How to learn AI and get RICH in the AI revolution
Summary
TLDRThis video script offers a comprehensive guide to building AI tools like ChatGPT, emphasizing the importance of learning to create AI for job security and high earning potential. It outlines the foundational steps, including mastering Mathematics, Statistics, and Python programming, before delving into Machine Learning and Deep Learning. The script highlights key courses and resources, such as those by Dr. Andrew Ng, and encourages hands-on practice through platforms like Kaggle. The ultimate goal is to understand advanced AI systems, particularly those involving Natural Language Processing.
Takeaways
- 🤖 Learning to build AI tools like ChatGPT is a way to future-proof your career and may be one of the last jobs to be replaced by AI.
- 💰 AI engineers are highly paid, with OpenAI reportedly paying nearly 1 million dollars to its AI engineers.
- 🛠 To build AI tools, one must understand Artificial Neural Networks, which are interconnected nodes similar to human neurons.
- 📚 Deep Learning is a subset of Machine Learning, focusing on training neural networks with large datasets to make predictions.
- 🔢 Mathematics is foundational for Machine Learning, with Linear Algebra, Calculus, and Probability Theory being key areas to master.
- 📈 Statistics is essential for understanding Machine Learning algorithms, with concepts like Probability Distributions and Regression being important.
- 🐍 Programming, specifically in Python, is crucial for implementing Machine Learning algorithms and building AI tools.
- 🎓 Dr. Andrew Ng's courses on Coursera, such as 'Mathematics for Machine Learning and Data Science' and 'Machine Learning Specialization', are recommended for learning the necessary skills.
- 📝 Hands-on practice is vital, with platforms like Kaggle offering projects and competitions to build experience and a portfolio.
- 🌐 Deep Learning expands on Machine Learning with specialized courses on Neural Networks, Convolutional Neural Networks for Computer Vision, and Natural Language Processing.
- 🔑 The Transformer architecture, covered in the Deep Learning specialization, is key to understanding systems like ChatGPT used in Natural Language Processing.
Q & A
Why is learning to build AI tools like ChatGPT considered future-proofing one's career?
-Learning to build AI tools like ChatGPT is considered future-proofing because such skills are likely to be in demand as AI continues to advance and integrate into various industries. Moreover, the creators of AI tools are likely to be among the last to be replaced by AI themselves.
What is the average salary for AI engineers at OpenAI, according to the video?
-The video mentions that OpenAI pays almost 1 million dollars to its AI engineers, indicating that the field is financially rewarding for skilled professionals.
What are the three pillars of Machine Learning mentioned in the script?
-The three pillars of Machine Learning mentioned are Mathematics, Statistics, and Programming.
Which programming language is recommended for learning Machine Learning in the script?
-The script recommends learning Python for Machine Learning due to its popularity and wide usage in the field.
What is the role of Artificial Neural Networks in AI tools like ChatGPT?
-Artificial Neural Networks, which are interconnected nodes similar to neurons in the human brain, form the basis of AI tools like ChatGPT. They are trained on large datasets and used to make predictions, such as predicting the next word in a sentence for ChatGPT.
What is the relationship between Deep Learning and Machine Learning?
-Deep Learning is a subset of Machine Learning. It involves training neural networks with large amounts of data to make predictions or perform tasks, whereas Machine Learning is a broader field that includes various methods for machines to learn from data.
What are the core mathematical concepts needed to understand Machine Learning algorithms?
-The core mathematical concepts needed include Linear Algebra, Calculus, and Probability Theory, which are essential for understanding how different Machine Learning algorithms work.
Which courses are recommended for learning Mathematics for Machine Learning in the script?
-The script recommends the 'Mathematics for Machine Learning and Data Science' specialization on Coursera created by DeepLearning.ai, which includes courses on Linear Algebra, Calculus, and Probability.
What is the importance of Statistics in Machine Learning?
-Statistics is crucial for Machine Learning as it provides the foundational concepts needed to understand and implement various Machine Learning algorithms, such as Probability Distributions, Central Limit Theorem, Confidence Intervals, and Regression.
How does the script suggest one gets started with programming for Machine Learning?
-The script suggests starting with the basics of Python programming through hands-on exercises on learnpython.org, focusing on if statements, loops, functions, and classes.
What are the steps to build AI tools like ChatGPT using Neural Networks and Deep Learning?
-The steps include mastering Machine Learning first, which involves learning Mathematics, Statistics, and Programming, particularly Python. Then, one should study Neural Networks and Deep Learning through further specialization courses, such as those offered by Dr. Andrew Ng, focusing on advanced AI systems and Natural Language Processing.
