How I'd Learn NLP in 2024 (If I Had to Start Over)
Summary
TLDRThis video serves as a comprehensive guide for anyone keen on learning natural language processing (NLP) in 2024. It emphasizes the importance of understanding language fundamentals, acquiring programming skills with Python, and grasping the basics of machine learning and deep learning. The speaker recommends key resources, including 'Cambridge Handbook of Linguistic Theories' and Andrew NG's courses, and stresses the significance of the Transformer architecture and attention mechanisms in modern NLP models. The video also encourages viewers to engage in projects and stay updated with industry advancements through newsletters, positioning them as valuable NLP talents in the job market.
Takeaways
- 📚 Start with language fundamentals by reading books like the 'Cambridge Handbook of Linguistic Theories' to understand the nuances of natural language.
- 💻 Learn Python as a beginner-friendly programming language and progress to libraries like NumPy, pandas, NLTK, and advanced tools like TensorFlow and Hugging Face.
- 🎓 Take courses on machine learning basics and deep learning, with recommendations including Andrew NG's courses on Coursera and Michael Nielsen's book 'Neural Networks and Deep Learning'.
- 📈 Gain hands-on knowledge of NLP techniques and terminologies by studying resources like the book 'Speech and Language Processing' by Dan Jurafsky.
- 🧠 Understand the Transformer architecture and its components like self-attention, multi-head attention, positional encoding, and the differences between models like GPT and BERT.
- 🔍 Explore and practice NLP on platforms like Kaggle, which offer datasets and problem statements to apply your learnings.
- 🚀 Work on real-world projects to apply your NLP knowledge and create end-to-end solutions that can be showcased in your portfolio.
- 📰 Subscribe to newsletters to stay updated with the latest advancements and trends in the NLP field.
- 💼 Becoming well-versed in both the basics and the latest developments in NLP can make you a strong candidate for data science or machine learning engineering roles.
- 🤗 Sharing this knowledge with others who are interested in NLP can help build a community and enhance your own learning through teaching and collaboration.
Q & A
What is the first step recommended for learning natural language processing?
-The first step recommended for learning natural language processing is to learn about language fundamentals and how language operates. The Cambridge Handbook of Linguistic Theories is recommended for understanding the nuances of natural language.
Why is it important to have a good understanding of language before learning NLP?
-Having a good understanding of language is important because NLP is not just about feeding in numbers and getting responses; it involves understanding the subtleties of natural language, which is crucial for creating effective NLP solutions.
Which programming language is recommended to start with for learning NLP?
-Python is recommended as the programming language to start with for learning NLP due to its beginner-friendly nature and the availability of numerous libraries that are essential for NLP tasks.
What are some of the libraries and tools mentioned for learning NLP with Python?
-Some of the libraries and tools mentioned for learning NLP with Python include NumPy, pandas, NLTK (Natural Language Toolkit), spaCy, TensorFlow, and Hugging Face models.
What is the significance of learning machine learning and deep learning in the context of NLP?
-Learning machine learning and deep learning is significant in the context of NLP because most NLP models, including advanced ones like chatbots and language models, are based on these concepts. They form the foundation for understanding and implementing complex NLP systems.
Which course is recommended for learning the basics of machine learning for NLP?
-Andrew NG's machine learning introduction course on Coursera is recommended for learning the basics of machine learning for NLP. It's a free course that has been taken by millions of users.
What book is suggested for understanding the fundamentals of deep learning?
-For understanding the fundamentals of deep learning, the book 'Neural Networks and Deep Learning' by Michael Nielsen is suggested.
Why is the book 'Speech and Language Processing' by Dan Jurafsky recommended for NLP learners?
-The book 'Speech and Language Processing' by Dan Jurafsky is recommended because it covers the small integrities of NLP techniques and terminologies in a systematic approach, which is essential for understanding the building blocks of NLP solutions.
What is the importance of understanding the Transformer architecture in NLP?
-Understanding the Transformer architecture is important because it is the basis for most modern NLP models. It includes concepts like self-attention, multi-head attention, positional encoding, and the roles of encoder and decoder blocks, which are crucial for advancing in the field of NLP.
How does the speaker recommend staying updated with the latest advancements in NLP?
-The speaker recommends staying updated with the latest advancements in NLP by working on projects, subscribing to newsletters, and following authentic sources that provide updates on the field's progress.
What is the role of projects in learning and demonstrating NLP skills?
-Projects play a crucial role in learning and demonstrating NLP skills by allowing learners to apply their knowledge to real-world problems, create end-to-end solutions, and showcase their practical abilities, which can be beneficial for job applications or personal growth in the field.
