How I’d learn ML in 2024 (if I could start over)
Summary
TLDRThis video script offers a comprehensive guide for anyone aspiring to learn machine learning in 2024. It emphasizes starting with Python basics and understanding fundamental math concepts like calculus, linear algebra, and probability theory. The speaker, a former student and researcher with experience at top tech companies, recommends learning the ML developer stack, including tools like Jupyter notebooks and libraries such as pandas, numpy, and matplotlib. The script also suggests engaging with courses by Andrew Ng for machine learning and deep learning, using frameworks like PyTorch, and highlights the importance of practical experience through Kaggle challenges and reimplementing research papers to stand out in the field.
Takeaways
- 💻 Start with learning Python basics, as it is the foundation for machine learning and other steps.
- 🧠 Understand the fundamentals of calculus, linear algebra, and probability theory to grasp machine learning approaches.
- 📚 Utilize free resources for learning Python and math, such as online tutorials and courses.
- 🔍 Actively code along with tutorials to enhance learning and enjoyment of machine learning with Python.
- 📈 Learn about the ML developer stack, including tools like Jupyter notebooks and libraries like pandas, numpy, and matplotlib.
- 📊 Numpy is essential for matrix operations, matplotlib for data visualization, and pandas for data manipulation in tabular format.
- 🎓 Enroll in comprehensive machine learning courses like the Machine Learning Specialization by Andrew Ng for a solid understanding of ML concepts.
- 🤖 Dive into deep learning with the Deep Learning Specialization, which includes practical implementation and training of neural networks.
- 🏆 Engage in real-world projects by participating in Kaggle challenges to apply and deepen your understanding of ML.
- 📝 Reimplementing papers and recreating results is a challenging project that can significantly enhance your ML skills and application.
- 🌟 Stand out by working on projects that demonstrate your practical skills and understanding of machine learning concepts.
Q & A
What are the six key steps mentioned for learning machine learning in 2024?
-The script does not explicitly list the six steps but implies learning Python basics, understanding fundamental mathematics, getting familiar with the ML developer stack, learning about machine learning and deep learning, working on real projects, and standing out in the ML application process.
Why is Python the recommended programming language for machine learning?
-Python is recommended because it is the language used by most people working on machine learning, and all other steps in the learning process build on top of it.
What are some basic Python concepts beginners should learn according to the script?
-Beginners should learn about lists, dictionaries, if-else statements, for loops, list comprehensions, and class inheritance.
What is the importance of learning mathematics for machine learning?
-Mathematics is important because it provides the foundational understanding of calculus, linear algebra, and probability theory, which are essential for grasping any machine learning approach.
Can someone learn machine learning without understanding complex mathematics?
-While much of machine learning is automated, understanding the fundamentals of mathematics is necessary. However, complex mathematics is not required; high school or entry-level college math is sufficient.
What are some tools and libraries mentioned for the ML developer stack?
-Jupyter notebooks, pandas, numpy, and matplotlib are mentioned as essential tools and libraries for the ML developer stack.
What is the recommended course for learning machine learning according to the script?
-The Machine Learning specialization by Andrew Ng is recommended, as it covers classical ML concepts and introduces machine learning frameworks like scikit-learn and TensorFlow.
Who is Andrej Karpathy, and what is his contribution to learning neural networks?
-Andrej Karpathy is an AI researcher who has a series of videos implementing a simple neural network from scratch to a transformer model, covering the math of backpropagation, which is highly recommended for learning neural networks.
What is the Deep Learning specialization, and what does it focus on?
-The Deep Learning specialization is a course that focuses on implementing and training neural networks, including the use of the Hugging Face library for NLP.
What are the two types of projects recommended for hands-on learning in machine learning?
-The two types of projects recommended are working on challenges on Kaggle and reimplementing papers to recreate the results.
How can one stand out in their machine learning application process?
-One can stand out by working on real projects, such as Kaggle challenges and reimplementing papers, which demonstrate practical experience and understanding of ML concepts.
