AI Engineer Roadmap | How I'd Learn AI in 2024

Summary

The video provides an 8-month roadmap to become an AI engineer, requiring 4 hours of dedicated daily study. It covers core computer science fundamentals, Python programming, data structures/algorithms, SQL databases, math/statistics, machine learning, deep learning, NLP/computer vision, and LLM/Langchain. Soft skills like building an online presence and ATS resume are also discussed. Tips are provided for effective learning through more implementation/sharing and less passive video watching.

Takeaways

• 😊 The video provides a detailed 8-month roadmap to become an AI engineer, requiring 4 hours of dedicated study per day
• 📈 AI engineer is currently the highest paying technical role, with very high demand
• 🧠 Strong coding and math skills are an absolute must to become an AI engineer
• 🐍 Learn Python basics, data structures, algorithms and advanced concepts like inheritance, generators etc
• 💻 Develop software engineering fundamentals like OS, networks, HTTP, databases etc if you don't have a CS degree
• 📊 Spend 1 month focused just on math and statistics - linear algebra, calculus, probability etc
• 🤖 Spend 1 month on machine learning models like regression, classification etc and MLOps concepts
• 🧠 Consider specializing in NLP or Computer Vision by spending weeks 28-30 focused on one of these
• 👩‍💻 Build projects, online presence on LinkedIn, portfolio and work on soft skills in parallel
• 🚀 Lifelong learning is key for AI. This roadmap sets a strong foundation to start an AI career

Q & A

What are the two key skills needed to become an AI engineer?

The two key skills needed are strong coding skills and strong math skills. The video mentions that without skills in these two areas, you cannot become an AI engineer.

What is the recommended daily study time for the 8-month AI engineer roadmap?

The recommended daily study time is 4 hours per day for the 8-month roadmap.

What Python concepts should be learned in weeks 3-4 of the roadmap?

In weeks 3-4, you should focus on learning Python basics like variables, data types, conditional statements, loops, functions, classes etc. The video recommends going through the first 16 Python beginner tutorials.

What data structures and algorithms should be learned in weeks 5-6?

In weeks 5-6, you should learn arrays, linked lists, stacks, queues, trees, graphs, sorting algorithms, search algorithms etc. The fundamentals of time and space complexity should also be understood.

What key soft skills are recommended to develop in parallel with technical skills?

The key soft skills recommended are: building a strong LinkedIn profile, following AI influencers, commenting meaningfully on posts, developing presentation skills, learning business concepts, and mastering the art of asking questions.

What are some good resources for learning SQL in weeks 10-11?

Some good free resources for learning SQL are: Khan Academy SQL course, W3Schools SQL tutorial, SQLBolt interactive platform, and MySQL tutorial playlist on YouTube.

What machine learning topics should be covered in weeks 18-21?

The machine learning topics to cover are: data preprocessing, linear/logistic regression, decision trees, SVM, KNN, clustering algorithms, ensemble methods like random forests and gradient boosting machines.

What frameworks are used for building ML model APIs?

Popular frameworks used for building ML model APIs are Flask and FastAPI. These allow you to wrap a trained model in a web service/API that can serve predictions.

What tips are provided for effective learning of AI skills?

Tips include: spend more time implementing than just watching videos, learn concepts thoroughly before moving ahead, share your learning with friends, work on real projects, build an online presence.

What should be the next steps after completing the 8-month roadmap?

After completing the roadmap, next steps are: work on more projects, build online credibility, apply for jobs/internships, continue lifelong learning as AI is a fast-evolving field.

Outlines

00:00

🎥 Brief intro about AI engineers roles and salaries

The paragraph introduces AI engineers, stating they earn the highest salaries among technical roles. It mentions the skills needed - coding and math. It compares AI engineers to a combination of data scientists and software engineers.

05:02

📝 Roadmap PDF overview and starting guidance

The paragraph overviews the 8-month roadmap to become an AI engineer. It emphasizes avoiding scams, doing proper research, and not taking shortcuts. It mentions LinkedIn posts to avoid scams.

10:03

🖥️ Learn computer science fundamentals

The paragraph advises spending 2 weeks learning computer science fundamentals using a Khan Academy course if you lack a Computer Science background. It explains why software engineering fundamentals are critical for AI engineers.

15:04

🐍 Python programming language basics

The paragraph focuses on learning Python over weeks 3-4. It recommends specific YouTube playlists for tutorials and exercises. It advises completing the first 16 beginner tutorials and all exercises.