Outlines
📘 Introduction to Learning NLP in 2024
This paragraph introduces the video's focus on structured learning for natural language processing (NLP) in 2024. It highlights the importance of understanding language fundamentals and recommends the 'Cambridge Handbook of Linguistic Theories' for a deep dive into language structure and nuances. The speaker emphasizes the need for a clear understanding of language components like adjectives, nouns, and phrases before delving into machine learning and NLP-specific libraries such as NLTK, SpaCy, TensorFlow, and Hugging Face models.
🔧 Tools and Techniques for NLP Mastery
The second paragraph emphasizes the importance of mastering Python as a programming language for beginners in NLP. It suggests starting with the basics of Python and then moving on to libraries like NumPy and pandas. The speaker also recommends learning about cutting-edge libraries and understanding the fundamentals of machine learning and deep learning through courses like Andrew NG's on Coursera or books like 'Neural Networks and Deep Learning' by Michael Nielsen. The paragraph underscores the necessity of hands-on knowledge in NLP techniques and terminologies.
📚 Deep Dive into NLP Techniques and Transformer Architecture
This paragraph delves into the specifics of NLP techniques and the Transformer architecture, which is foundational to many advanced models like chat GPT and Google Bard. The speaker recommends 'Speech and Language Processing' by Dan Jurafsky as a comprehensive resource for understanding part-of-speech tagging, sentiment analysis, and topic modeling. It also discusses the importance of understanding the Transformer's components, such as the encoder and decoder blocks, positional encoding, multi-head attention, and the differences between models like GPT and BERT.
🛠️ Building Projects and Staying Updated in NLP
The final paragraph encourages viewers to apply their NLP knowledge by building projects, using platforms like Kaggle for data sets and problem statements. It suggests finding practical applications of NLP in everyday work scenarios, such as summarizing documents or analyzing customer reviews for e-commerce products. The speaker also advises subscribing to newsletters to stay current with advancements in the field, ensuring that one remains a relevant and knowledgeable professional in the ever-evolving landscape of NLP.
Mindmap
Keywords
💡Natural Language Processing (NLP)
💡Linguistic Theories
💡Python
💡Machine Learning
💡Deep Learning
💡Transformer Architecture
💡Attention Mechanism
💡Positional Encoding
💡Kaggle
💡Newsletters
💡End-to-End Solutions
Highlights
Introduction to learning natural language processing (NLP) in 2024 with a focus on structured learning.
Importance of understanding language fundamentals in the context of NLP.
Recommendation of the 'Cambridge Handbook of linguistic theories' for language understanding.
The necessity of a clear understanding of language nuances for effective NLP solutions.
Learning a programming language, with a strong recommendation for Python due to its beginner-friendliness.
Importance of learning Python libraries such as NumPy, pandas, NLTK, and advanced libraries like TensorFlow and Hugging Face.
Recommendation of Andrew NG's machine learning course for foundational knowledge.
The significance of understanding machine learning and deep learning concepts as the basis for NLP models.
Suggestion to start with the book 'Neural Networks and Deep Learning' by Michael Nielsen for deep learning understanding.
The necessity of understanding NLP techniques and terminologies such as part-of-speech tagging, sentiment analysis, and topic modeling.
Recommendation of the book 'Speech and Language Processing' by Dan Jurafsky for comprehensive NLP knowledge.
The importance of understanding the Transformer architecture and its components like encoder, decoder, positional encoding, and attention mechanisms.
The significance of the attention mechanism in understanding and excelling in NLP.
The value of engaging in projects to apply and showcase NLP knowledge, with a mention of Kaggle as a resource.
The suggestion to discover real-life applications of NLP to create impactful solutions.
Recommendation to subscribe to newsletters for staying updated with the latest advancements in NLP.
The potential of becoming a relevant data scientist or machine learning engineer by following the recommended NLP learning path.
Encouragement to share the video with others interested in NLP and to subscribe for more content on related topics.
