What Universities Don't Teach You In AI/ML

Summary

The video discusses the significant gap between university education and practical skills needed in the AI and machine learning industry. It emphasizes the lack of training in applying AI models to real-world business scenarios and generating revenue. The speaker suggests that universities focus on theory rather than practical deployment and staying updated with the latest models and architectures. To bridge this gap, the video recommends self-education, building a personal brand, and leveraging platforms like 'Simply Learn' for industry-aligned AI courses, which can lead to better job opportunities and higher salaries.

Takeaways

• 🎓 Universities often lack practical skills training in AI and machine learning, focusing more on theory than application.
• 🏢 The transition from academia to the corporate world can be challenging due to the gap in practical knowledge required for business use cases.
• 💰 Companies prioritize practical AI applications that solve problems and generate revenue over theoretical knowledge.
• 🛠️ The script emphasizes the importance of learning how to deploy AI models and set up automated pipelines for real-world business applications.
• 📈 The speaker suggests that traditional education paths may not be the most efficient for career advancement in the AI field.
• 👨‍🏫 Many university educators may not be up-to-date with the latest AI models and architectures, which can hinder students' learning.
• 🌐 The script recommends self-education through online platforms and courses to stay current with the latest AI advancements.
• 📚 The AI Career Program mentioned in the script aims to fill the gap by teaching practical skills and personal branding for career opportunities.
• 🏆 The script highlights the value of real-world projects and visibility in the field, which can lead to more opportunities and higher credibility.
• 🔗 Networking and personal branding are key to standing out and securing better job opportunities in the competitive AI job market.
• 🚀 To excel in the AI field, one must go beyond traditional education and actively seek out the latest knowledge and skills to stay competitive.

Q & A

What is the main gap that the speaker identifies between university education and the corporate world in the AI and machine learning field?

The speaker identifies a gap in practical skills, where university education focuses on theory and math but does not teach how to apply AI and machine learning models in real-world business scenarios and production.

Why is it important for AI and machine learning professionals to understand business use cases and generate revenue?

It is important because businesses are focused on providing value, solving problems, and making money. Professionals who can apply AI models to generate revenue and solve business problems are more valuable to companies.

What does the speaker suggest is the reason for the lack of practical skills among university graduates in AI and machine learning?

The speaker suggests that universities focus on theoretical knowledge and do not teach students how to apply that knowledge to business use cases, leading to a gap in practical skills when they enter the corporate world.

How does the speaker describe the typical career path for someone following a traditional university education in AI and machine learning?

The speaker describes a traditional path where individuals spend 5 years getting a degree, then enter the corporate world as interns or junior positions, and it takes a long time to gain senior positions and responsibilities due to the lack of practical skills.

What is the 'AI Career Program' mentioned by the speaker, and how does it aim to bridge the gap between university education and corporate needs?

The 'AI Career Program' is a course that the speaker offers, teaching the practical skills and personal branding necessary to stand out in the AI and machine learning field. It focuses on real-world projects and providing value to businesses, which is not typically covered in university education.

What is the speaker's view on the importance of personal branding and visibility in the AI and machine learning field?

The speaker emphasizes the importance of personal branding and visibility, stating that having practical projects and showcasing one's work can lead to more opportunities and credibility in the field.

What is the 'Learn' platform mentioned in the script, and how does it relate to the AI and machine learning field?

The 'Learn' platform is an online learning platform offering boot camps and courses designed to empower individuals in their career journeys. It has an AI engineering program, created in collaboration with IBM, which covers practical use cases and advanced topics in AI and machine learning.

How does the speaker address the issue of universities not being up-to-date with the latest developments in AI and machine learning?

The speaker points out that most university professors are not interested in teaching and are not up-to-date with the latest models and architectures in AI and machine learning. They suggest that students need to learn on their own to stay competitive.

What are some of the practical skills that the speaker believes are not taught in universities but are essential for AI and machine learning professionals?

The speaker believes that universities do not teach practical skills such as deploying AI models, setting up retraining loops, automating pipelines, and spotting business use cases for AI and machine learning applications.

What advice does the speaker give to individuals looking to stand out and get ahead in the AI and machine learning field?

