. Twitter is

paste. And then Twitter. Twitter is

extremely important. Okay. So that will be your assignment writing two meaningful post and see if you look at any job post folks see look at this job

post effective communication with key stakeholders stakeholder management communication is the fundamental skill that every single AI engineer job will

be looking for. Therefore you can't uh you know underestimate it and I have given profile of some other person as well. So I'm seeing a lot of these

well. So I'm seeing a lot of these people who are let's say from small town in India they are from tier three college but they have figured out this art of online credibility okay and they

participate and for example this this person he's a research collaborator at coad which is a very good company right and look at his his education background

you can check his background and all these people come from very humble background okay tier three college small town and they get job immediately after their engineering. They don't have to

their engineering. They don't have to even apply because somebody has noticed them. In week five, you will cover

them. In week five, you will cover numpy, pandas and data visualization.

Numpai and pandas are the Python packages that you use to perform exploratory data analysis to perform data cleaning. These steps are required

data cleaning. These steps are required before you start training your model.

You will also learn SQL basics. Now see

you don't need to go in depth for SQL as long as you know basics like the basics queries you know select where disting etc the basic joins you know the primary

key foreign key some simple basic concept should be good enough because as an AI engineer sometimes you will be interacting with SQL databases I I I think most of the time your data will be

living in some SQL database so when you want to pull it to train your model or to build your rack solution whatever you will be writing those SQL queries and

that is the reason SQL is added here. So

for SQL I have this YouTube tutorial very simple 1 hour video is good enough and for numpy and pandas in my course I

have made those chapters free. So see

numpy chapter all the videos are free and then pandas mattplot lib all the videos are free. So you just watch it and that should be good enough. Pandas

is a vast topic. You don't need to learn every single thing in pandas. Okay, just

learn these basics and start doing your work and in the future if you need to learn extra concepts you can learn at that time. Week 6 7 8 you will focus on

that time. Week 6 7 8 you will focus on math and statistics for AI. If you look at any AI engineer job you will not find math as a skill but if you go for any interview they will definitely ask you

math question. Okay. So it is kind of

math question. Okay. So it is kind of assumed that you have some fundamentals on math and statistics. Now if you are targeting integrator role then it's okay

not to go too much in depth in math okay some basic math is good enough but if you're targeting a builder role or let's say allrounder role then all these

concepts are something that you need to know now you'll be like okay but nowadays we use LLM why do we need to use this well I talked about that at real project experience right where for

part of the problem we had to use statistical ical ML and when you use statistical ML or deep ML in order to evaluate the model in order to pick a right model you need all these

fundamentals. So folks trust me I work

fundamentals. So folks trust me I work on industrial AI projects all these fundamentals are going to be useful either today or tomorrow and when you go

to interview they will definitely ask you these questions and by the way some people have this uh thought that okay they're not good in

math but if you follow right resources for example Khan Academy three blue one brown okay I hope you have seen this channel three blue one brown state

Quest. These are amazing channels and

Quest. These are amazing channels and even if you got let's say 10 out of 100 during your school days in math, if you watch these amazing teachers in math,

you will be like okay I can do math. You

will be filled with that confidence.

Okay. All you need is a good teacher. So

please try it out. Math is seriously not that hard. Okay. And then there are

that hard. Okay. And then there are exercises that you can follow. Week 9,

10, 11 will be statistical ML. Now

somebody asked me this question that hey the we live in this LLM era so LLMs can do classification they can do regression

so how relevant is statistical ML I gave that answer by giving the analogy of a Bangalore traffic let's say you are

going in a Bangalore in a crazy traffic will you take a bike or will you take a big car most of the time people will take bike why because it is lightweight

weight. It will help you navigate faster

weight. It will help you navigate faster in the traffic. It will save you fuel cost. Similarly, statistical ML models

cost. Similarly, statistical ML models are lightweight. They are very cheap. In

are lightweight. They are very cheap. In

some domains such as healthcare and finance, in all these industries, there is a high requirement for interpretability.

And with statistical ML, let's say if you're using linear regression or some simple model, you will get high interpretability. I have a friend who

interpretability. I have a friend who works as a data scientist in some fintech company and when she builds a model they prefer statistical model over

other complex models you know like genai or deep learning because in statistical model which are simple you get good explanability and due to regulatory

requirements you need to have that so statistical ML is 100% valid nowadays folks we have clients in ATL we use

statistical ML Now in terms of learning there are two big modules pre-processing and model building. Pre-processing means making

building. Pre-processing means making your data ready for model training. And

this includes handling NA values outlier treatment data normalization encoding your data feature engineering your train test split and so on. When it

comes to model building you'll be mostly working on regression or classification.