Transcripts
well if you've clicked on this video I'm
assuming you fascinated by the chat gpts
of the world the Google's Gemini model
as well as the open source llama 2
models if you're looking for a
structured way of learning natural
language processing in 2024 then this
video is ultimately for you in this
video I'll break down the entire steps
of how you can follow and learn natural
language processing and how you can keep
up with the advances that are happening
in this amazing field so without wasting
any further time let's Kickstart the
video and discuss more about how I would
have learned natural language processing
if I were starting this entire process
in 2024 let's
begin now given if I have to learn about
natural language processing the word
language stands out in every natural
language processing context that you can
think of right so the first and foremost
thing that you have to learn is language
so you have to learn about about
language fundamentals and how language
operates okay in order to understand
language you have multiple books out
there one of the books that I can
wholeheartedly recommend is the
Cambridge Handbook of linguistic
theories this book will take you through
the nties of what you require in terms
of understanding language so NLP is not
just NLP where you feed in some numbers
and you get a response you have to have
a clear understanding of the nuances of
natural language which is where this
book would come in handy again there are
multiple books out there you can refer
to any book that you like but before you
start learning natural language
processing the machine learning version
you also have to have like a good idea
of how language operates what are
different adjectives what are different
nouns how do you understand them how do
you join sentences together what are
different phrases and all of that so
which is where this particular book
comes in handy so in order to create
amazing natural language processing
based Solutions the first thing that you
have to do is learn language and how it
operates specifically this book is
designed for English there are other
books for other languages but your
starting point for learning natural
language processing should be
understanding language so this is my
first recommendation in terms of how you
can Kickstart your journey in natural
language
processing now that you have an idea in
terms of how you can start learning
about language the second piece of most
important thing in the entire puzzle is
to learn a good programming language
well there is a lot of debate in terms
of which language you should learn but I
would highly recommend that you start
learning python python is a very
beginner friendly language so if you
have very little programming experience
as well you can Kickstart your journey
with python then you can kind of take
many steps by learning numai pandas and
the other libraries that are there once
you're confident enough that you know
python decently well then you can start
learning about nltk the NLP library then
you can also start learning about Spacey
and the hugging face models if you have
to kind of integrate that into your end
to-end Solutions my second
recommendation would be that you start
with a good programming language that is
python start from the fundamentals then
go up a notch by learning about
different libraries and finally learn
about The Cutting Edge Library such as
tensor flow hugging face pyop and the
other libraries that that will help you
become better at natural language
processing so this is my second
recommendation that you should
definitely follow if you have to excel
in the field of natural language
processing the third recommendation that
I would have if you want to learn
natural language processing in 2024 is
that you should Kickstart your journey
by learning the fundamentals of machine
learning and deep learning there are
multiple courses out there which teach
you machine learning Basics one such
course that I can recommend
wholeheartedly is Andrew NG's machine
learning introduction it's a free course
that's available online on corsera I
think more than million users have
picked up this course and they've
started learning machine learning so
that is a good starting point that you
can utilize in order to Kickstart your
journey in this amazing world of natural
language processing with respect to deep
learning again Andrew NG has created an
amazing specialization around deep
learning so you can follow that as well
if you are more interested in something
that is more say written as compared to
video then there is an amazing book by
Michael neelen called as neural networks
and deep learning so you can definitely
Kickstart your journey using that book
as well that book is very well
structured in terms of understanding the
key fundamentals of deep learning so
these are my two recommendations which
are clubbed into uh one point which is
if you have to excel in the field of
natural language processing all of the
natural language processing models that
you can think of which is say chat GPT
uh and say Google bar and the other
models the fundamentals of all these
models are based on machine learning and
deep learning concept so in order to
excel in this amazing field of NLP you
require good Hands-On knowledge of
natural language processing so which is
where these recommendations would help
you get started in this amazing field of
natural language
processing
you are now well vered with language you
have solid backbone of python the next
thing that you've also done is you've
kind of understood about machine
learning and deep learning what's next
well you have to understand about NLP
techniques and
terminologies what is part of speech
which is pause what is Neer how do you
perform sentiment analysis how do you
perform topic modeling all of these are
small integrities that you should be
aware of before you start creating
amazing NLP based Solutions what do I
recommend here well there are tons of
resources that you can find but I'll
recommend a Bible to you a Bible that
every NLP practitioner kind of has gone
through once in their life the book is
speech and language processing by Dan
jski it's an amazing book that every
practitioner has gone through and this
is something that I can wholeheartedly
recommend the small integrities of how
NE fun functions how the entire PA
tagging system can be implemented all of
this is explained in a very very
systematic approach the attention
mechanism that has kind of blown up the
entire NLP Community all of that has
been also very well explained in the
latest version of this entire book so
this is my recommendation after you've
completed language fundamentals once you