The speaker advises individuals to learn the practical skills not taught in universities, build personal branding, network, and understand the market to get different job opportunities. They also emphasize the importance of presenting oneself well, such as having a professional setup for job interviews.

How does the speaker compare the traditional university education system to having a map for navigation?

The speaker compares not having a map to not having a guide or system for navigating life, including career paths. They suggest that having a 'map' or system, like the one taught in their AI Career Program, can significantly improve one's chances of success.

Outlines

00:00

🎓 Gap Between University Education and Corporate Needs

The speaker discusses the significant gap that exists between what is taught in universities regarding AI and machine learning and the practical skills required in the corporate world. They emphasize the lack of instruction on how to apply AI models in production and solve real-world business problems. The speaker points out that universities focus heavily on theory and math but often fail to teach students how to implement their knowledge in a way that generates value and revenue for companies. This gap can lead to a prolonged period in junior positions before individuals can advance to more senior roles where they can work on impactful projects.

05:01

🚀 Importance of Practical Skills and Staying Updated in AI

This paragraph highlights the importance of staying current with the latest developments in AI and machine learning, which universities often do not cover due to outdated curricula and a focus on research rather than teaching. The speaker suggests that most professors are not up-to-date with the newest models and architectures, and students need to take the initiative to learn about these advancements independently. The speaker also stresses the necessity of practical skills, such as deploying AI models and setting up automated pipelines, which are crucial for providing business value and generating revenue, and are often not taught in universities.

10:02

🌟 Standing Out in the AI Field by Building a Personal Brand

The speaker encourages individuals to stand out by building a personal brand and showcasing their practical skills in AI and machine learning. They discuss the importance of networking, personal branding, and the ability to present oneself professionally during interviews as key factors in securing job opportunities and higher salaries. The speaker also emphasizes the value of having a 'map' or strategy for career advancement, suggesting that without one, individuals may find themselves lost and unable to navigate their professional journey effectively.

Keywords

💡AI (Artificial Intelligence)

AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the context of the video, AI is a core subject of study in universities and a field where graduates are expected to apply their knowledge in practical, real-world scenarios. The script mentions the gap between theoretical AI education and the practical application of AI models in business.

💡Machine Learning

Machine learning is a subset of AI that enables computers to learn from data and improve from experience without being explicitly programmed. The video emphasizes the importance of understanding machine learning models and their practical application in production environments to solve business problems and generate revenue.

💡Production

In the script, 'production' refers to the stage where AI and machine learning models are implemented in a live business environment. The video discusses the gap in university education regarding taking theoretical models and deploying them into production to solve real-world business use cases.

💡Business Use Cases

Business use cases are specific scenarios or problems within a business context that require solutions. The video highlights the importance of understanding and applying AI and machine learning to these use cases to provide value and solve problems effectively.

💡Practical Skills

Practical skills are hands-on abilities that can be applied in real-world situations. The video identifies a lack of practical skills training in universities as a significant gap, where graduates need to know how to apply AI and machine learning models to business scenarios to generate value and revenue.

💡Corporate World

The term 'corporate world' refers to the business environment outside of academia. The video discusses the challenges graduates face when transitioning from university to the corporate world, particularly the application of their AI and machine learning knowledge in a business context.

💡Intern Positions

Intern positions are temporary work opportunities for students or recent graduates to gain practical experience in their field. The script mentions the difficulty of moving from academic learning to intern positions in the corporate world, where practical application of AI and machine learning is crucial.

💡Jupyter Notebook

A Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations, and narrative text. The video uses the Jupyter Notebook as an example of a tool where students might spend time experimenting with data and models, contrasting it with the need for practical application in the corporate world.

💡Personal Branding

Personal branding is the process of creating a unique image or identity in a professional context. The video suggests that having a strong personal brand can help individuals stand out, gain credibility, and secure more opportunities in the AI and machine learning field.

💡Simply Learn

Simply Learn is mentioned in the script as an online learning platform offering various courses, including an AI engineering program. The video presents Simply Learn as a resource for individuals to acquire practical skills and knowledge that may not be covered in traditional university education.

💡Industry-Aligned Curriculum

An industry-aligned curriculum is one that is designed in collaboration with industry professionals to ensure the content is relevant and up-to-date with current practices. The video praises the AI engineering program on Simply Learn for having an industry-aligned curriculum, which is crucial for providing practical and marketable skills.

