How I'd Learn AI in 2024(If I could start over)

Summary

The video provides a comprehensive roadmap for learning Artificial Intelligence (AI) without a formal degree. The speaker, Zano, explains that AI can be mastered in 6-8 months with dedication. Key topics include understanding the basics of AI, the importance of mathematics (linear algebra, calculus, and probability), and learning Python programming. Zano emphasizes practical learning through projects and recommends resources like YouTube channels, online courses, and books. He also discusses popular AI frameworks such as PyTorch, Scikit-Learn, and TensorFlow, and suggests practicing on platforms like Kaggle to build a strong portfolio for job applications.

Takeaways

• 💻 You don't need a degree to learn AI anymore—6-8 months of consistent learning with a roadmap is enough.
• 📚 Free resources exist for AI education, but paid resources provide comprehensive coverage.
• 🤖 AI involves recognizing patterns in data and using them to predict future outcomes. Machine learning is about how models learn these patterns.
• 🔢 95-99% of AI is based on mathematics—key areas include linear algebra, calculus, and probability.
• 📐 Linear algebra is vital for handling large datasets, while calculus helps optimize models, and probability is used to assess prediction confidence.
• 🐍 Python is the best programming language for AI and machine learning due to its wide usage and community support.
• 📊 Key Python libraries for AI are Pandas (data handling), NumPy (numerical operations), and Matplotlib (data visualization).
• 🧑‍💻 Suggested machine learning frameworks include PyTorch, Scikit-learn (beginner-friendly), and TensorFlow (more advanced).
• 🎓 Recommended free learning resources include Andrew Ng's Coursera courses and Andrej Karpathy's YouTube series for deep learning.
• 🏆 Kaggle is a platform for practicing AI and machine learning projects, offering datasets and competitions to build skills and portfolios.

Q & A

What is the main requirement to start learning AI according to the video?

You don't need a degree to learn AI anymore. All you need is a laptop and a well-structured learning roadmap.

How long does it typically take to self-educate in AI?

It generally takes 6 to 8 months of dedicated time investment to self-educate in AI.

What is artificial intelligence in simple terms?

Artificial intelligence is a model or program that recognizes patterns in data and uses those patterns to predict future outcomes.

Why is mathematics so important in AI and machine learning?

Mathematics is crucial because 95-99% of AI involves understanding and applying mathematical concepts, especially in areas like linear algebra, calculus, and probability.

Which programming language is most recommended for learning AI and machine learning?

Python is the most widely recommended programming language for AI and machine learning due to its extensive support for machine learning models and community support.

What are the main Python modules used for data handling in AI?

The main Python modules for data handling are Pandas (for data processing), NumPy (for numerical computations), and Matplotlib (for data visualization).

What are the key math topics to focus on for AI and machine learning?

The key math topics are linear algebra, for working with large datasets, calculus, for optimizing models, and probability, for assessing the accuracy of model outputs.

What are the top frameworks for machine learning mentioned in the video?

The top frameworks are PyTorch, Scikit-learn, and TensorFlow. PyTorch and Scikit-learn are more beginner-friendly, while TensorFlow is more advanced and abstracts much of the underlying math.

What free resources are recommended for learning AI?

The video recommends free resources like the YouTube channel 'ThreeBlueOneBrown,' Khan Academy, Brilliant.org, and the free AI courses by Andrew Ng on Coursera.

Where can learners practice and compete in AI and machine learning problems?

Learners can practice on Kaggle, a platform with data sets, practice problems, and competitions for AI and machine learning.

Outlines

00:00

🚀 The Future of AI and Learning Path

The author predicts that AI will dominate the market by 2030 and emphasizes that formal degrees are no longer essential for learning AI. Instead, one can self-educate with the right resources and dedication. The author, Zano, shares his experience studying AI at IIT but asserts that anyone can learn AI within 6-8 months of consistent effort. This video will outline a roadmap for mastering AI without a formal degree, covering both free and paid resources, starting with an introduction to AI and machine learning, and how models recognize patterns and predict outcomes.