well versed with python and once you
have good understanding of machine
learning and deep learning then start by
understanding small integrities of
natural language processing techniques
and this will help you kind of progress
in your journey ahead in the field of
natural language
processing chat GPT Google bard or any
other language model that you consider
95% of the models have been created
using one one
network the network that I'm referring
to is the Transformer
architecture inside the Transformer
architecture you have an encoder block
and you have a decoder Block in the
encoder you have different sections you
have different concepts such as
positional encoding how is that used in
the entire Transformer architecture what
is multihead attention what is self
attention how is the entire information
transferred from the encoding block to
the decoding section how is a GPT model
different from a Bert model all of these
are fine details that you should be
aware of if you want to excel in the
field of natural language processing
there are tons of tutorials out there
that you can kind of refer to so I don't
have like a preference in terms of one
tutorial I've kind of referred multiple
tutorials multiple blogs multiple
research papers in order to understand
the attention mechanism and the
Transformer Network so my recommendation
is if you ever apply for an NLP research
role or an NLP application role at any
company this will be a set of questions
that you will get in terms of
understanding the integrities of
language model then there are also
aspects of fine-tuning a large language
model which is something that you should
be aware of but at the very basic what
you should be aware of is the attention
mechanism be it self attention
multi-head attention then what is
positional encoding what are the encoder
blocks basically doing what is the
decoder doing what is teacher forcing
there are so many terms in the entire
Transformer Network that you should be
aware of a lot of candidates that I have
interviewed so far with respect to an
NLP role a lot of them have very
superficial knowledge in terms of
understanding all of these building
blocks which is where what I would urge
you to do is I would urge you to
understand the basics first and then
jump to complex large language models
such as Lama 2 or the others that are
there without understanding this you
wouldn't be able to appreciate the
amazing work that's been done by the
community so so far so this is my
recommendation start learning attention
so pay attention to attention to get a
lot of attention from the
interviewers now that you're well versed
with the Transformer architecture you
know a good amount of detail about how
self attention multi-head attention work
and all the integrities that follow with
say large language models it is time for
you to start picking up projects a good
website that you can use for your entire
say NLP journey is kaggle.com so kaggle
is that place where you get good
readable amount of data uh you get good
amount of problem statements on that
particular website and then you can
start practicing your NLP knowledge on
that particular data set if you're not
very heavy with respect to kaggle usage
then what you can do is you can start
discovering places where an NLP solution
can make an impact in your life try
discovering features where if you have
good amount of documents in your
workflow if you want to create a summary
of those documents can you use NLP in
that particular approach if you work for
an e-commerce company and if you are
part of the team which is collecting
good amount of reviews and ratings for
your products chances are that you can
use your NLP knowledge and you can
derive insights in terms of what people
are speaking about your products you can
also filter them out based on say
positive or negative reviews so there
are tons of things where you can use
your natural language processing skills
which is where my next recommendation is
start building projects based on what
you've learned and that can be shown in
terms of like a project that you've done
either in your organization or as a
part-time project that you can kind of
show in your resume so this is the other
recommendation that I have that once you
have acquired significant amount of
knowledge in this particular field start
utilizing your knowledge in creating end
to-end Solutions and then that can show
up in your resume as well the other
point that I can Club in the entire say
Activity of creating projects is
subscribe to newsletters say today if I
consider today's date that I'm recording
this video the most famous open-source
large language model is Lama 2 maybe six
or eight months down the line this
particular model may not be beating the
benchmarks there might be other better
models as well which is where you have
to be updated with respect to what is
currently happening in the industry
which is where subscribing to
newsletters subscribing to authentic
good newsletters is very very beneficial
I have subscribed to a lot of
newsletters and you to can kind of
discover the newsletters that you are
more inclined towards in terms of
understanding the Realms of how NLP is
progressing and with that updated
knowledge you can keep yourself updated
in terms of what's actually happening in
this entire space so this is my final
recommendation that is start working on
projects once your fundamental are clear
stay subscribed to newsletters that will
kind of give you the advances of how the
entire field is progressing with this
approach what you would become is you
would become a relevant data scientist
or a machine learning engineer who is
well aware of the basics plus he's also
aware of how the entire advances are
happening in this entire amazing field
so this in totality if you are able to
follow from scratch you would be really
good in the job market if you're
searching for a job if there are
companies that are looking out for good
exceptional NLP talent and if you
followed these set of approaches then
you are a bright candidate for getting
hired in those organizations be it
research based roles or be application
based roles so these are my
recommendations in terms of how you can
Kickstart your journey in natural
language processing in the year
2024 I hope you found this video
beneficial if if you have other friends
who are kind of wanting to break into
this entire field of natural language
processing please feel free to share
this video with all of them and if you
like the content that I create on my
channel it would be super motivating if
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you so much for watching this
video
oh
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