Okay. And for linear models you can learn linear regression uh gradient descent logistic regression. For

nonlinear models you can learn decisionry, random forest and xg boost.

Uh in our practical experience we mostly use xg boost and random forest. They are

very efficient. They give you high accuracy. In terms of model evaluation,

accuracy. In terms of model evaluation, you need to know all these parameters.

Okay. How to evaluate the model. Now

model evolution is very important folks because that will help you figure out if model is good to go in production.

Hypertuning topics these are the listed topics for hypertuning and when it comes to unsupervised learning K means DB scan etc is enough. Now we have this playlist

in YouTube it has got see millions of views. Uh so you can just follow this

views. Uh so you can just follow this playlist. I have highlighted how many

playlist. I have highlighted how many videos you need to watch. And then in terms of core and soft skills. See when

you get hired in any company, you'll be working in a team. There will be project manager. They'll be doing project

manager. They'll be doing project management through uh these techniques.

Okay. So there's there is this scrum training series. So when I was at

training series. So when I was at Bloomberg, we had an agile coach who came and he forwarded this particular material. These are bunch of free

material. These are bunch of free videos, excellent videos which will tell you what is scrum. So scrum is a common technique that they use for project management. And in terms of tools, they

management. And in terms of tools, they use Jira and notion. Okay. And these are the exercises folks. So I have given exercises in my ML playlist. You also

need to work on two kegel ML notebooks and then write two LinkedIn post.

Whatever you have learned in ML, just write a LinkedIn post or whatever projects you have built. Let's say you worked on some kegel notebook. You can

share your learning via LinkedIn post.

Okay. In discord at least help 10 people with their questions. See helping people will consolidate your own knowledge. It

will give you an opportunity to practice your explanation skills. It will also help you build relationships. Okay. So

there are numerous benefits that you can get out of it. Week 12 and 13 you will learn DevOps, MLOps and fast API. See if

you get an AI engineer job which is falling under integrator category. Then

you will take a trained model and you will deploy it. For deployment you need fast API. Okay. So fast API is a popular

fast API. Okay. So fast API is a popular Python framework where you can take a model and you can write a backend server. See model is like our human

server. See model is like our human brain. But just by having brain you

brain. But just by having brain you can't get things done. You need a body.

So right now I'm talking I have brain but I have eyes, I have mouth, I have hands, I have body. So fast API will help you build that body around train

model. And then to deploy the model you

model. And then to deploy the model you need to know CI/CD pipeline, docker, kubernetes, all of that. MLOps is

required for experiment tracking, monitoring your model performance in production etc. And then you need to have familiarity with at least one cloud

platform. Now there are three popular

platform. Now there are three popular platforms AWS, Azure and GCP. Out of

these top two are AWS and Azure. And it

is clearly reflected in this analysis job analysis. See Azure is at the top

job analysis. See Azure is at the top and then there is AWS. I have provided a link of free YouTube tutorials for each of these topics. So please refer to

that. Now in this time period you should

that. Now in this time period you should start contributing to opensource.

You'll have this question that okay I'm still learning. I'm not an expert. How

still learning. I'm not an expert. How

can I contribute to opensource? That is

totally a wrong concept folks. You can

contribute to opensource. You can go to some repository, fix the documentation or fix an easy issue. So let's say if you go to GitHub. So let's say GitHub

hugging face repository. Okay. So

hugging face is a open-source package.

You can go to their issues. So they will have let's say thousand issues pending which means there are thousand pending bugs that they want to fix. And if you

go to label and search for uh good first issue. So these are the beginner

issue. So these are the beginner friendly issues I have labeled here. So

good first issues you'll find tons of issues. If you want to target simple

issues. If you want to target simple repositories then see mine SQL is one repository that you can target. There is

another repository called rag rank. So

just find all these repositories and start contributing. Okay. So you go to

start contributing. Okay. So you go to issues look at this issue and then folks use your friend. Who is your friend?

