How To Self Study AI FAST
Summary
TLDRThis 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
- π The Renon Method or Concentric Circle Method is introduced as an alternative learning approach for AI to avoid getting stuck or bored.
- π― 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.
- π Aim to build a simple AI project, like a study tool or personal assistant, as soon as possible to maintain motivation and interest.
- π After completing a basic project, use the excitement as motivation to delve deeper into the subject, expanding knowledge layer by layer.
- π Machine learning is illustrated with the 'hot dog, not hot dog' model, showing how computers learn to recognize patterns in data.
- π€ Large language models, like the ones powering chatbots, are explained as systems that predict the next word in a sentence based on previous words.
- π οΈ For beginners, learning Python basics, APIs, and understanding of large language models are essential to start building AI products.
- π Intermediate learners should focus on Python modules for data manipulation, and foundational mathematics including calculus, linear algebra, and probability.
- π Statistics knowledge is vital, including understanding descriptive and inferential statistics, hypothesis testing, and distributions.
- π§ Deep learning is an advanced subfield of machine learning that involves stacking layers of artificial neurons to perform complex tasks.
- π 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
π 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.
π€ 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.
π 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.
π§ 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.
Mindmap
Keywords
π‘AI (Artificial Intelligence)
π‘Machine Learning
π‘Deep Learning
π‘Neural Networks
π‘Convolutional Neural Network (CNN)
π‘Large Language Models
π‘APIs (Application Programming Interfaces)
π‘Linear Algebra
π‘Statistics
π‘Python
π‘Brilliant
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
few times especially for short attention
span friends because they're so
interactive brilliant actually only
specializ izes and stem subjects so that
they're able to make the best courses to
teach these subjects I personally love
using brilliant whenever I want to learn
new things and brush up on different
skills especially the math and stats
part I get so bored when I try to just
like watch a video or do some courses um
so just you know having those like
little interactive things helps a lot in
my understanding they have Timeless
course offerings like math and stats
programming and python as well as new
course offerings like the neuron
networks courses for the Deep learning
and introduction to large language
models you can join a millions of people
already learning on brilliant I head on
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go through my link the first 200 people
will get 20% off in annual membership
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
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