20:05

💼 Develop soft skills and LinkedIn profile

The paragraph parallels technical skill development with improving soft skills like building a strong LinkedIn profile. It shares a checklist to optimize LinkedIn profiles.

25:06

📊 Learn data structures and algorithms

The paragraph stresses the importance of data structures and algorithms over weeks 5-6. It links to a YouTube playlist for concepts and exercises to spend 2 weeks on.

30:06

😊 Motivational videos and advanced Python

The paragraph inserts motivational videos in weeks 7-8 when learning advanced Python like inheritance and multiprocessing. It links to the advanced section of the Python playlist.

🏢 Business concepts and asking questions

The paragraph covers improving business acumen using a YouTube channel in weeks 7-8. It recommends asking coding questions on Discord and shares tips on this art.

🌐 GitHub for version control and collaboration

The paragraph stresses learning GitHub and version control systems in weeks 7-8. It shares YouTube playlists explaining concepts clearly for beginners.

🎤 Presentation skills and communication

The paragraph focuses on presentation skills by recommending a specific YouTube video. It explains why presentations are critical for AI engineers working with stakeholders.

💾 SQL and relational databases

The paragraph covers SQL over weeks 10-11, listing specific topics to learn. It shares free resources like Khan Academy and paid courses for more extensive applied learning.

📊 Exploratory data analysis (EDA)

The paragraph focuses on EDA using pandas and matplotlib to clean and transform data. It recommends practicing EDA over weeks 12-13 using datasets from Kaggle.

🤯 Essential math and statistics

The paragraph stresses the importance of math and statistics fundamentals over 4 weeks. It lists the key topics and links to free learning resources on YouTube and paid courses.

🔎 More exploratory data analysis

The paragraph parallels technical EDA skills with communication skills via LinkedIn posts. It sets goals to analyze more datasets and share via 2 LinkedIn posts.

🧠 Core machine learning module

The paragraph focuses on machine learning over 4 weeks using a popular YouTube playlist. It also links short videos on agile methodologies used in ML projects.

🤖 MLOps and model deployment

The paragraph quickly introduces key MLOps concepts like APIs, Docker, and cloud. It states ML engineering roles often overlap with MLOPs so foundation is key.

🎯 End-to-end ML projects

The paragraph focuses on building regression and classification ML projects over weeks 23-24. It suggests customizing public GitHub code for uniqueness.

⚡️ Trendy deep learning module

The paragraph covers deep learning over 3 weeks using YouTube playlists. It explains how deep learning drives innovations like ChatGPT.

📷 Computer vision or NLP specialization

The paragraph suggests specializing in either computer vision or NLP instead of both. It shares topic details and playlist links for both domains.

🤗 LLMs and LangChain framework

The paragraph focuses on the popular LLMs and LangChain over the last 2 weeks. It states most ML roles now desire LangChain exposure.

😃 Tips for effective learning

The closing paragraph shares tips for effective learning like group studies and spending more time practicing versus consuming videos.

Keywords

💡AI engineer

An AI engineer is a software engineer who specializes in developing artificial intelligence systems and machine learning models. The video focuses on the high demand and career potential for AI engineers. It provides a detailed roadmap for how to acquire the necessary skills over 8 months to become an AI engineer.

💡machine learning

Machine learning is a core field within AI where models are trained to make predictions or decisions without explicit programming. Understanding machine learning techniques is critical for AI engineers to build effective AI systems.

💡programming fundamentals

Strong programming skills in languages like Python are foundational for implementing AI systems. The roadmap includes learning computer science basics, Python, data structures & algorithms.

💡data exploration

Before building models, AI engineers need to understand the data through exploratory data analysis using libraries like NumPy and Pandas. This includes cleaning, transforming and visualizing data.

💡MLOps

Similar to DevOps in software engineering, MLOps focuses on automating and streamlining machine learning workflows for production systems. This allows continuous model improvement.

💡deep learning

Deep learning uses neural networks to learn complex patterns from large datasets. Understanding deep learning frameworks like TensorFlow enables building advanced AI models like computer vision and NLP.

💡soft skills

In addition to technical expertise, the video emphasizes developing soft skills like presentations, asking questions, using version control systems, and showcasing work through portfolios.

