Don't Learn Machine Learning, Instead learn this!
Summary
TLDRDeep Chen discusses the challenges of pursuing machine learning in 2024, highlighting its complexity and the need for a solid foundation in math, statistics, and programming. He notes the current job market's preference for experienced individuals over freshers due to the economic slowdown. Chen suggests that generative AI, which focuses on prompt engineering with large language models (LLMs), is a more accessible field for freshers. He advises learning backend and frontend development to complement basic machine learning knowledge for roles in generative AI, positioning oneself as an AI/ML engineer. For those not interested in full-stack development, alternative careers in data science are recommended.
Takeaways
- 🧠 Machine Learning is complex and requires a solid foundation in mathematics, statistics, and programming.
- 📚 Learning ML is not a quick process; it demands years of practice and deep understanding of algorithms.
- 🌐 The job market for ML and data science roles has tightened due to economic slowdown, favoring experienced candidates.
- 🔍 Despite learning ML, freshers often lack the industry experience compared to those with 2-3 years of experience.
- 💼 Companies are increasingly seeking experienced individuals for ML and data science roles, making it difficult for freshers to break in.
- 🚀 The market is shifting towards Generative AI, which is less about deep ML knowledge and more about prompt engineering with LLMs.
- 💼 For those with ML experience, transitioning to Generative AI roles might be easier, as it requires less in-depth ML knowledge.
- 🛠️ If aiming for Generative AI roles, having a basic understanding of ML and DL, along with backend and frontend development skills, is beneficial.
- 🎓 For those deeply interested in ML research, pursuing higher education like a master's or PhD is recommended.
- 🔄 Other career options in the data science industry for freshers include data engineering, MLOps engineering, AI product management, and data analysis.
Q & A
Why does the speaker suggest that machine learning might not be a good choice for some people in 2024?
-The speaker suggests that machine learning might not be a good choice due to its complexity, the need for a solid foundation in mathematics, statistics, and programming, and the current economic slowdown that has led to fewer job opportunities for freshers in the field.
What are the challenges faced by beginners when learning machine learning according to the script?
-Beginners face challenges such as the difficulty of learning the subject, the need for a solid foundation in related fields, and the complexity of topics that can be overwhelming, leading many to give up.
How has the job market for machine learning and data science roles changed according to the speaker?
-The job market has become more competitive and less welcoming to freshers due to the economic slowdown, with companies preferring experienced candidates and a shift towards roles that involve generative AI.
What is the role of generative AI in the current AI job market as per the speaker?
-Generative AI is becoming a more prominent role in the AI job market, as it involves working with large language models (LLMs) and prompt engineering, which is less technically demanding compared to traditional machine learning roles.
Why does the speaker recommend generative AI roles for freshers over traditional machine learning roles?
-The speaker recommends generative AI roles because they are less demanding in terms of deep machine learning knowledge, focus more on prompt engineering and API usage, and are in higher demand due to the current market shift towards AI integration.
What advice does the speaker give to those who have learned machine learning and deep learning but are not sure about their career path?
-The speaker advises those with knowledge in machine learning and deep learning to consider roles in generative AI, which may require some machine learning background but also involve full-stack development skills.
What are some alternative career paths in the AI field that the speaker suggests for freshers?
-The speaker suggests alternative careers such as data engineer, MLOps engineer, AI product manager, or data analyst, which may not require as deep machine learning expertise as traditional machine learning roles.
What skills does the speaker recommend learning for someone interested in generative AI roles?
-For generative AI roles, the speaker recommends learning backend development with frameworks like Django, FastAPI, or Flask for Python, and front-end development with HTML, CSS, and frameworks like React, Angular, or Vue.js.
How does the speaker describe the future of AI products in relation to generative AI?
-The speaker describes the future of AI products as being heavily based on generative AI, with many apps integrating chatbots and companies automating tasks using AI, indicating a growing demand for generative AI skills.
What does the speaker suggest for those who are passionate about deep machine learning concepts and research?
-For those passionate about deep machine learning and research, the speaker suggests pursuing higher education like a master's or a PhD to contribute significantly to the industry.