05:01

📊 The Importance of Mathematics in AI

Mathematics is fundamental to AI, and the author stresses that 95-99% of AI revolves around math. While frameworks like TensorFlow might obscure much of the underlying mathematics, it’s crucial to grasp core concepts such as linear algebra, calculus, and probability. Linear algebra is essential for handling large datasets, calculus for optimizing models, and probability for understanding the confidence in AI predictions. The author advises starting with resources like Three Blue One Brown's videos and eventually exploring Khan Academy and Brilliant.org for further learning in these areas.

10:02

🐍 Getting Started with Python for AI

Python is the go-to programming language for AI and machine learning due to its widespread use and vast community support. The author recommends focusing on Python basics like data types, variables, loops, and functions before moving on to advanced topics like recursion for neural networks. For learning Python, channels like 'Sendex' or resources from 'FreeCodeCamp' are suggested. The next phase involves learning essential Python libraries for data handling, such as Pandas for data manipulation, NumPy for numerical operations, and Matplotlib for data visualization.

📚 Choosing Developer Stacks and Frameworks for AI

Before diving into machine learning frameworks, the author suggests getting familiar with the development environment like Jupyter Notebook (part of Anaconda). Then, one should explore popular AI frameworks like PyTorch, Scikit-learn, and TensorFlow. PyTorch and Scikit-learn are recommended for beginners, while TensorFlow is best suited for those more advanced in their AI journey. The author also advises reading specific books on AI to deepen knowledge at one’s own pace. By learning the mathematical foundations of machine learning algorithms, one can better understand how models work and optimize their performance.

📖 Best Free Resources to Learn AI

The author highly recommends Andrew Ng’s courses on Coursera as a top-tier free resource for learning AI and machine learning. These include a three-course series on machine learning and a five-course series on deep learning. Additionally, the 'Neural Networks from Scratch' playlist by Andrej Karpathy on YouTube offers valuable insights into building NLP models from scratch. After completing these courses, the next step is practical learning through Kaggle, a platform where aspiring AI practitioners can find datasets, participate in challenges, and build projects to strengthen their resumes.

Keywords

💡Artificial Intelligence (AI)

Artificial Intelligence 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 the overarching theme, with the speaker discussing the potential of AI to be a significant market by 2030 and the possibility of learning it through self-education without a formal degree.

💡Self-education

Self-education is the process of learning on one's own, typically without the guidance of a teacher or formal institution. The video emphasizes that with dedication and the right resources, one can self-educate in AI within 6 to 8 months, highlighting the accessibility of AI education.

💡Machine Learning

Machine Learning is a subset of AI that focuses on the development of algorithms that allow machines to learn and improve from experience without being explicitly programmed. The video simplifies it as a process where a model recognizes patterns and predicts outcomes, which is fundamental to understanding AI.

💡Mathematics

Mathematics is the study of numbers, quantities, and shapes, and it plays a crucial role in AI. The video stresses that a strong foundation in mathematics, particularly linear algebra, calculus, and probability, is essential for understanding the algorithms and models in AI.

💡Linear Algebra

Linear Algebra is a branch of mathematics concerning linear equations, linear transformations, and their representations in vector spaces and through matrices. It is important in AI for handling large datasets and is mentioned as a key mathematical domain to understand for AI learning.

💡Calculus

Calculus is a branch of mathematics that studies how things change and is used to model and optimize AI models. The video mentions that calculus is vital for understanding how well a model is performing and for optimizing error functions.

💡Probability

Probability is the measure of the likelihood that an event will occur and is crucial in AI for understanding the confidence levels of model predictions. The video explains that AI outputs are often probabilistic, reflecting the uncertainty inherent in predictions.

