How I’d learn ML in 2024 (if I could start over)

Summary

This 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

00:00

💻 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.

05:03

📚 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.

Keywords

💡Machine Learning

Machine Learning is a subset of artificial intelligence that enables systems to learn from and make decisions based on data. In the video's context, it is the central theme where the speaker shares their insights on learning this field. The script mentions steps to learn machine learning, including understanding its mathematical foundations and practical applications.

💡Python

Python is a high-level programming language widely used in machine learning for its simplicity and the powerful libraries it supports. The video emphasizes the importance of learning Python as a foundational skill for anyone starting in machine learning, as it is the language 'used by pretty much everyone' in the field.

💡List Comprehension

List comprehension in Python is a concise way to create lists based on existing lists. It is mentioned in the script as one of the Python concepts beginners should learn, indicating the level of familiarity with Python that is expected for machine learning.

💡Linear Algebra

Linear Algebra is a branch of mathematics that deals with linear equations and their representations through matrices. The video script highlights its importance in understanding the fundamentals needed for machine learning algorithms, such as how to compute the dot product between two matrices.

💡Probability Theory

Probability Theory is the study of random phenomena and is essential in machine learning for understanding the likelihood of different outcomes. The script suggests that knowing the basics of this theory is necessary to grasp various machine learning approaches.

💡Jupyter Notebooks

Jupyter Notebooks is an open-source web application that allows the creation and sharing of documents containing live code, equations, visualizations, and narrative text. The video mentions it as a basic tool for machine learning, useful for implementing and experimenting with Python code.

💡Pandas

Pandas is a Python library for data manipulation and analysis. It is highlighted in the script as a crucial tool for handling tabular data, which is common in machine learning, allowing for easy data manipulation and visualization.

💡Matplotlib

Matplotlib is a plotting library used for creating static, animated, and interactive visualizations in Python. The video script describes it as a tool for visualizing data and graphs, which aids in understanding the mathematical operations performed in machine learning.

💡Deep Learning

Deep Learning is a subset of machine learning that uses neural networks with many layers to learn complex patterns in large amounts of data. The script introduces it as an advanced topic after the basics of machine learning have been covered, with specific courses recommended for learning.

💡Kaggle

Kaggle is an online platform for data science and machine learning competitions. The video suggests participating in Kaggle challenges as a way to gain practical experience and learn by working on real-world machine learning problems.

💡Reimplementing Papers

Reimplementing papers refers to the process of taking a published research paper and replicating its results using code. The script positions this as a challenging yet highly educational project that can significantly enhance a machine learning practitioner's portfolio and understanding.

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