Outlines
🤖 The Complexity of Machine Learning in 2024
Deep Chen discusses the challenges of learning machine learning in 2024, particularly for freshers. He highlights the complexity of the subject, which requires a solid foundation in mathematics, statistics, and programming. The video emphasizes that mastering machine learning is not a quick process and that the job market has shifted, making it difficult for beginners to find roles in machine learning and data science due to an economic slowdown and a preference for experienced professionals. The speaker suggests that generative AI, which involves prompt engineering with large language models (LLMs), is a more accessible field for newcomers, as it does not require in-depth machine learning expertise.
🎓 Alternative Career Paths in AI for Fresh Graduates
The speaker advises fresh graduates to consider generative AI roles over traditional machine learning or data science positions due to the current market trends. They recommend learning backend development, such as Django, FastAPI, Flask for Python, or Express JS for JavaScript, and front-end technologies like HTML, CSS, and frameworks like React, Angular, or Vue.js. This skill set can lead to roles as an AI/ML engineer, which may still involve some machine learning tasks but are less demanding than traditional data science roles. For those not interested in full-stack development, alternative careers like data engineer, MLOps engineer, AI product manager, or data analyst are suggested. The speaker also offers to provide a full roadmap for generative AI in the comments section and encourages viewers to engage with the content.
Mindmap
Keywords
💡Machine Learning
💡Complexity
💡Economic Slowdown
💡Generative AI
💡Prompt Engineering
💡Full Stack Development
💡Data Science
💡Deep Learning
💡Frameworks
💡AI Product Manager
Highlights
Machine learning in 2024 is not a good choice for freshers due to its complexity and the current job market.
Machine learning requires a strong foundation in mathematics, statistics, and programming, making it difficult for beginners to master quickly.
Many learners give up on machine learning because of the overwhelming complexity of its topics.
In 2024, companies are hiring fewer freshers for machine learning and data science roles due to an economic slowdown and preference for experienced candidates.
Even internships in machine learning and data science don't guarantee job opportunities in the current market.
The job market has shifted away from pure machine learning and data science roles towards generative AI, especially for freshers.
Generative AI roles are less technically demanding than machine learning, focusing more on working with large language models (LLMs) and prompt engineering.
Big tech companies are leading the LLM race, making it easier for smaller companies to build AI products using LLMs.
Freshers can pivot to roles in generative AI by learning full-stack development and integrating LLM APIs.
Generative AI positions often require knowledge of backend technologies like Django, FastAPI, or Express.js, and frontend development skills using React, Angular, or Vue.js.
If a fresher prefers not to pursue full-stack development, they can explore alternative AI roles like data engineer, MLOps engineer, AI product manager, or data analyst.
Generative AI is becoming the dominant field in AI, with increasing demand for professionals to build and integrate chatbots and automation tools.
For freshers wanting to stay in AI without deep machine learning expertise, generative AI is the most accessible and future-proof career path.
A research-focused career in machine learning might be better suited for those who are deeply interested in complex algorithms and can pursue advanced degrees like a master's or PhD.
The speaker emphasizes that the future of AI will revolve around generative AI products, and freshers should align their skillsets to meet this demand.
