How To Self Study AI FAST

Summary

This video script introduces an engaging method to learn AI without getting overwhelmed. It suggests starting with the basics and building a simple AI project to maintain motivation. The script outlines a step-by-step learning process, from Python fundamentals to advanced machine learning and deep learning concepts, recommending various resources for different learning styles. It emphasizes interactive learning with platforms like Brilliant for math and statistics, and practical application through building AI models and utilizing APIs.

Takeaways

• 😀 The Renon Method or Concentric Circle Method is introduced as an alternative learning approach for AI to avoid getting stuck or bored.
• 🎯 The initial learning phase should focus on the basics of AI, including high-level understanding of machine learning and how to use AI models with Python.
• 🚀 Aim to build a simple AI project, like a study tool or personal assistant, as soon as possible to maintain motivation and interest.
• 📚 After completing a basic project, use the excitement as motivation to delve deeper into the subject, expanding knowledge layer by layer.
• 🌭 Machine learning is illustrated with the 'hot dog, not hot dog' model, showing how computers learn to recognize patterns in data.
• 🤖 Large language models, like the ones powering chatbots, are explained as systems that predict the next word in a sentence based on previous words.
• 🛠️ For beginners, learning Python basics, APIs, and understanding of large language models are essential to start building AI products.
• 📈 Intermediate learners should focus on Python modules for data manipulation, and foundational mathematics including calculus, linear algebra, and probability.
• 📊 Statistics knowledge is vital, including understanding descriptive and inferential statistics, hypothesis testing, and distributions.
• 🧠 Deep learning is an advanced subfield of machine learning that involves stacking layers of artificial neurons to perform complex tasks.
• 🌟 The script emphasizes the importance of choosing one learning resource and applying the knowledge through building projects rather than trying to consume all available resources.

Q & A

What is the 'Renon Method' or 'Concentric Circle Method' for learning AI as introduced in the video?

The 'Renon Method' or 'Concentric Circle Method' is a learning approach where one starts with the basics of AI and then gradually expands their knowledge outwards in concentric circles. It involves learning just enough to build a simple AI project quickly, then using the excitement from that project to dive deeper into the next layer of knowledge.

What are the basics of AI one should learn according to the video?

The basics of AI include understanding how machine learning works, how large language models function, and most importantly, how to use these models with Python.

How long does the video suggest it might take to learn enough AI to build a simple project?

The video suggests that it could realistically take about a month if you have zero coding experience, and a week or two if you have some intermediate experience in Python.

What is an example of a simple AI project that a beginner might build?

An example of a simple AI project is a study tool or a personal AI assistant.

What is the definition of machine learning provided in the video?

Machine learning is defined as a way for computers to learn and make decisions by themselves by studying and recognizing patterns in data.

What is a 'convolutional neural network' as mentioned in the video?

A 'convolutional neural network' (CNN) is a type of machine learning model that is used for image recognition tasks, such as distinguishing between hot dogs and non-hot dogs.

What are some of the fundamental mathematical concepts one should understand before diving into machine learning?

Fundamental mathematical concepts include the basics of calculus, linear algebra, and probability.

What programming skills are necessary for learning AI as per the video?

Necessary programming skills include understanding variables, data types, if statements, loops, object-oriented programming, and APIs.

What is the role of APIs in building AI products?

APIs, or Application Programming Interfaces, are used for interacting with other people's software, which is essential for using AI models that others have created.

What are some resources recommended in the video for learning the basics of Python and AI?

The video recommends resources like Brilliant for interactive learning, Free Code Camp for video tutorials, and 'Automate the Boring Stuff' for a text-based approach.

What is the importance of building projects while learning AI according to the video?

Building projects is important as it allows learners to apply their knowledge practically, which in turn helps in reinforcing learning and maintaining motivation to delve deeper into more advanced topics.

Outlines

00:00

🎓 Overcoming Boredom in Learning AI

The speaker addresses the common challenge of getting bored or stuck when learning AI, suggesting an alternative learning method called the 'Rengon' or 'Concentric Circle' method. This method involves starting with the basics of AI and then progressively expanding knowledge outwards, building small projects to maintain motivation. The speaker emphasizes the importance of learning how to use AI models with Python and provides a timeline for achieving basic proficiency, ranging from one month for beginners to a week or two for those with intermediate Python skills.