💡career development

The roadmap focuses not just on acquiring AI skills but also actively building online credibility via platforms like LinkedIn, Kaggle and GitHub to get a job and progress in your career.

💡continuous learning

The fast pace of advancement in AI means learning is lifelong. The roadmap allocates time specifically for more advanced skills like LLM and langchain after covering fundamentals.

💡motivation

Persistence and willpower over 8 months of daily study is key. Tips like group learning, progress tracking and inspirational stories aim to drive motivation.

Highlights

AI engineers make highest salary among all technical roles

Preparing to be an AI engineer requires a lot of hard work over 8 months studying 4 hours every day

You need strong coding and math skills to become an AI engineer

An AI engineer is like a data scientist plus software engineer combined

LinkedIn helps you build relationships and get job referrals from people in the AI industry

Asking good questions and getting quality answers accelerates the learning process

Working on public resume project challenges showcases skills and personality

Math and statistics form the foundation for any AI project

Exploratory data analysis involves data cleaning, transformation, and visualization

Machine learning skills take a whole month to gain competency

MLOps focuses on automating parts of the machine learning development process

An ATS resume gets through automated screening systems used by companies

A project portfolio website showcases work samples to demonstrate skills

Deep learning is behind innovations like chatbots and language models

The learning process for an AI engineer never stops as the field rapidly evolves

Transcripts

we are going through a gold rush

companies have started investing

billions of dollars in AI projects and

there is one career role that is going

to benefit the most out of this boom AI

engineer or ml engineer in my company at

lck Technologies I hire AI Engineers I

have previously worked with Bloomberg

and Nvidia based on my industry

experience I'm going to discuss a road

map with week by week study plan using

free learning resources and checklist

that you can use to become an AI

engineer AI engineers make highest

amount of salary among all the technical

roles what I'm showing you on the screen

is just a range the exit salary depends

on your skills and experience the

company that is hiring and the location

now obviously if company's paying you

such a high salary they will have a high

expectation therefore preparing for AI

engineer requires a lot of hard work if

you're looking for any shortcut then

please leave this video right now

because this road map will require 4

hours of dedicated study for 8 months

and that will help you set a strong base

the actual learning is a lifelong

process before you begin the study you

need to figure out if this is the right

career for you and the way you can find

it out is you need to evaluate if you

have interest in coding and math AI job

requires strong coding and math skills

and without that you cannot become a

engineer so if you don't have interest

or skills in any of these two then don't

go for a engineer this is not the end of

the road because there are other career

roles as well such as AI sales

representative AI product manager AI

ethics executive Etc we will not only

talk about tool skills we will also

discuss core skills and all learning

resources associated with it once we

have discussed upskilling we'll also

discuss how you can show showcase your

work to the world so that you can get an

interview call and crack an interview in

case you are confused about data

scientist and AI engineer role think of

it as data scientist plus software

engineer is equal to AI engineer that's

a very simple way of looking Ed it here

is a road map PDF you can download it

from video description below requires 8

months of study 4 hours every day and

the week zero starts with proper

research folks there are so many scams

going on in the market so if you buy a

wrong course or if you learn from an

instructor who doesn't have industry

experience let's say or who is not

legitimate then you will get into

trouble nowadays you see many YouTubers

many people teaching on online learning

platforms they claim that their courses

are the best but when you look at their

background they don't have experience

they just know how to talk nicely and

they conduct all kind of scams we have

created couple of LinkedIn post which

you can look at it and make sure you

don't get into those scams we are in

fact running a scam awareness program

once you have done enough research week

one and two will go into learning

computer science fundamentals if you

have computer science degree you are

covered but let's say you don't have

Computer Science Background then I will

suggest you go through this Khan

academic course which covers the basics

such as bits and bites storing text and

numbers basic B of computer networks

HTTP worldwide web basics of programming

and so on once again look at this

particular equation you need to have

solid software engineering fundamentals

in order to become AI engineer here is

the course which is free and you need to

just finish the first four modules

remaining modules you can go over it if

you have interest in time but the four

modules are good enough enough Khan

Academy is a very good platform this

person teaches you very well along with

the practice exercises in week three and

four you will focus on python python is

the most popular programming language in

the AI world today learning python is

actually very easy you need to start

with these basic concepts okay and we

have a playlist on YouTube this is on my

channel and the other playlist is on

Cory shaffer's Channel you can refer to

whichever tutorials you feel comfortable

with in this stage I would suggest you