Transcripts
hey there my name is deep Chen and I
will be explaining to you why machine
learning is not really a good choice in
2024 for you so in the world of AI and
machine learning you might be thinking
why would I suggest you to not learn
machine learning so this video will be
completely about that only especially if
you are a fresher you might want to see
this video completely so let's talk
about the complexity of learning machine
learning you might have taken an online
course whether it's free or paid in
machine learning but you must have
observed that it is a bit difficult and
and a way different than regular
software engineering Concepts now
machine learning is something that you
cannot be learning overnight or just
over a course you need a very solid
foundation in mathematics statistics and
programming on top of that there are a
lot of complex topics that you might
feel overwhelmed as a fresher or as a
beginner many Learners especially who
are just starting out learning machine
learning easily give up on these topics
to truly Master machine learning you
needs years and years of practice and
deep understanding of Concepts behind
every algorithm that is there in the
existence now times have changed now it
is not anymore like 2020 or 2022 where
machine learning and pure data science
was very demanding let's consider even
if you learn machine learning completely
as a fresher it's really tough to break
into the job market today due to
economic slowdown companies have really
stopped hiring freshes for machine
learning data science and all the
relevant roles to that the reason behind
this is machine learning takes a lot of
time to learn
understand and get used to all the
things that are there in it as a fresher
you might not be exposed to such level
of experience when you compare someone
to whose experienced like 2 or 3 years
most of them who are stayed out of
college do not have industry experience
so there you see that is the problem
behind not hiring freshes for ML and
data science roles if at all if you have
done internships in machine learning
data science or deep learning it is
still difficult to break into such
Market because companies are preferring
to hire only experienced people in this
so don't be worried let me tell you what
is going on in the market let's go back
to 2024 January when I was in my final
year and I started job hunting I was in
the thought that I would land up
somewhere like data science or machine
learning engineer role but it definitely
did not happen now when I started
applying jobs from January I just
started to see that from February March
April gradually I could see that the
machine learning and data science which
a pure roles had started to disappear or
maybe it's not meant for freshers now
instead it is diverting to a role called
generative AI now if you have machine
learning and deep learning experience
it's good but if you don't have you need
not worry about that because generative
AI Deals Only with llms and prompt
engineering there is no rocket science
behind it you just need to know how to
prompt the llm and get output from that
since we are in llm race most of the big
tech companies have started building
their own llms beating
each other every week or maybe every day
since these big tech companies are
competing and producing the best AI
models in the world it is really easy
for smaller startups or midsize
companies to build their own AI products
with that so that is where demand for
generative AI comes in every midsize or
Smalls size company needs some sort of
chat Bots or anything related to llm now
since you know a little bit of machine
learning and deep learning it's
definitely Advantage when you apply for
roles like generative AI but what you
don't know is you don't know to build
back end or front end so basically what
I'm telling you is to slightly switch
towards fullsack development to now if
you start applying for generative AI
roles as a fresher it might be not as
technically demanding as what you can
expect for machine learning or data
science in generative AI you definitely
need to low a little bit of machine
learning and deep learning but you might
not need it in very depth all you need
to do is call some API and do the
backend stuff that is required by the
website the mobile application so this
is the reality of market today now if
you want to successfully transition to a
generative AI role let me know in the
comment section if you need a full road
map for it but in this video I'll
explain briefly what to do now since you
have learned machine learning and deep
learning you might be knowing a little
bit of psychic learn and tensor flow and
py toou all of that apart from this you
need to learn backend if you're
interested in Python you can learn Jango
fast API or flask now if you're
interested in JavaScript you you can go
for Express JS now also you need to
learn how to build front ends for front
end you need HTML CSS and other
Frameworks like anything like react
angular or VJs in this way you will be
still relevant to working in a AI field
and you'll be termed as AIML engineer
and in case if they tell you to train
some machine learning model yes you can
do because you have a little bit of
experience training it already so this
comes in handy but still if you are very
interested in deep machine learning
Concepts and you want to do some
research work then research will be
really good for you I guess you should
take a masters and take a PhD and do
something great for the industry but if
you're just out of college and you want
job immediately then I would suggest
that you should go for generative AI
instead of fighting in the lane of data
science and machine learning engineer
role now let's say you don't want to go
towards full stack development too but
you want to stay in the data science
Industry there are so many options that
I can tell you you can switch to careers
like data engineer mlops engineer AI
product manager or data analyst so these
all roles don't require machine learning
or full stack development but my
suggestion for any fresher out there
straight out of college who's trying to
get into the field of AI I would
definitely recommend generative AI now
because the future is going to be only
based on generative AI products you can
see a lot of development around you
itself every other app is integrating a
chatbot or some companies are even
automating tasks using AI if you want a
full road map on generative AI let me
know in the comment section I can really
help you with that so hope you enjoyed
this video If you really liked it leave
a like And subscribe to my channel for
more such videos
[Music]
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