05:00

🤖 Introduction to Machine Learning and AI Models

This paragraph delves into the concept of machine learning, using a humorous 'hot dog or not' example to illustrate how computers learn from data. It explains the process of training a model with images to recognize patterns and make decisions. The speaker introduces different types of machine learning models, including CNNs for image recognition and large language models for text prediction. The paragraph also discusses the ease of using AI models for building personal projects, emphasizing the need to learn Python basics, APIs, and the fundamentals of large language models.

10:01

📚 Recommended Learning Resources for AI and Python

The speaker provides a list of recommended resources for learning Python, APIs, and large language models, including courses from Brilliant, Free Code Camp, and books like 'Automate the Boring Stuff'. The focus is on understanding how to interact with AI models using APIs. The paragraph also covers the importance of learning the basics of machine learning, statistics, and mathematics to build a solid foundation for more advanced learning. Resources like Brilliant's interactive courses and Josh Starmer's YouTube channel are suggested for making complex subjects more accessible.

🧠 Deep Dive into Neural Networks and Deep Learning

This paragraph explains the concept of neural networks, drawing an analogy with the human brain's neurons to describe how AI models learn from data. It discusses the process of stacking neural layers to create deep learning models capable of complex tasks. The speaker differentiates between various fields within AI, such as computer vision and natural language processing, and provides resources for further learning in these areas. The paragraph concludes with a recommendation to choose one learning resource and start building projects to apply the newly acquired knowledge.

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 video, AI is the central theme, with the speaker discussing methods to learn about it without getting bored or stuck. The script mentions building AI things like study tools or personal AI assistants, indicating AI's practical applications.

💡Machine Learning

Machine Learning is a subset of AI that allows computers to learn from data and improve at tasks over time without being explicitly programmed. The video explains it as computers making decisions by recognizing patterns in data. An example given is a 'hot dog, not hot dog' model, showcasing how machine learning can be used for image classification.

💡Deep Learning

Deep Learning is a branch of machine learning that uses neural networks with many layers (deep neural networks) to analyze and learn from large amounts of data. The script introduces deep learning as an advanced subfield of machine learning, mentioning models like the 'hot dog, not hot dog' model and large language models, which are capable of complex tasks in computer vision and natural language processing.

💡Neural Networks

Neural Networks are a set of algorithms designed to recognize patterns. They are inspired by the human brain and are the foundation of deep learning. The video script describes neural networks as modeled after our brains, with nodes representing neurons that create an artificial neural network capable of learning from data.

💡Convolutional Neural Network (CNN)

A CNN is a type of neural network used primarily in image recognition and computer vision tasks. In the script, the 'hot dog, not hot dog' model is an example of a CNN, which learns to distinguish between images of hot dogs and non-hot dogs by recognizing patterns and features.

💡Large Language Models

Large Language Models are AI models that are trained on vast amounts of text data and can generate human-like text. The video script mentions 'Chachi BT' as an example of a language model that predicts the next word in a sentence based on the context, showcasing the application of AI in natural language processing.

💡APIs (Application Programming Interfaces)

APIs are sets of rules and protocols that allow different software applications to communicate with each other. The video emphasizes the importance of understanding APIs for interacting with AI models, as they are the means to access and utilize AI models built by others.

💡Linear Algebra

Linear Algebra is a branch of mathematics that deals with linear equations and transformations using vectors and matrices. In the context of the video, linear algebra is part of the foundational mathematics needed to understand machine learning algorithms, as it is used for operations on data in neural networks.

💡Statistics

Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. The video mentions statistics as a fundamental field that is essential for machine learning, including concepts like hypothesis testing and probability, which are used to make inferences and predictions from data.

💡Python

Python is a high-level programming language known for its readability and versatility, making it a popular choice for AI and machine learning applications. The script recommends learning Python basics such as variables, data types, if statements, loops, and object-oriented programming, as they are necessary for building AI applications.

💡Brilliant

Brilliant is an online platform offering interactive courses in STEM subjects. In the video, it is mentioned as a resource for learning foundational subjects like math, statistics, and programming in a way that is engaging and suitable for those with short attention spans. The platform is highlighted for its ability to make complex subjects more accessible.

Highlights

Introduction of the Renon method or Concentric Circle method for learning AI without getting bored or stuck.