Outlines
💻 Essential Steps to Learn Machine Learning
The speaker, a student and researcher with experience interviewing at top tech companies, shares a six-step guide for learning machine learning from scratch. They emphasize starting with Python basics, understanding fundamental math concepts like calculus, linear algebra, and probability theory, and learning to use tools like Jupyter notebooks, pandas, numpy, and matplotlib. The speaker also suggests not going too in-depth initially to maintain a fun learning experience and mentions the importance of revisiting math concepts when needed.
📚 Advanced Learning and Practical Experience in Machine Learning
The speaker continues with advanced learning strategies, recommending courses like the Machine Learning Specialization by Andrew Ng and the Deep Learning Specialization for practical experience. They highlight the importance of understanding classical machine learning concepts and neural networks, mentioning Andrej Karpathy's neural network series. The speaker also discusses the value of working on real projects, such as Kaggle challenges and reimplementing papers, to gain hands-on experience and stand out in the field of machine learning.
Mindmap
Keywords
💡Machine Learning
💡Python
💡List Comprehension
💡Linear Algebra
💡Probability Theory
💡Jupyter Notebooks
💡Pandas
💡Matplotlib
💡Deep Learning
💡Kaggle
💡Reimplementing Papers
Highlights
To learn machine learning in 2024, you need a laptop and a list of steps to follow.
Begin with learning the basics of Python, which is essential for machine learning.
Python tutorials and courses are widely available online for beginners.
Understanding the fundamentals of calculus, linear algebra, and probability theory is necessary for grasping machine learning concepts.
High school or entry-level college math is sufficient for most machine learning needs.
Learn about the ML developer stack, including tools like Jupyter notebooks, and libraries such as pandas, numpy, and matplotlib.
Numpy is useful for mathematical operations with matrices or arrays.
Matplotlib is a tool for visualizing data and graphs, aiding in understanding mathematical operations.
Pandas is essential for handling data in tabular format, common in machine learning.
Learn about machine learning and deep learning through courses like the Machine Learning Specialization by Andrew Ng.
The course introduces machine learning frameworks like scikit-learn and TensorFlow.
Classical machine learning concepts taught in the beginner course are crucial for interviews.
After Andrew Ng's course, watch Andrej Karpathy's neural network series for a deeper understanding.
The Deep Learning Specialization focuses on implementing and training neural networks.
Hugging Face, a library for NLP, is included in the Deep Learning Specialization.
Work on real projects to solidify learning, starting with simpler challenges on Kaggle.
Reimplementing papers and recreating results is a challenging project that can help you stand out in the ML field.
There are additional techniques and tips for standing out during the learning process.
Transcripts
all you need to learn machine learning
in 2024 is a laptop and a list of the
steps you need to take I'm a student
researcher working for an ex meta
professor and have had interviews with
Google deepmind Amazon and other cool
companies but it took me over 3 years to
get to this point so today I will share
how I would learn machine learning if I
could start over by revealing the six
key steps you need to take let's get
going in general all these steps don't
have to be strictly completed in any
particular order but I would not start
with the final and arguably most
important step that said what I do
highly recommend is to start with
learning the basics of python python is
the programming language used by pretty
much everyone to work on machine
learning and every other step on this
list Builds on top of it this mainly
applies to beginners that don't know
what a list or a dictionary are and that
don't know how to write a simple if else
statement or a for loop I would even go
as far as saying you need to learn on
what a list comprehension and what class
inheritance are and honestly I don't
know what else to say than just type in
Python tutorial or course on YouTube or
Google and get started there's so much
amazing free content out there but you
should always keep in Minds to actively
code along the tutorial enjoy getting
into machine learning with python but
don't go too in depth this lets you
start with a fun experience because at
some point you will also have to learn
maths now you could argue that you don't
need mouths because so much is already
automated and taken care of by cool
python libraries which is true but you
will need to know all the fundamentals
of calculus linear algebra and
probability Theory to understand pretty
much any machine learning approach that
said you really don't need complex maths
most of the maths is high school or
entry-level College maths like you just
need to understand what the derivative
of a function is and how to compute it
you need to know what a matrix is is and
how the dot product works there again
are amazing resources out there that are