💡Retraining Loops

Retraining loops refer to the process of continually updating machine learning models with new data to improve their performance over time. The script points out that universities often do not teach the setup of retraining loops, which is a practical skill needed in the corporate world for maintaining and improving AI models.

💡Automated Pipelines

Automated pipelines are systems that allow for the seamless flow of data and processes from one stage to another without manual intervention. The video discusses the importance of knowing how to set up automated pipelines for deploying AI models in a business environment, a skill not typically taught in universities.

💡Practical Projects

Practical projects are hands-on assignments or tasks that apply theoretical knowledge to real-world situations. The script emphasizes the importance of having practical AI and machine learning projects to showcase one's abilities and gain credibility in the field.

💡Modern Day Suit

The term 'modern day suit' is used metaphorically in the script to describe the professional image and setup one presents in a job interview, such as proper lighting and microphone setup. It illustrates the idea that presentation and professionalism can provide a competitive edge in the job market.

Highlights

There's a large gap between what is taught in universities and the practical skills needed in the corporate world for AI and machine learning roles.

Universities do not teach how to apply AI and machine learning models in production or to business use cases effectively.

In the corporate world, the focus is on providing value, solving problems, and generating revenue, not just theoretical knowledge.

Practical skills are crucial for transitioning from academia to industry, but they are often lacking in university education.

The time and cost implications of deploying AI models in a corporate setting are not addressed in university curricula.

The speaker emphasizes the importance of knowing how to take models from development to production in a business context.

University education often lacks the teaching of how to apply theoretical knowledge to real-world business scenarios.

The difficulty of moving from intern to more senior positions without practical skills is highlighted.

The speaker's AI career program aims to teach practical skills not covered by traditional university education.

Personal branding and showcasing practical projects are essential for gaining credibility and opportunities in the industry.

Companies value real-world AI and machine learning projects more than grades or theoretical knowledge.

Simply Learned offers an AI engineering program in collaboration with IBM, focusing on practical use cases and industry-aligned curriculum.

The Simply Learned program includes live classes, hands-on projects, and a custom project to solidify skills.

Universities often fail to teach the latest models, architectures, and developments in the machine learning field.

The importance of staying up-to-date with the newest technologies and research in the field is underscored.

The speaker shares personal experiences of self-education through online resources and the benefits it had on their career.

The video encourages viewers to stand out by gaining practical skills and building a personal brand in the AI and machine learning industry.

The necessity of having a 'map' or strategy for career advancement and opportunities is discussed.

Practical knowledge and skills in solving real-world AI and machine learning problems are emphasized as key to career success.