💡Python

Python is a high-level programming language that is widely used in AI and machine learning due to its simplicity and the extensive library support it offers. The video recommends Python as the programming language to learn for AI, emphasizing its importance in the field.

💡Data Handling

Data Handling refers to the processes involved in managing and processing data. In the context of AI, it is crucial for pre-processing data before it is fed into models. The video mentions Python modules like pandas, numpy, and matplotlib for data handling.

💡Jupyter Notebook

Jupyter Notebook is an open-source web application that allows creation and sharing of documents containing live code, equations, visualizations, and narrative text. It is mentioned in the video as a common environment for training AI models.

💡Frameworks

Frameworks in the context of AI refer to pre-built structures or libraries that provide support for developing AI applications. The video discusses three popular AI frameworks: PyTorch, SciKit-Learn, and TensorFlow, which are essential tools for machine learning.

💡Kaggle

Kaggle is an online platform for data science and machine learning competitions. It is mentioned in the video as a resource for finding datasets, practice problems, and participating in contests, which can help learners apply their AI knowledge and build a portfolio.

Highlights

AI will be the biggest market by 2030, and you don't need a degree to learn it, just a laptop and the right roadmap.

6 to 8 months of dedicated time is enough to self-educate in AI.

AI is a model that recognizes patterns to predict future outcomes, with machine learning as its core process.

Understanding linear algebra, calculus, and probability is crucial for learning AI.

Mathematics is 95-99% of AI; frameworks like TensorFlow can mask some of the underlying math, but knowledge is still essential.

Python is the go-to language for AI and machine learning, due to its extensive libraries and community support.

You only need to learn the basics of Python: data types, variables, control flow, loops, functions, and recursion.

Three essential Python modules for data handling are Pandas (data structuring), NumPy (matrix operations), and Matplotlib (data visualization).

Jupyter Notebook is the standard environment for training models, particularly through Python frameworks like PyTorch and TensorFlow.

PyTorch and Scikit-learn are beginner-friendly frameworks for machine learning, while TensorFlow abstracts the math even further.

Andrew Ng's Coursera courses are considered the best free resources for learning AI and machine learning.

Kaggle is an excellent platform for AI and ML projects, offering datasets and practice problems for real-world training.

Spending 3 to 4 months on Kaggle, building projects, and solving challenges is recommended to create a solid AI resume.

Despite having a degree in AI, the speaker interned as a software engineer at Microsoft, illustrating diverse career paths in tech.

The roadmap outlined includes free and paid resources, project-based learning, and a focus on applying mathematical concepts to AI models.