go through only first 16 tutorials

because that will cover the beginner's

logic in Python and as an assignment

you'll have to finish all the exercises

so if you click on this link you will

see all the exercises okay so let's say

there is an exercise and there is a

solution link as well I know you all are

sincer students so you will practice on

your own and then only you will look at

the exercise exercises now in this time

period week three and four along with

python you need to learn some soft

skills which are those soft skills well

you need to build a LinkedIn profile

LinkedIn is the platform that will help

you get a job eventually therefore you

should not wait until you done with all

your technical skills the approach I

suggest is in parallel you will start

building LinkedIn profile and all other

softes okay we have created a check list

that will help you make your LinkedIn

profile stronger all you have to do is

follow all this guideline check check

check and once you have checked all

these check boxes your LinkedIn profile

will look nice once you have covered the

python Basics week five and six you

should focus on data structures and

algorithms as a ml engineer or AI

engineer you will be writing programs

which needs to scale so you need to know

the trade-off between memory and CPU you

need to have deeper understanding on how

the data structures work underneath okay

and for that we have once again a

YouTube playlist it's a free uh Learning

Resource the playlist contains exercises

as well so you can go through all the

data structures and algorithm it will

take you 2 weeks time period to go

through all of these and also practice

those exercises now this is going to be

a long learning Journey it's very

important that you keep yourself

motivated for that I have included some

inspirational videos for example in this

video I interviewed tanul Singh who was

a mechanical engineer and he used Kagel

platform to become ml engineer he's

sharing a lot of useful tips and

insights so please go through this video

while you are learning your technical

skills in week seven and8 now you can

learn Advanced python such as what is

inheritance generators iterator

when you writing big programs for

Enterprises which scales well or which

is doing a huge volume of data

processing knowing all these concepts

are going to be extremely beneficial

when I was at Bloomberg we were using

generators and iterators a lot because

we used to deal with huge objects and

when you're running a full loop it's

hard to keep those objects in memory so

using generator you can return it on the

Fly list comprehensions are going to be

super important for optimizing your

program multi-threading and

multiprocessing is very useful when you

want to uh utilize your computer

resources such as the course or even

multiple processor within the computer

to achieve a high throughput for this

once again refer to the same playlist

but in this uh you should go through

video number 17

227 and all of these videos have

exercises so make sure you watch the

video and cover the exercises in all

these videos I will be talking about

Theory then we'll be writing some code

and then there will be an exercise in

terms of soft skills you should start

following some prominent AI influencers

on LinkedIn one of them is for example

natin he is a head of AI services at

Google and he writes posts which will

talk about the current trends he will

also talk about the hiring trends that

he's seeing because he himself hire a

lot of AI engineers in his team I find

reading his post to be extremely useful

the other person is dalana she writes

mainly on data science Ai and data

science are kind of overlapping so

therefore you can follow all the post on

data science as well so follow all these

influencers and spend let's say half an

hour every day that way you're keeping

yourself up to date and also you are

becoming active on LinkedIn you should

also start commenting meaningfully on

those post when you comment on anybody's

post what happens is your post if it is

having valuable content your post or

your comment then it will get an

engagement okay so let's say this post

got 155 likes some of these people who

are giving you likes could be hiring

managers or they could be AI Engineers

working in other company so this way you

are building a relationship with those

folks and tomorrow when you're looking

for a job maybe they will give you a

referal or maybe they will hire you in

their own team building relationship

ship on LinkedIn is super important and

you posting comments valuable comments

okay don't post comments like this okay

true Absolut right because that will not

generate any engagement you're not

adding any value but when you add some

value to the post you are omitting your

personality in this online world of

LinkedIn and that will help you build

relationship that will help you get

attention to your profile remember that

online presence is a new new form of

resume along with online presence you

need to also think about business

fundamentals as an AI engineer you will

be working in some industry Retail

Finance any industry if you have good

understanding of business Concepts it

will help you communicate better with

the stakeholders which are involved in

your project to learn the business

Concepts I will suggest you follow this

think school YouTube channel okay so

I'll will show you one video where he

talked about how amul beat the

competition and here he's talking about

numbers and strategies and dairy

industry in general so when you go

through these kind of business studies

you are building your business

understanding you're developing a

business acument okay Additionally you

need to learn the art of asking

questions Discord is a platform which

allows you to ask questions while you

are learning let's say python SQL