Transcripts

so in this video here we're going to

talk about what universities don't teach

you in the AI machine learning field

because there act like a very large gap

when you go from University once you

graduate and get into the corporate

world get into your intern positions and

also your Junior positions once you

start to get your first job we're going

to cover a bunch of different aspect of

it what they don't teach you and also

what you can do on your own so the most

important thing that universities are

not teaching you is basically all the

Practical stuff how you take your AI

machine learning models and apply them

into production apply them on top of

business use cases because once you get

into a job once you get into a business

it's all about providing value solving

use cases solving problems and then also

generating money we can't just sit in

our Jupiter notebook playing around with

the data implementing our own custom

models layer by layer trying out some

different architectures fine-tuning some

models H primaries and so on we can't

spend month on it because let's say that

you're making 120k per year as machine

learning and AI engineer if you divide

that on a monthly basis it is basically

$10,000 companies they're not going to

spend tens of thousands of dollars on

you just sitting there playing around

with the models in a jbit a notebook we

need to know how can actually like go

from the models into production and

solving problems so we can generate

money because at the end of the day

companies out there they are there to

provide value help other people and

companies and also make money so this is

a very large gap when you go from un

University into the corporate world is

basically just the practical skills in

University we learn all the math all the

theory and so on but we don't learn how

to act like apply that on business use

cases and this is where people are

lacking a ton and also why it takes so

long to get into intern positions Junior

positions and so on before you start to

level up get into more senior positions

get responsibility start to get into the

business side of it as well and also

just be able to work on the problems and

project that you want as well most

people they're following the traditional

path they're taking a University degree

for 5 years they're just grinding all

the theory trying to get the highest

grades and so on then they get into the

corporate world stting an intern

Precision Junior precisions and so on

where they actually just have all the

vertical knowledge they have no idea

about how can you deploy AI models how

can we set up whole like retraining

loops and so on automate the whole

pipeline provide business value how do

you spot use cases where we can apply a

on machine learning too and this this is

where money is made and also where all

the like the fun and nice projects are

so this is a very large gap that people

are facing they spend five years on that

they spend three to five years in junior

prisions or internships before they

start to get more responsibility and

into more senior prisions where the fun

starts inside my AI career program I'm

basically teaching you my whole path so

definitely check that out where we go

over all the personal branding how we

can get more opportunities and so on

because we need to take our work we we

need to provide value we need to have

practical projects practical machine

learning AI projects and use cases put

them out there so we can be visible and

also get credibility for the work that

we done because at the end of the day

like companies they don't really care

that much about your grades compared to

having real world Aon machine learning

projects that you put out there and show

it and you will get way more

opportunities earlier on so definitely

check out the air career program where I

teach you my whole system because I

basically spending my b L just trying to

grind get good grades and so on but it

was not the case I was not getting any

good grades at all I started to learn

everything on YouTube myself taking

different courses and so on starting

leveling up and once I got into the

master degree I pretty much knew

everything beforehand so much grades

they become way better I got the best

grades out there without spending any

time on my University degree because I

just KN everything from the things that

I looked up on YouTube courses and so on

beforehand throughout the whole process

I basically just reced everything and

put my work out there through my YouTube

LinkedIn GitHub and so on so you guys

can pretty much just follow my whole

path and this is what I'm teaching

inside my program so if you want to

learn more practical stuff and also what

universities are not teaching you simple

learn is act like a very good platform

it's a online learning platform they're

offering a wide array of boot camps and

also like courses designed to empower

individuals in their career Journeys so

it's basically like a very cool platform

they have tons of courses in there but

their AI in engineering program is very

impressive it's basically just a

comprehensive 11mon online boot camp

that covers everything from the basics

to advaned topics in machine learning

and AI but also with a focus on act like

practical use cases you'll dive into

machine learning natural language

processing deep learning and even

cutting its topics like generative AI

with the nearest large language models

so what sets this program here part like

first of all it's created in

collaboration with IBM as you can see

here so you're getting industry aligned

curriculum and also tools so this is

where all the Practical stuff comes in

and also the skills which are in demand

out in the corporate world and here in

this program here specifically from IBM

so you'll earn IBM certificates and

you'll also get access to exclusive IBM

master classes and hackaton The Learning

Experience is pretty much just top-notch

with live online classes led by industry

experts Hands-On projects and a castom

project to solidify your skills you'll

also have lifetime access to self-paced

learning content so everything is

available in there but don't just like

take my word for it like the program it

has excellent reviews you can go in and

check it out from the past students who

have transformed their careers pretty

much many have landed exciting new jobs

or like received significant salary

increases after just completing this

course you can just go in visit the

simply learned website to explore the

full curriculum watch some sample

lectures and also just see if this is

the right fit for you and the second

problem with universities that they

don't teach you is BAS basically all the

new stuff all the new models

architectures all the things happening

in a machine learning they are not up to

date like most of the teachers

professors and so on at the universities

like I have talked with a bunch out

