How I'd Learn AI in 2024(If I could start over)
Summary
TLDRThe 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
🚀 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.
📊 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.
🐍 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.
Mindmap
Keywords
💡Artificial Intelligence (AI)
💡Self-education
💡Machine Learning
💡Mathematics
💡Linear Algebra
💡Calculus
💡Probability
💡Python
💡Data Handling
💡Jupyter Notebook
💡Frameworks
💡Kaggle
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
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