whatever and if you have question where

should you go one of the ways is asking

questions in Discord server okay now

there are many Discord servers for code

basics for our Channel we have this

Discord server which has around 33,000

members okay and if you have question

let's say uh for Math and statistics or

let's say for machine learning you can

post a question and the community

members will answer those questions now

asking questions is an art do not just

copy paste the error that you're facing

and ask for the help because then people

will not help you because you're looking

for spoon fitting the right approach is

to look for direction not the spoon

feeding I have highlighted that art in

this particular LinkedIn post I have

link linked it here you can go through

it and your assignment for this time

duration will be to write meaningful

comments on at least 10 AI related

LinkedIn post and KN down your key

learnings from three case studies on

things School and share them with your

friend as in when you finishing those

assignments you can just keep on marking

them that way you are tracking a

progress as an AI engineer you will not

be working alone on a project you will

be working with a team now how do you

collaborate with the team how do you

share your code with the team how do you

review that code well the way to do that

is via Version Control therefore you

need to have sound understanding of

Version Control Systems such as git G

Hub is a website which is using git as

an underlying Version Control System

there is another website called gitlab

too okay but GitHub is very popular

develop an understanding of how git and

GitHub works the topics you will learn

are listed here and in terms of learning

resources once again you can use YouTube

on YouTube you can refer to Cory safer's

playlist or I also have a playlist here

and in this playlist I have explain

things as if you are a high school

student in a very simple language using

a practical approach to keep the

motivation High I have linked an

interview of a mechanical engineer who

became deep learning engineer using

self- study mahad is the name of the

person and I love his confident and the

way he approached his entire journey is

really inspirational so I would highly

recommend you watch this interview when

it comes to soft skills presentation is

the most underrated skill I would say

for this I would suggest you watch uh

this Death by PowerPoint video this

video is a gold mine it is giving you

very simple and very powerful tips of

how do you build effective

presentations as an AI Engineers you

will be working with stakeholders you

will be in a meeting rooms you will be

presenting all the time and if you don't

know how to present well there is no use

of your technical work because you're

not able to sell your work or you're not

able to convey your ideas in a language

that the business stakeholders

understand watching this video and

preparing skills for presentation is

going to boost your career week 10 and

11 we need to focus on SQL and

relational databases as an AI engineer

we will need data to train our models

and to do variety of operations this

data is often stored in a relational

database and SQL is called structured

query language it's a language that you

use to query data from those databases

here you need to learn all these topics

and in terms of free learning resources

we have an excellent Khan Academy SQL

course so you can go through it learn

those skills you can also use W3 schools

or a platform like SQL bolt which allows

you to practice SQL while you're

learning it so I really love this

platform you should definitely try it

out and then on YouTube also there are

tons of video my channel have this

particular video which goes through SQL

skills uh there are so many other high

quality SQL tutorials available on

YouTube in case you want to speed up

your learning and you want to learn in a

very practical approach and also work on

an industry project then I have this SQL

course okay this SQL course is very

highly rated it's very affordable and uh

we are not only going through all the

SQL technical fundamentals but we are

teaching how these SQL projects are

executed in the industry so all the

stakeholder management skills project

management skills are also covered for

assignment uh you need to work on SQL

resume project Challenge on our platform

Cod basics. we run this free resume

project challenges where we share

problem statement and data with folks

and people work on these projects and

not only that they build presentation

and they present it on LinkedIn so let

me show you so here is the resume

project challenge where you see the data

said the mockups everything the problem

statement so many people participate in

this one and the winner for example here

is Aran Sharma so if you click on this

LinkedIn post what he did is he built a

solution in SQL and then he created a

linken post where he explained the

solution that he built not only that he

attached a video presentation where he

was talking as if he's presenting this

to business stakeholders now when you

are doing this kind of activity you are

uh showcasing your verbal your written

uh English communication skills to the

world let's say if a potential hiring

manager watches this video they will get

lot of clues about aran's personality

his technical as well as his softes the

fact here is that Aran literally got

hired in a company as a data analyst

just based on this particular resume

project challenge so this is really

effective it has worked for Aran and

many other folks and it can work for you

as well next comes numai and pandas and

I have attached the playlist and

learning resources for it numai and

pandas are used for data cleaning data

exploration those kind of things so you

are spending just one week in learning