The importance of learning the basics of AI and how to use machine learning models with Python.

Building a simple AI project as early as possible to maintain motivation for learning.

Explanation of machine learning through the example of a hot dog classifier.

How machine learning models like CNNs learn to recognize patterns in data.

The process of training a machine learning model with various examples to improve its predictions.

Introduction to Chachi BT, a machine learning model for text data and predicting sentence structures.

The ease of using AI models to build personal AI products even with minimal coding experience.

Basic knowledge required for coding in Python, including variables, data types, and APIs.

Resources for learning Python and understanding APIs for AI model interaction.

The basics of large language models and their application in AI chatbots.

Course recommendations for prompt engineering and using AI models through APIs.

The necessity of understanding the fundamentals of machine learning before diving into algorithms.

Learning intermediate Python modules for data manipulation in the context of machine learning.

The role of math in machine learning and resources for overcoming the intimidation of mathematical concepts.

Statistics concepts important for machine learning and resources for learning them effectively.

Using AI models like Chat GPT as personal tutors to explain difficult concepts.

Deep dive into machine learning categories, algorithms, and the difference between supervised and unsupervised learning.

Introduction to artificial neural networks and their comparison to human brain neurons.

Exploration of deep learning, its layers, and specializations like computer vision and natural language processing.

Resources for diving deeper into subfields of AI like computer vision and natural language processing.

Advice on choosing one resource and building projects to consolidate learning rather than information overload.

Introduction to Brilliant as an interactive learning platform for STEM subjects, including AI and math.

Special offer for Brilliant's annual membership for the first 200 people using the provided link.