Well chat GPT. So you can say that go to chat gpt and say I want to contribute to opensource guide

me on first steps. See it will guide you step by step folks and if you are looking at any issue you can say okay I'm looking at this issue can you help me fix it? It will fix it. It will also tell you what to do what what steps you

need to follow. Chat GPT is likeading you know it is there in your service all the time. You just need to know how to

the time. You just need to know how to use it. All right. Now, the reason I am

use it. All right. Now, the reason I am putting so much focus on opensource contribution is look at this particular video. Okay. This person got two ML

video. Okay. This person got two ML engineer jobs immediately after 12th. He

did not even go to college. How was that possible? Well, the repository which I

possible? Well, the repository which I showed you, rag rank is written by this guy. Okay? He's from small town in

guy. Okay? He's from small town in Kerala. It's not like he's from IIT and

Kerala. It's not like he's from IIT and from some big town and big connection.

No very simple background but he understood that open-source contribution is a way to go. So watch that video you will get lot of good tips. All right in

week 14 you will build machine learning projects and I will say okay build just one project which is uh this particular

project where you are going over all the steps in uh ML model development. So you

are doing data cleaning, feature engineering, outright removal, see model building, you're also writing Python flask server. So flask is another

flask server. So flask is another framework similar to fast API. I would

advise you to use fast API uh instead of flask because I think it's better and then you are building website, you are even deploying it to AWS. Okay. So all

these steps you will cover and you will build an end toend project. During this

same time you should start building your resume. Okay. And I have provided some

resume. Okay. And I have provided some good videos. I have also provided you

good videos. I have also provided you this checklist. So if you click on this

this checklist. So if you click on this checklist. Okay. See this is a

checklist. Okay. See this is a checklist. So if you follow all this

checklist. So if you follow all this checklist you will end up in an excellent ATS you know application tracking system ATS compliant resumeum.

You also need to start building your project portfolio website because see you build this project then you create a website. So let me just show you this

website. So let me just show you this particular website. So this is the

particular website. So this is the website that all our boot camp students get. You can create portfolio by going

get. You can create portfolio by going to GitHub. There are some free tools

to GitHub. There are some free tools available as well. So here this person has given idea on what projects he has worked on and see you can see his projects here along with the screenshot.

You can go to his GitHub, you can check his code, you can um I think look at his LinkedIn profile where he posted about his project. Okay, so this is a project

his project. Okay, so this is a project I did uh and so on. So this project portfolio website is sort of like your live resume. A recruiter will be really

live resume. A recruiter will be really impressed if you have a professional looking profile. In terms of

looking profile. In terms of assignments, as I told you already in that project instead of flask use fast API. You can also build one more

API. You can also build one more classification project. So this is a

classification project. So this is a regression project but I will advise you to build a classification project. You

can get data sets from Kel from variety of websites. Okay. And then in your

of websites. Okay. And then in your project portfolio website now you have one classification project, one regression project which is going to be super amazing. All right. Next step is

super amazing. All right. Next step is deep learning. So you'll spend four

deep learning. So you'll spend four weeks in deep learning. See deep

learning is the reason behind modernday LLM boom. So deep learning is essential.

LLM boom. So deep learning is essential.

If you go to interview, they will ask you questions on neural networks, different architectures, forward propagation, back propagation and so on.

Now there are two frameworks.

TensorFlow, PyTorch. PyTorch is number one folks. You should definitely learn

one folks. You should definitely learn PyTorch. TensorFlow is something it's

PyTorch. TensorFlow is something it's little bit low level and in some jobs they will ask for TensorFlow. Now I have this particular playlist where I have

taught things using TensorFlow. I'm

going to build a new playlist using PyTorch. But this playlist is not just

PyTorch. But this playlist is not just TensorFlow. It is all the concepts. So

TensorFlow. It is all the concepts. So

there are many videos here which will go over fundamentals right like what is neuron, how neural network works, then u activation function, derivatives. So I

have gone into maths in detail. So look

at this playlist for fundamentals. If

you're interested in learning PyTorch then you can refer to this campus playlist. I think he uses English and

playlist. I think he uses English and Hindi. So if you know these languages

Hindi. So if you know these languages this is an excellent playlist that you can follow. And then for end to end

can follow. And then for end to end project I have this amazing project uh where you are building a disease classification for potato plant. Okay.