this basic libraries and later on there

will be a time period where you will

actually practice the Eda skills

exploratory data analysis skills then

comes the heavy module math and

statistics for AI math and states is the

foundation for AI any AI project so if

you're working as an AI engineer you

need to have sound fundamentals in math

and statistics now math and states is a

vast field I have listed down all the

topics which are need needed by an AI

engineer okay so just focus on all these

topics I have also linked the learning

resources which includes Khan Academy

scores the YouTube channels you know

channels such as St quest uh there is a

free YouTube playlist uh and a channel

called three blue one brown this person

teaches mathematics in a very Visual and

very appealing way so just refer to his

videos if you are interested in learning

things like calculus linear algebra Etc

I have also linked my math and

statistics course here which covers all

the fundamentals it also covers an

industry project where we had a database

of half a million records and we did

hypothesis testing on the launch of a

new credit card okay so you can refer to

this course if you want to uh learn

using industry Style Project based

learning next one is exploratory data

analysis you might have heard this term

Eda Eda is nothing but you get all the

data that you need for your AI project

you need to First do some exploration

there might be lot of bad values you

need to clean those bad values you also

need to perform certain data

transformation okay so this module

covers that the technical skills that

you need for this are numai pandas

matplot lib Etc which you have learned

previously correct but in this

particular module what I want you to do

is go to kel.com Kel is a website which

is hosting uh data sets and competitions

related to Ai and here you will find a

lot of useful data sets and also the

problem statements so you have to go

through some of these problem statements

okay and practice you will see solutions

from other folks as well but I want you

to practice things on your own first and

then look at the solution from other

people so the exercise here Will be if

initially during learning you do Eda

using three data sets and then you work

on additional two data sets and perform

exploration now comes probably the most

important module machine learning here

you will be spending week 18 to 21

entire month machine learning is a vast

field and this particular segment covers

only this statistical machine learning

okay so you need to First cover

pre-processing techniques and then model

building techniques the great news here

is that we have a YouTube playlist this

is a playlist on my own channel it has

received more than 2 million views I

have explained the theory in a very

intuitive way then there is code and

then there is exercise so go through

this playlist first 21 videos only when

you get a job as an AI engineer you will

be using some kind of project management

tool in the industry right now scrum and

Canan are the two popular agile project

management techniques it will be good to

have some understanding of scrum and

Canan I have linked excellent free

resources for both of it it won't take

you much time so please go through them

and here is the assignment you need to

complete all the exercises in the ml

playlist work on two Kel ml notebooks

write two LinkedIn post on whatever you

have learned in ml on LinkedIn let's say

if you have learned about uh

classification you know let's say

logistic regression and if you have

worked on a small problem statement you

can write a nice summary of what you

have learned and that will generate some

engagement so being active on LinkedIn

is going to be a constant requirement in

week 22 we will be looking at mlops

mlops is similar to devops if you are

aware about software engineering in

software development there is this role

called Dave Ops where a person will look

into uh you know automating some parts

of a software development so they will

be working on cicd pipelines on Jenkins

on automating workflows integrating

linters and many other useful tools in

GitHub Etc similar to that ml Ops is a

field where you are trying to automate

some of the things in machine learning

project development here you need to

learn what is API and then fast API fast

API and flask are the two popular

Frameworks that people use to write

server around a train model once you

have train model you will write this

server so that it can serve HTTP request

coming from a client fast API is getting

popular for which we have once again a

free YouTube video which goes through

all the fundamentals of fast API and you

are creating this You Know sample

website and calling fast API from that

then comes Docker and kubernetes these

two technical tools are used widely in

the industry whenever we build any ml

solution we usually put them in

container and doer is something that

helps you with

conization and you can also use

kubernetes for orchestration okay uh

also make yourself aware about at least

one Cloud platform okay AWS or Azure and

you don't need to go crazy just uh

fundamental understanding of how Cloud

Works create a free uh account on either

Azure or AWS if you're talking about AWS

there is something called Amazon Sage

maker that's a platform that allows you

to do machine learning on the cloud okay

so on the sage maker create a platform

try to run some Notebook on sagemaker

mlops itself is a vast topic and many

companies have a separate mlops engineer

role but as an AI engineer at least you

need to have some understanding of mlop

so don't go crazy here okay because for

details there is mlops engineer it's a

separate career role but as an AI

engineer sometimes when you are working

in a small company where there is no

separate mlops role you will have to do

some of the mlops all right so just