Transcripts

before anybody makes a comment yes I do

know that my hair is wet but I got to go

somewhere after this video is for my

short attention span friends who still

want to learn AI so usually when you're

trying to learn something new it look

something like a straight line first you

learn calculus linear algebra

probability statistics programming

machine learning deep learning Etc so

kind of like that progression don't get

me wrong you do need to learn these

things eventually but my problem is that

I can't even get past one of these

subjects without getting really bored

getting stuck and giving up hey no hate

these are amazing resources so what if

it's not the resources themselves that's

the problem but the way that we use them

is there a way that we can learn Ai and

not give up introducing the renon method

or if for some strange reason you don't

like Naruto the concentric Circle method

how does it work so in the middle of the

renegon you have the thing that you want

to learn which is AI we go from the

middle and we go outwards so for the

small circle we just need to learn the

basics of AI such as a high level of how

machine Learning Works how large

language models work but most

importantly how do you use these models

with python don't worry I'll go into a

lot more detail about exactly what you

need to learn and recommend some

resources later but the point is that

you learn just enough so that you're

able to build a really cool AI thing

like this study tool or personal AI

assistant as soon as possible like I'm

saying realistically 1 month if you have

zero coding experience and a week or two

if you have some intermediate experience

in Python and then after you do this we

take this excitement and satisfaction of

building this really cool thing and we

use that as motivation to go into the

next layer of the circle we dive a

little bit deeper into what exactly is

machine learning how does it work as

some of the math surrounding it which

would allow us to then build something

else that is really cool and then use

that as motivation to expand again into

the next level of the circle so you kind

of just repeat this cycle so that you're

learning more and more advanced things

and also getting to apply them until you

become quite Advent and be able to tr

truly understand AI models like how chat

GPT works and even build your

own so what is machine learning let's

start with a hot dog do pizza yes do

pizza that's that's it it only does hot

dogs no and a naha dog so that was an

example of machine learning machine

learning is a way for computers to learn

and make decisions by themselves by

studying a recognizing patterns in data

there are many different types of

machine learning models and this one

specifically is called a CNS a

convolutional neuron Network by the way

I might be throwing some terminology

here and there but don't worry about

remembering things and understanding I'm

just putting these here so as you're

learning you kind of go like oh like she

talked about this like I'm learning this

right now I'll be explaining more about

how these work later in the video as

well but first let's talk about how

Jimmy was able to build this hot dog not

hot dog model so first you have your

little baby model that has not seen the

world yet and you got to start feeding

it images about hot dogs but you also

have to show it pictures of not hot dogs

you also want to show it some tricky

cases like this dog that looks like a

hot dog and this hot dog sausage doesn't

have a bun I don't know if that's still

considered a hot dog is that actually a

hot dog though anyways you do this many

many times and it starts to learn what

is considered a hot dog and what is not

considered a hot dog or more

specifically what are the features that

make it more hot dog like and what are

the features that make it less likely to

be a hot dog for example if it sees this

cylindrical reddish thing it makes a

note that this is an increased

likelihood of that being a hot dog and

it sees this white stuff around this red

thing and again it will make a note that

there's an increase in a likelihood of

being this hot dog it will then come up

a score with its prediction of How

likely it is a hot dog but for example

if it sees this triangular looking thing

it goes like huh triangles are not hot

dog like so it decreases the likelihood

of that being a hot dog and so on and so

forth until it gets better and better at

predicting whether it's a hot dog or not

a hot dog now let's take a look at

Chachi BT over here which is also a

machine learning model except in this

case the data we're feeding it is a

bunch of text Data like the entire

internet's Text data and it uses his

data to predict the next words in a

sentence it's based upon its previous

words for example if you have the words

I am and the word sleeping it'll give a

likelihood of that being the next word

which is probably relatively high but

there can also be a word like potato

which probably has a pretty low

likelihood of being an next word so the

algorithm picks the word with the

highest probability and it somehow

magically is able to chain these

together to form coherent sentences

isn't that crazy like thinking about how

it actually works of course I'm

simplifying things a little bit here for

now though what's very exciting is that

you can actually use these AI models

pretty easy easily to start building

your own AI products say like this AI

personal assistant that's able to

schedule your life and stuff and by

easily I mean if you have zero knowledge

about coding it'll probably take you

about a month or if you have some

intermediate level of coding it'll take

you like less than a week or two what

you need to learn first is the basics of

python variables data types if

statements Loops objectoriented

programming and apis which stands for

application programming interfaces and

it's for interacting with other people's

software I'm also going to give you some

suggestions for resources brilliant has

a super beginner friendly course which

is super interactive which is great for

people with very short attention spans

cuz you can like you know do the little

dios and click things and things pop up

you can get started with brilliant for

free they also are the sponsor of

today's video If you prefer video

learning there is this really good

introduction to python from free code

camp and if you're into text or reading

textbooks so me personally I'm not that

into textbooks because it makes me

really bored easily but I have heard

that this book automate the boring stuff

is a really good introduction I want you

to especially focus on understanding

apis and how to use them because that's

how we're going to be able to use these

AI models that other people made next

we're going to learn the very Basics

about large language models which are

the AI models that power chat Bots like

chat gbt brilliant also has a crash

course on large language models which is

super beginner friendly like you don't

even need to know how to code but if

you're into videos this is a 1-hour

introduction to large language models by

Andre karthy who is an expert in this

field next up we're going to do this

course on prompt engineering for

developers this course is only an hour

long and is completely free from

deeplearning.ai but seriously this is

such a good course in starting to build

AI products using open AI apis it

teaches you prompt engineering en able

to interact with AI models and how to

connect and use the API to access the

models all right at this point you have

the basics of building AI products you

can use open AI apis in order to build

chat Bots and personal assistance you

can also generate images from Models