So if you look at this project what we did is we created a mobile app. So look

at this mobile app. Okay. In this mobile app you go and you take a picture of a plant and then it will send that picture

to your fast API back end and back end will have a trained model. It will do the prediction. It will send the result

the prediction. It will send the result back to your mobile app which is returned in react native. So this is see early blight 100%. So this is an amazing end to-end project that you can add to

your rumé and of course you don't want to just copy paste this project right you can use a different data set instead of potato plant use some other plant images and deploy to Azure instead of

GCP okay so you can make customization and make it your own unique project after you are done with the project you will create a presentation and you will

present it to stakeholders and put it on LinkedIn Okay, week 19 2021 you will spend in either NLP or computer vision.

What I have observed is there are some AI engineer who will focus on text which is NLP. There are other engineers who

is NLP. There are other engineers who will focus on images which is computer vision. Okay. So you can choose one

vision. Okay. So you can choose one path. Uh I would say you don't need to

path. Uh I would say you don't need to go both both the ways. Okay. Start with

one. Uh in terms of NLP you will learn regular expression then text representation. These are the topics and

representation. These are the topics and once again I have a playlist that you can refer to. This playlist has got very

good uh response okay and I have covered all the topics in very much in detail.

Now for the project see in this playlist you we have a project using dialog flow but dialogflow is kind of getting older and there are new technologies coming

up. So I would say skip this particular

up. So I would say skip this particular project instead of this do this project.

Okay. Now this is the same project which I explained at the beginning of the video where for you you remember this diagram I hope you remember it. So we actually implemented

remember it. So we actually implemented this particular project where we used a hybrid approach of uh regular expression

statistical ML and genai. Okay. So do

this project and then for computer vision uh in ATL once we had a project where some grocery shops in US they wanted us to build a solution where you

take a picture of the items which are kept at the shelf and it should count those items. For example, Cheerios.

Okay, this item is Cheerios. So it will do object detection and then it will do the count as well. Okay, so this is the use case. Uh these are the things you

use case. Uh these are the things you will learn. OpenCV is the library that

will learn. OpenCV is the library that you will learn. Then you will use some of the concepts in deep learning. For

example, convolutional neural network.

It is heavily used for image processing.

So you can use that. You can also learn YOLO and all these other techniques.

Okay. And at the end comes the most interesting part or the part that everybody is waiting for which is JAI and agentic AI. There is no doubt we are

living through a boom and jai and aentici is a very very hot skill nowadays. In terms of topics you need to

nowadays. In terms of topics you need to know what is llm vector databases embeddings. What is retrieval augmented

embeddings. What is retrieval augmented generation? Lang chain is a de facto

generation? Lang chain is a de facto framework that people use. People use

langraph for agentic uh AI. Crew AI is another agentic AI framework. I have

provided all the resources. See for

langraph for example I have this complete crash course okay complete crash course on lang graph crash course on QI folks this information is there on

YouTube for free if you have motivation if you have discipline you can learn it for free without spending any money then MCP then we have complete ji crash

course this is a I think this is 3hour long uh YouTube crash course which is available uh for you for free and then week 25 to

27 you will spend in building the projects. See you learned all the skills

projects. See you learned all the skills but until you build end to end projects your skill building process will not be

complete. So you need to target some

complete. So you need to target some projects where you are using rag you're using some agentic AI etc to solve real life problems. So I have this playlist

and I'm adding new projects actively.

Okay. So go through this. This is a project you already did, right? But this

this these two and then aentic using langraph. Okay. Okay. So there are I

langraph. Okay. Okay. So there are I think this one two and three this project you already did. So these

projects are good enough and make sure you add these projects to your project portfolio. All right. Now in week 28 and

portfolio. All right. Now in week 28 and 9 you need to work on unguided projects.

So for that uh a very good resource is uh this data challenge. So in at code basics we conduct these data challenges.

These are free there is no money. Here

we post a problem statement along with data set you know sort of like kegle and you build a solution. After you build a solution, you will make a presentation

and you will present it uh via LinkedIn post. So this is how kegel and these

post. So this is how kegel and these challenges differ. Kaggel is just

challenges differ. Kaggel is just building technical skills but here you are building both technical skills as well as soft skills. So Arin Sharma is a person who won one of our data

challenges and he got a job based on that. Okay. So let me show you. So this

that. Okay. So let me show you. So this

is the challenge that he won and when he presented see if you look at his presentation he's presenting as if as if he's talking to business stakeholders.