Transcripts

we all know that AI will be the biggest

Market by 2030 and you don't really need

a degree to learn it anymore all you

need is a laptop and the perfect road

map for learning hi I am Zano and I have

completed my btech degree in artificial

intelligence from an IIT but you only

generally need 6 to 8 months of good

time investment to self-educate yourself

in Ai and in today's video I'm going to

tell you exactly which road map you need

to follow to learn AI this is the road

map that I would follow follow if I had

to learn AI without a degree all over

again in 2020 now free resources may I

will obviously share different free

resources for different domains because

there is not a single free resource that

covers everything but there are paid

resources which covers everything and

I'll talk about that later in this video

so firstly let's try to understand what

is artificial intelligence and machine

learning in a very simple term

artificial intelligence is a program or

a model that can recognize patterns in

different things and help helps its

learning to predict future outcomes and

how it learns these patterns and

recognize these patterns is basically

the whole domain of machine learn

because how such models are trained is a

little bit complex and Beyond the scope

of this video but if you're really

interested three blue one brown has made

a very intuitive video which is

something that sparked my interest into

Neal networks and everything you can

watch that video right over here in very

broad terms how we train AI model is

something like this we feed in data in

in terms of inputs and outputs to the

model and it tries to figure out what

are the logic and what is the pattern is

that will give us these outputs from the

corresponding input so it can try to

accurately predict outputs on unforeseen

inputs and that brings us to the

probably most important part when you

are trying to learn artificial

intelligence or machine learning and

that is mathematics Believe It or Not 95

or 99% of AI is purely mathematics if

you do not like maths you will not have

a good time learning AI now a lot of

people will tell you that the newer

Frameworks like tensor flow will

actually mask the lot of mathematics

that is going on behind the scenes of

the algorithms and you don't really need

to learn it while that is true to some

extent you really need to understand the

underlying mechanisms of how things work

and why things work to also understand

which things are the best suited for

your particular problem and that is why

you need to have very good understanding

of a few domains in mathematics

thankfully the few domains that I'm

talking about are linear algebra

calculus and probability linear algebra

is very important because how else will

we work with data sets with millions and

billions of data interest in them

calculus is important to understand and

optimize how well our model or our

function our program is actually working

and basically optimizing the error

function again I'm not going to go too

functional too technical in this sorry

and probability is important to

understand how good our output is coming

out to be because the output of a

machine learning model is never black

and white it is never yes or no it

always says it is I'm 99% sure this is

yes but there is still 1% It can be no

so that is how our confidence in the

output is shown and that is why

probability and understanding basic

probabilistic distributions is very very

of course that is a very simplified way

of talking about how all these things

encapsulate each other but there is a

very good point of understanding to

start with and if you are still feeling

uncomfortable to get you to have a good

understanding you can go on to three

blue one Browns Channel and literally

watch every video that is related to

this I will share the links of few

videos in the description below and uh

after you have a good understanding of

how things work then you can go to maybe

Khan Academy or brilliant.org to

actually take full-fledged courses I

think Khan Academy is free b.org is you

can take this fully fledged courses on

linear algebra probability and calculus

and we will be good to go now once we

are thankfully done with maths it is

finally time to move on to get our hands

dirty with a programming language and

when it comes to artificial intelligence

and machine learning there is no other

right answer other than python python is

the most widely used and probably the

only language that has all these

different machine learning models and uh

community support and all of that so

there is no other right answer start

with python now initially you don't

really need to learn the whole vastness

of python because python has like

millions of libraries and a lot of

different features you only need to

understand the basics because we're

going to be using python and a few of

its modules to do the mathematical

calculation that we cannot do our

however python is a very vast language

you only need to know the basics of

python which include things like data

types variables control flow Loops

functions and the general idea of

recursion recursion will be required

when we try to learn neural networks and

deep learning there will be a very

similar concept called backtracking

which is honestly a huge pain I hate

that particular concept it's like I have

studied it so many times I still cannot

memorize it how exactly it works it it's

it's so so if you're looking for

free resources to Learn Python I can

suggest you two different things one is

this YouTube channel called sendex which

is the channel that I learned python

from now this channel is not like other

tutorial channels because this guy does

not really spend much time focusing and

clearing ver ifying the basics he

believes in building small small

projects and that way you learn better

and that is how I like to learn things

through projects and that is why this

was the best channel for me but if you

like the general tutorial is kind of

videos you can just surf on YouTube or

go to free code Camp to find small 3 4

hours uh worth of python beginner

courses that will be more than enough

now the next challenge in the process is