like do also link some more apis that

you can use to generate text to video

and other cool things you can do also

link some examples of projects that you

can build Link

description you now know how to use AI

models through apis but you still don't

really know how they work or how to make

your own to be able to do that it's kind

of like building the foundations of the

building you need to lay a very solid

foundation first by getting a better

understanding of machine learning but

before we can dive into the machine

learning algorithms themselves we still

need to take a step back and break it

down into its sub fields of fundamental

mathematics statistics and programming

specifically in Python what you need to

learn one at this intermediate level of

python you need to start learning more

modules that are related to data

manipulation because you need to use

data in order to teach your machine

learning model stuff so we need to learn

the modules of numpy pandas matpa live

for data visualization and pyit learn

for building machine learning models

there are so many great tutorials and

courses out there and I'll link them

below free code Camp is probably my

favorite resource and if you're into

books python for data analysis I've

heard is very good now math these scary

stuff A lot of people are intimidated by

math I am also intimidated by math math

so the good news is that you don't need

to learn that much of math you don't

need to sit there and learn how to do

like derivatives by hand you just need

to understand like the concept of

calculus the contract of what a matrix

is for linear algebra how to use

probability to determine the likelihood

of something that's about to happen

these are the foundations of machine

learning models for my short attention

span friends especially I feel like for

math math is like especially challenging

because it's it can be so boring

brilliant is nice and interactive and it

gives examples of things so I recommend

the coures calculus fundamentals

introduction to linear algebra and

introduction to probability you can also

take this math for ML specialization

free on corsera if you want to dive a

little bit deeper next up statistics you

got to know things like descriptive

statistics inferential statistics

hypothesis testing Central limit theorem

distributions confidence intervals it

sounds like a lot but it's pretty much

just first year statistics in college

again brilliant is how I personally

brushed up and learned more about

statistics but I also love supplementing

with my all-time favorite techdata

YouTuber Josh starmer He is very short

attention span friendly because how can

you possibly get bored of someone

singing about math don't be afraid of

neur networks they're not scary if you

want something more thorough there's a

Standford course on corsera called

introduction to statistics by the way a

pro tip especially for subjects like

math that are kind of like conceptual

and hard to understand using chat PT as

a personal tutor is literally a game

changer it can help explain difficult

Concepts and give analogies for things

uh where you can like use it to dive

deeper into stuff so I'm not going to go

into too much detail about how to do

that because I already made a video

which I'll link over here talking about

how to use CH PT as a learning tool

highly recommend all right all right now

we have truly laid a very solid

foundation and we can now dive into

machine learning yay machine learning as

a field is very very large and there's a

lot of different aspects of it so I only

want you to focus on understanding the

categories of different algorithms and

some of the example algorithms out there

stuff like regressions K means

clustering decision trees Etc and

understand the difference between

supervised and unsupervised learning

Josh starmer is absolutely my go-to for

machine learning content I give Josh

full credit for me actually graduating

my Master's Degree because I took with

this really hard machine learning course

and yeah like I would not have graduated

without him if you wanted something a

little bit more thorough there's also

the Stanford and deeplearning.ai course

called machine learning specialization

all right we' have expanded into the

next

[Music]

Circle neurons are cells in your brain

that form a network so that you're able

to think and do stuff now ai is modeled

after our brains we have these nodes

that represent neurons which create what

we call artificial neuron networks if

you feed these neuron networks data it's

able to start learning by itself kind of

like when a baby is first born it

doesn't really like have anything in its

brain but as it starts having more

experiences collecting more data it's

able to start learning by itself now if

you start stacking layers and layers of

these neurons together things start

getting really interesting and you can

create models that are capable of doing

incredible tasks this is called Deep

learning cuz you got a lot of layers

stack together and it's like very deep

it's an advanced subfield of machine

learning the hot dog no hot dog am model

is a model that uses deep learning

specifically in the field of computer

vision and the AI models that powers

chat Bots like chat gbt are called large

language models they also use deep

learning in the field of natural

language processing okay so at this

point we're another layer deep and

learning about deep learning deep

learning layer deep and learning about

specializations like computer vision and

large language models some recommended

resources Brilliance introduction in

neuron Network covers the basics and the

artificial neuron Network course goes

into deep learning again Josh sarmer is

just is the best and if you want to go a

little bit deeper there's a corsera

specialization in deep learning now at

this point you can also start branching

out into different sub Fields like for

example you're interested in hot dogs

and not hot dogs you can dive deeper

into computer vision and here's also a

free corsera specialization on it or if

you're interested in large language

models and things like that you can dive

deeper into natural language processing

here's another specialization of corsera

a final quick tip okay I do know I give

a lot of different resources here but

that's mostly just to give you guys a

variety based upon what kind of learning

style you have do not I repeat do not

try to go through all the different

resources and try to like learn every

single little thing and get like really

obsessed with everything just choose one

of these resources they're all amazing

go through it and then start building

your own projects you can build your own

neuro networks contribute towards open

source AI models and fine-tune other

people's models now I want to talk a

little bit more about the sponsor of

today's video brilliant thank you

brilliant I've already mentioned them a

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span friends because they're so

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teach these subjects I personally love

using brilliant whenever I want to learn

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so just you know having those like

little interactive things helps a lot in

my understanding they have Timeless

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all right that is the end of today's

video thank you guys all so much for

watching let me know in the comments if

you're now interested in learning Ai and

if you want me to make more videos

related to learning AI things I don't

know if you guys are into that okay

anyways have a wonderful day and I'll

see you guys in the next video or live

stream