there they're not really interested in

teaching other people they're just there

for the research writing emails and

basically just having fun working on the

projects that they want on their own so

it's not the best resources I usually

just say that what is the chance that

the best teacher at the specific topic

that you are interested in is at your

your University and it's very low I'm

not saying that all universities are bad

like we have all the high ones Stanford

MIT and all those universities but let's

just be real most of you guys out there

including myself like we're not going to

be at Stanford MIT and so on getting

Master degrees in artificial

intelligence so we kind of like just

need to figure out on our own we need to

stand out because if we just follow the

exact same path as everyone else like

we're just going to limit the

opportunities on our side and this is

really important University they don't

teach you any of this like they're just

teaching you a whole system how you can

follow a system and they will take 5 8

10 years before you actually get into

positions be able like you need to grind

10 years just to get opportunities

instead of actually like standing out

learning the relevant stuff that you can

apply to business use cases so you can

make more money but also get more

opportunities earlier on so the teachers

at my University I was going to like a

pretty pretty average University they

had no idea like it wasn't 2022 or

something like that that like the

teachers teaching me machine learning

and AI they had no idea what the

transform architecture was the attention

mechanism and so on it came out 5 years

ago and they have no idea about all the

new research coming out all the new

models all the new architectures and so

on so how do you expect to be the best

out there how do you expect to stay

competitive and so on if you're just

following what you're learning at the

universities so the universities they're

not teaching you what's up to date or at

least most universities are not and I

can tell you that because again the

teachers they're not interested in

teaching most of them are not up to date

they basically just treat it as a 9 to5

job they go to it they're not really

interested in learning staying up to

date with the newest Technologies and so

on and even though I wish that all of us

out there could just be like the best

researchers at meta Google Tesla Amazon

and those guys like most companies they

don't have the resources to have

researchers or like people s just doing

all theoretical stuff testing out

different model architectures working on

fine-tuning some high parameters and so

on and just spending weeks months and so

on cleaning up data set trying out some

different pre-processing methods and so

on like the time to market for the

product and businesses like it needs to

be as fast as possible like companies

they don't have time they don't have

resources and at the end of the day the

only thing that they care about is both

to provide value but most importantly to

make money so it will not be worth it

for a company to High you if you're not

able to provide enough value take your

AI and machine learning models apply to

business use cases and act like generate

money set up automated pipelines deploy

models out there know all the Practical

stuff because they don't care about your

grades how good you are at solving math

problems and so on compared to if you

know how to solve real world AI computer

vision machine learning projects and act

like putting it out there being

independent being able to do that

without needing too much help it will

just unlock so much more opportunities

at the end of the day and also more

money because at the end of the day you

need to be worth it for the company both

to hire you but also to give you a good

salary or even increase and raise your

salary along the path so if you don't

want to just get stock at a job you

really have to do something

extraordinary and in today's world it

doesn't really have to be that much it's

just put your work out there build the

credibility around you build some

personal branding and so on and also

just know the market know how you can

get into different job opportunities

networking how you can negotiate share

your skills and so on and basically just

stand out it could even just be when

you're interviewing like how do you act

like just present yourself compared to

let's say that I'm in a job interview

someone is just sitting with their phone

or the MacBook with the web camera just

imagine the advantage that I have if I

have this camera set up my lighting my

microphone everything I call this the

modern day suit so just imagine the

advantage I will have in a job interview

if I had this whole setup here to

compare to someone else so it's all the

small details all of them are just

accumulating up if you just know it

again we can go back to if you don't

have a map like how do we expect to

navigate in an environment if you don't

have a map it is the exact same thing as

if you're driving in a new city or even

in a new country you have no idea about

how you can get to a destination without

a map it works exact same way in every

aspect of life investing relationship

your work interest sport Sports hobbies

and so on at the end of the day it is

way easier if we have a map so really

encourage you guys to get out there

stand out just try to stand out it is

very simple I'm not saying it's easy to

do but it's very simple if you just have

a map stand out there because I couldn't

imagine all the opportunities that I

have now if I just go like two three

years back one year ago I could

basically just go straight out of

University get 10 different offers I

could choose between all the offers that

I wanted to work with the projects that

I want to work with and I could get into

more senior positions just out of the

box because I had the Practical

knowledge I had the practical skills how

can I act like solve real world AI

machine learning projects so it's way

more important if you want to go in that

direction so I hope you have learned a

ton throughout this video here at least

there something that universities are

not teaching you they're not teaching

you this year because they have no idea

behind it don't just follow the

traditional system like if you do that

your opportunities they will be so

limited your salary the projects your

opportunities everything will just be

limited because their competition it

will be harder everyone is doing it and

you don't really have to do that much I

hope this video here have helped you and

also encouraged you maybe opened up your

eyes of some of the stuff here which is

act like true there is a large gap from

going from University into a corporate

job because again at the end of the day

they have two different purposes

teaching your theory providing business

value and making money