Okay. And this was noticed by one of the recruiter and he got the job opportunity. It's like he did not apply

opportunity. It's like he did not apply in that company but the recruiter reached out to him. So this

participating in these challenges and winning the challenges can be very beneficial. So the first challenge is

beneficial. So the first challenge is building a rag based assistant which will be controlled based on the roles.

Okay. So there is role based access control RBAC in this project. So it's not a simple rag project. It is bit complicated. It

rag project. It is bit complicated. It

is very much similar to how you will build the project in the industry. The

other challenge for which we announced the winners just today by the way is this uh challenge to use NLP to detect

adverse drug effects. Okay. So this

challenge is actually very complex and if you can do it it will be amazing. So

we conduct these challenges we announce the winners but even after the challenge is closed you can still practice it. The

problem statement is there, data set is there. So practice it and then add this

there. So practice it and then add this project to your project portfolio website. Okay. Now week 30 to 32 you

website. Okay. Now week 30 to 32 you will learn at least one cloud platform.

I will say go for either Azure or AWS.

Now as a fresher you are not required to learn this. So if you go for any fresher

learn this. So if you go for any fresher interview let's say person comes to a fresher interview in our company at lake. We don't ask any cloud question.

lake. We don't ask any cloud question.

But if a person knows cloud then it will give them unfair advantage. So based on the time and willingness that you have you can learn. If you are an experienced professional you need to know it. I'm

seeing this trend nowadays where many software engineers want to learn AI and want to move into this. Okay for them this integrator role is a perfect. So

you already know back end, you already know one of these cloud platforms. You need to know certain ML concepts and now you can become AI engineer which falls into that integrator category. I have

mentioned the topics for both Azure and AWS. And folks see once you learn one

AWS. And folks see once you learn one topic for example identity and access management AM right if you learn it in AWS learning it in Azure is going to be super easy. So you will learn this

super easy. So you will learn this transferable skills. So just learn one

transferable skills. So just learn one cloud platform and when you work on other cloud platform you know the fundamentals. So you'll be able to learn

fundamentals. So you'll be able to learn the Second Cloud platform very easily.

Now I don't have any specific recommendation for this resource. You'll

find tons of YouTube videos, tons of free courses. So go figure it out at

free courses. So go figure it out at some point on our channel. We plan to publish videos on these cloud platforms as well. Now the optional skill is no

as well. Now the optional skill is no code agentic tool. NAN is very popular.

Make Zapier these are no code agentic tools. Okay, this is an optional skills.

tools. Okay, this is an optional skills.

There are most of the AI engineers jobs will not ask for it. But let's say if you're working for a small company where the clients projects are demanding both of these you know no code tools as well

as uh coding frameworks like langraph then in their job posting you will find it. So after you have learned all the uh

it. So after you have learned all the uh mandatory skills if you have time you can learn at least one of these two tools and then week 33 and onwards more

projects online building through LinkedIn kegel discord opensource contribution and then eventually someone will notice you folks and you will get

the job success. Now I'm going to build another video for uh how to apply for jobs and how to get success because there is a science to it. I will talk

about that funnel where you apply then you get shortlisted, you appear in the interview, you know I I'll walk you through the entire process. So that is going to be the follow-up video. Uh so

please stay tuned uh watch it out. Tips

for effective learning is going to be don't watch 10 tutorials on the same topic. Watch one tutorial, spend time in

topic. Watch one tutorial, spend time in digesting, implementing and sharing. You

know, spend 15 minutes watching the tutorial, 45 minutes in digesting, implementing and sharing. If you're

jumping from one video to another, you will drain out your energy. It's not

going to result into effective learning.

Okay? So, it's a simple principles requires lot of mental discipline. You

almost need to put your phone away when you're learning and you need to have this discipline that I will spend more time in these three and less time in consuming. Group learning is also very

consuming. Group learning is also very effective. If you're learning let's say

effective. If you're learning let's say uh swimming and if you go alone versus if you go with two of your friends. I

think doing it in group is going to motivate you more. It is going to give you more inspiration. Similarly in code basics discord server you will see this

partner and group finder channel where people will be like hey can anyone join me in learning AI agents and you can respond and you can kind of make a study

group watching the video is not going to be enough you need to act on it folks I have provided all the resources in the video description please take them start

your action from today I wish you all the best and if you have Any question there is a comment box below.