having fundamentals clear is going to be

super important now that you have

learned essential skills in week 23 24

you will be building some machine

learning projects so I have linked two

projects one for regression one for

classification both of these are YouTube

playlist end to endend projects incl

including deployment please go through

them in terms of soft skills you need to

build an ATS resume don't build resum

towards the end you can start building

resume right now ATS stands for

application tracking system which many

companies are using and they will use

this system to filter out your resume so

make sure your resume is ATS compliant

so that it doesn't get filtered

automatically by ATS system we have

created a video on this topic so please

go through that video and and there is

also a checklist that will help you make

your resume ATS compliant so just go

through all this point check check check

and once you have checked all the boxes

your resume will indeed be ATS friendly

other than resume you need to build a

project portfolio website we have linked

some resources here so for example I'm

going to show you one sample a project

portfolio website this website is like

your own website where you are writing

about your skill

what kind of projects you have worked on

and you will give a link to a GitHub or

whatever that online tool is where you

are showcasing your work and here are

some ideas for the assignment the

projects that we have done on YouTube

maybe you can start using different

technology for example instead of flas

use fast API okay in classification

project uh instead of sport celebrity

classification you can use

classification of movie stars or maybe

your family member pictures that will

give your project a unique flavor and it

doesn't look like you're just copying a

project from YouTube now comes a very

hot topic deep learning you'll spend 3

weeks learning about what is neural

network the fundamentals of

convolutional neural network sequence

models such as RNN Etc deep learning is

getting very popular it is the biz of J

llm chat GPT all the hype that you

seeing is using deep learning underneath

for learning deep learning there are two

playlist I will uh refer you to so the

tensor flow is a framework from Google

we have this very popular playlist on

YouTube once again exercises code Theory

everything is covered folks all the

learning resources are available for

free all you need is a willpower

motivation a computer and a stable

internet and then comes end to end deep

learning project for potato disease

classification in this project we built

a mobile app which any farmer can use to

take a picture of a potato plant and it

will tell you whether the plant has a

disease or Not underneath it is using

deep learning and convolutional neural

network week 28 230 you can either learn

NLP or computer vision you don't need to

learn both there will be AI Engineers

who will be specializing either in

computer vision or NLP it's like you

become a general doctor and then you

become lung doctor or heart doctor you

don't need to become both in terms of

NLP these are the topics that you can

learn there is once again a YouTube

playlist that you can use to learn

theory practice coding and also work on

exercises the last two weeks of this

entire 8 month long journey will go in

learning llm and Lang chain these are

the buzzword and and Lang chain is a

framework that is getting very popular

and if you look at any machine learning

engineer positions nowadays majority of

them require you to have some exposure

to Lang chain framework so for this also

I have a playlist where we have covered

all the Lang chain fundamentals and we

have built three projects three llm

projects which you can use to learn as

well as you can put those projects on

your resume obviously with some

customizations remember that in this 8

months you have learned all the

fundamental skills but that doesn't mean

you have become an expert AI engineer

the learning for AI is continuous so

many things are happening every day

therefore from week 33 onwards you'll be

working on more and more projects you'll

be working on building online

credibility through Linkedin Kel uh and

then you'll be applying into jobs and if

you have prepared with sincerity you'll

definitely get a job because there is a

huge boom and there is lot of demand for

people who know AI well now I want to

share tips for Effective learning as

well because there is lot of things that

you have to learn and you want to make

sure that you spend less time and learn

effectively there are some rules for

Effective learning for example you spend

less time in consuming tutorials you

spend more time in digesting

implementing and sharing nowadays people

do reverse they spend more time in

watching videos and for digestion they

spend less time it should be other way

around if you're spending 1 hour in

studies maybe 20 minutes or 30 minutes

you spend in uh watching the tutorials

and remaining time you spend in

digesting then you implement you write

some code and you share it with your

friends group learning is very important

when it comes to sharing in our Discord

uh server you will see partner and group

finder Channel where people say Okay I

want to learn data science uh who wants

to partner with me and this way people

make groups and then they have weekly

Zoom calls where they check progress of

each other you know it's like a going to

gym with bunch of friends if you go

alone you will get bored but if you go

in group you will stay motivated that's

it folks I wish you all the best once

again check video description for the

PDF the entire PDF is included here all

the learning resources are free I wish

you all the best if you have any

question Post in the comment box below

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