data handling data and it has like

thousands or millions of data entries

how do we handle that how do we process

that how do we work with it that is when

three different python modules come into

play number one is pandas this is

basically for holding data structuring

it and basically processing any type of

pre-processing that you need to do to

the data before feeding it to the model

that is done with pandas number two on

the list is numpy basically all the

Matrix multiplications and all other

determinants and matrices related

operations that we're going to do on our

data set that is is done with numpy

basically anything related to numbers

anything related to computation can be

done with numpy third on the list is

matplot lib now matplot lib as the name

suggests it is used to basically plot

different graphs and curves because as

you say there is a data set with

millions of data entries it is very hard

to understand anything and draw any

conclusion any resolution from that

particular data set that is why we need

to sometimes plot the data set against

some property that is included in the

data set again if any of this does not

make sense right now do not worry if you

once you are going into the process you

will understand everything but yeah B

Cloud live is basically a module that

helps us plot graphs and make like pie

charts and diagrams and V diagrams

whatever you want from a particular data

that is the main three modules we are

going to be using and it will cover like

99% of all the modules that you're going

to be using while using python as your

vessel for artificial intelligence or

machine learning now at this point of

your journey before picking up a

particular developer stack for machine

learning I will highly suggest that you

pick up one of these books that I have

shown on the screen because they are

very beautifully written I have

personally read one of these books and

they are essentially like a gold mine of

knowledge and what I like about books

when I'm learning to code like it's a

little bit counterintuitive that you're

learning to code from a book but what I

like about it is that I can learn

actually at my own pace like if it's a

10hour long tutorial I'm learning from

even if I play it at 2x I still need 5

seven hours because I also have to

practice and everything and with books

out of the way I really need to talk

about the developer stack that you're

going to be using for machine learning

and uh before going into the developer

stacks and the three top Frameworks that

almost everyone uses in the world I also

need to talk about the environment where

you're going to be training models that

is generally the most Ed one is Jupiter

notebook which is a part of a bigger

thing called Anaconda you don't need to

know about Anaconda but jupyter notebook

is something that you will stumble

across in almost any of the higher like

Advanced tutorials in machine learning

but yeah once that is done you have to

choose between one of these three

Frameworks machine learning Frameworks

that have a lot of machine learning

algorithms pre-coded into them one is py

torch psychic learn and tensorflow while

pytorch and psychic learn are a little

more beginner friend friendly and they

sort of soase the math the underlying

math that is going around tensor flow is

something that sugarcoats everything and

it puts everything in a black box you

wouldn't even know what maths are going

on so when you are starting out I will

highly suggest starting with py torch

and whenever you are maybe four 5 months

into the journey you can go back and

start using tensor flow then you can

finally appreciate how beautiful of a

framework tensor flow is now there is a

chance you will be choosing this

developer framework for your yourself or

whatever course that you are following

will assign you a particular framework

to use like this is the point when you

are going to finally learn the concepts

of how ml algorithms and deep learning

algorithms work how do the maths work

and how the models are optimized you are

going to understand all the underlying

mathematics and all the underlying

optimization principles how we

differently like how we do all the

operations you're going to understand

the cruxs of all of them and this is is

probably the hardest part to learn and

if you're looking for free resources

there is this three course I think it's

a three course series by Andrew NG on

corsera which is like the Holy Grail of

free resources on AI that is on the

internet this is probably the best free

resource that is available on the

internet in the realm of machine

learning I personally learned everything

from it and uh you can also check it out

I'll leave a link in the description if

you want to go much further into it if

you want to go into deep learning there

is also another I think five courses

series by Andrew and someone else on

corser as well that is also free there

is also the neural network Z2 hero

playlist that is available on YouTube by

Andre karpati I think that is how you

say his name and that is honestly a gold

mine if you can follow through with it

because he builds an NLP model from the

very very scratch and if you can follow

through with it if you can replicate

whatever he is doing you're going to

learn a lot of things after you have

completed those courses by Andrew NJ

that I talked about you are sort of all

set to start building and how do you

build them you go to this particular

website called kaggle kaggle is the uh

Ai and ml equivalent for like code

forces or lead code here you can find

data sets and practice problems and a

huge number of them where many people

are participating and competing against

each other there are contests and

everything so you have to spend a quite

some amount of time 3 to 4 months at

least even if you're sitting down every

day at least that amount of time is

needed before you have a good handful of

projects and a good resume and you can

start applying for jobs now you know

what the funny part is is that although

I had a btech in AI my third year

internship I did it as a software

engineer in Microsoft and if you want to

know how exactly I did that here is a

video talking exactly about that