free like these courses right here or a
website called Can Academy I mean you
can even learn most of what you need on
brilliant.org sadly not sponsored or you
just go to college and take the
mandatory maths classes for any
engineering major I'll tell you about my
absolute favorite resource for learning
the fundamental Maths for new networks
after we cover the next important steps
this already shows you that you don't
need to hustle through all the courses
there are in the beginning in fact I
wouldn't even recommend that because it
can be very frustrating and just not fun
whenever you don't understand some maths
later on you can always revisit it by
just Googling learn the basics and then
continue on to the next fun step
learning about the ml developer stack so
now you know the basics of python and if
you haven't already can learn some basic
tools like Jupiter notebooks and
libraries like pandas numpy and matplot
lip numpy is a library for doing mouths
with matrices or arrays it's a great
starting point because you can now
implement the mouths that you just
learned about and see how simple it is
to compute a DOT product between two
matrices met plot lip is a tool for
visualizing data and graphs and just
seeing what maths you are doing and in
my opinion at least visualizing stuff is
fun and just very useful finally pandas
is a great tool for dealing with data
that is in tabular format a lot of
machine learning problems deal with
tabular data and pandas lets you again
very easily manipulate those and
visualize the tables all those libraries
also work very well with jupyter
notebooks and are an essential part of
learning machine learning as you will
see by getting to know those Frameworks
you will automatically improve your
overall practical Python and ml skills
but again only focus on the basics by
following a few tutorials later when
working on projects you'll really get to
know the libraries so now let's get back
to some Theory and finally actually
learn about machine learning and deep
learning okay up until now everything
should honestly not take too long
perhaps a few weeks depending on how
much time you put in and at what level
you already are but the ml courses I
will now recommend do take some time the
best and probably most famous machine
learning course or collection of courses
is the machine learning specialization
by Andrew Nung the cool thing is that
you here already get to know some
machine learning Frameworks like psyched
learn and tensorflow well I do have to
admit I personally prefer and would
recommend py Toge but learning one
framework pretty much lets you already
quickly adapt to the other one this
course is absolute gold and it's free
although this is the beginner course it
is still very important they here teach
a lot of classical ml Concepts and those
are the things you need to be able to
answer quite easily in ml interviews now
remember when I teased my favorite
resource for learning maths in neural
networks well after learning about those
in Andrew's course I would watch Andre
kathi's neural network series he here
implements a simple NLP model from the
ground up and goes all the way up to a
transform model he also goes through all
the mths of back propagation and so on I
cannot recommend this series enough
since in Andrew NS and Andre kath's
courses you already get some practical
experience with the taught ml Concepts I
would then continue on to the next more
advanced and practical course the Deep
learning specialization this course
focuses more on implementing and
training new Nets and the absolutely
amazing thing here is that they also
include hugging face which is a library
that you pretty much cannot avoid it's
really amazing and if you feel like this
course doesn't teach you enough about
hugging face you can also just go
through the hugging face NLP course
directly there you also learn even more
advanced concepts in NLP well if you are
interested in NLP that is so yeah those
are the two or perhaps even three
courses I would take and recommend by
now you have learned a lot and worked on
several smaller projects or rather
tutorials now it's time to actually get
your hands dirty and work on real
projects I honestly think you here learn
the most and there are two things I
would work on first I would go to kagle
and just work on challenges there are
many available for any level try not to
underestimate the complexity and start
with simpler challenges so you don't get
frustrated and demotivated and if you do
take on more difficult ones that also
come with prize money don't expect to
win one it's really difficult to get to
that point and you also need a lot of
compute so okay after working on kegle
challenges comes my final and favorite
type of project to work on
reimplementing a paper and recreating
the results this is challenging and you
will learn a lot and most importantly
this type of project will definitely
help you stand out on your ml
application that said there are a few
other simpler ways to stand out that you
can already get started during your
learning process so I'm sure you might
want to watch this video right here
where I reveal those techniques and tips
bye-bye
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