How I'd Learn NLP in 2024 (If I Had to Start Over)

Summary

This 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

00:00

📘 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.

05:02

🔧 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.

10:04

📚 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.

Keywords

💡Natural Language Processing (NLP)

Natural Language Processing refers to the field of computer science that focuses on the interaction between computers and human language. It is the core theme of the video, which aims to guide viewers on how to learn and keep up with advances in this area. The script discusses various steps and resources for learning NLP, emphasizing its importance in understanding and generating human language in a way that computers can process and analyze.

💡Linguistic Theories

Linguistic theories are frameworks that explain the structure and function of language. The video recommends the 'Cambridge Handbook of Linguistic Theories' as a resource for understanding the nuances of language, which is fundamental to mastering NLP. These theories help in grasping how language operates, which is crucial for developing algorithms that can effectively process and understand human language.

💡Python

Python is a high-level programming language recommended in the script for beginners learning NLP. It is known for its readability and extensive library support, which makes it an ideal choice for programming in the field of NLP. The script suggests starting with Python and then moving on to libraries like NumPy, pandas, and NLTK, which are essential for building NLP solutions.

💡Machine Learning

Machine Learning is an application of artificial intelligence that provides systems the ability to learn and improve from experience without being explicitly programmed. The script mentions that understanding machine learning is fundamental to learning NLP, as many NLP tasks, such as sentiment analysis or topic modeling, are approached using machine learning algorithms.

💡Deep Learning

Deep Learning is a subset of machine learning that uses neural networks with many layers (deep neural networks) to model and understand complex patterns in data. The video emphasizes the importance of understanding deep learning for advancing in NLP, as many state-of-the-art NLP models, like those mentioned in the script, are based on deep learning architectures.

💡Transformer Architecture

The Transformer architecture is a type of deep learning model that has gained significant attention in the field of NLP due to its effectiveness in handling sequential data. The script discusses the importance of understanding the Transformer model, including concepts like self-attention and positional encoding, which are critical for building and fine-tuning advanced NLP models.

💡Attention Mechanism

The attention mechanism is a technique used in neural networks to help the model focus on different parts of the input data to various degrees. In the context of the video, the attention mechanism is a key concept within the Transformer architecture, allowing models like GPT and BERT to process information in a way that captures the context of words in a sentence.

💡Positional Encoding

Positional encoding is a method used in the Transformer model to provide information about the relative or absolute position of the tokens in the sequence. The script mentions positional encoding as a critical component of the Transformer architecture, which helps models understand the order of words in a sentence, despite the model's inherent inability to capture order due to its self-attention mechanism.

💡Kaggle

Kaggle is an online community for data scientists and machine learners to share data, solve complex problems, and learn from each other. The video script suggests using Kaggle as a platform for practicing NLP skills by working on datasets and problem statements, which is an excellent way to apply and reinforce the knowledge gained from learning theoretical concepts.

💡Newsletters

Newsletters are periodic emails that provide updates and news on specific topics. In the script, subscribing to NLP-related newsletters is recommended as a way to stay current with the latest developments in the field. This is important for learners to keep their knowledge up-to-date and to understand the evolving landscape of NLP technologies and models.

💡End-to-End Solutions

End-to-end solutions refer to complete systems or processes that handle a task from beginning to end without requiring additional integration or intervention. In the context of the video, building end-to-end NLP solutions is a way for learners to apply their knowledge to real-world problems, creating systems that can process natural language data and generate meaningful outputs.

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

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oh