Outlines
🤖 Building AI Tools for Job Security and High Earning Potential
The script emphasizes the importance of not only learning to use AI tools like ChatGPT for increased productivity but also building them to ensure job security in a future where AI might replace many roles. It suggests that building AI tools is one of the last jobs likely to be automated. The video promises a step-by-step guide to creating AI tools, particularly for those with some programming or math background looking to transition into AI. It introduces the concept of Artificial Neural Networks as the foundation of AI, explaining their role in Deep Learning and the process of training a network to predict outcomes. The script outlines the broader field of Machine Learning, of which Deep Learning is a part, and the necessity to understand it before delving into Neural Networks.
📚 Essential Steps to Master Machine Learning and Deep Learning
This paragraph outlines the three foundational pillars for mastering Machine Learning: Mathematics, Statistics, and Programming. It stresses the importance of a strong mathematical foundation, including Linear Algebra, Calculus, and Probability Theory, and recommends specific courses on Coursera for these subjects. The paragraph also touches on the vast field of Statistics, suggesting a breadth-first approach to learning its core concepts. For programming, Python is highlighted as the language of choice for Machine Learning, with a focus on basic programming skills rather than advanced proficiency. The summary also includes recommendations for learning Python and further Machine Learning through Prof. Andrew Ng's courses on Coursera and practical experience on Kaggle. It concludes with an introduction to Deep Learning, mentioning Dr. Ng's specialization and the importance of understanding Neural Networks, Convolutional Neural Networks, and Natural Language Processing to build advanced AI systems like ChatGPT.
Mindmap
Keywords
💡AI tools
💡Productivity
💡Artificial Intelligence (AI)
💡Artificial Neural Networks
💡Deep Learning
💡Machine Learning
💡Mathematics for Machine Learning
💡Statistics
💡Programming
💡Neural Networks
💡Natural Language Processing (NLP)
💡Data Science
Highlights
Learning to build AI tools like ChatGPT ensures job security in the face of AI advancements.
Building AI tools is one of the last jobs likely to be replaced by AI itself.
AI engineers at OpenAI can earn up to 1 million dollars, indicating high demand and value in the field.
The video provides a comprehensive guide for creating AI tools like ChatGPT.
Human Intelligence and AI both rely on the transmission of information through interconnected nodes.
Artificial Neural Networks are the foundation of AI tools like ChatGPT.
Deep Learning involves training neural networks with large datasets to make predictions.
ChatGPT's network predicts the next word in a sentence, showcasing the application of Deep Learning in language models.
Deep Learning is a subset of Machine Learning, which encompasses various learning methods for machines.
Mastering Machine Learning requires a strong foundation in Mathematics, Statistics, and Programming.
Mathematics for Machine Learning and Data Science on Coursera is recommended for learning the necessary math concepts.
Data Science Math Skills by Duke University offers a lighter approach to math for those already familiar with the concepts.
Statistics is crucial for understanding Machine Learning algorithms, with key concepts taught in Stanford's Introduction to Statistics.
Python is the programming language of choice for Machine Learning due to its popularity and ease of use.
Prof. Andrew Ng's Machine Learning Specialization on Coursera is a comprehensive resource for learning ML algorithms.
Kaggle offers hands-on practice and a platform to build a portfolio of Machine Learning projects.
Dr. Ng's Deep Learning Specialization furthers understanding of Neural Networks and advanced AI systems like ChatGPT.
The final course in the Deep Learning Specialization covers the Transformer architecture used in ChatGPT.
Completing the Deep Learning Specialization equips individuals with the knowledge for a successful AI career.
The journey to mastering AI tools is long but rewarding, offering insights into next-generation technology.
Transcripts
Learning to use AI tools like ChatGPT can make you more productive at your job. But,
learning to build AI tools like ChatGPT will make sure you have a job to be productive at.
Will AI take over your job? Well, I don’t know about that. But one thing I do know is that
building AI tools would be one of the last jobs AI can replace. So, if you want to be future proof,
you might want to invest some time into learning to build AI. And if that’s not
a good enough reason for you, you will be shocked to know that OpenAI, the company
who built ChatGPT pays almost 1 million dollars to its AI engineers. In this video, I will give
you a step by step guide on everything you need to learn to be able to create AI tools like ChatGPT.
This video is especially important for someone who already knows a little bit of programming
or Math and wants to transition into an AI related job. Let’s do this.
Human Intelligence comes from the transmission of information through Neurons. Neurons are
nothing but a bunch of interconnected nodes in our brain. In the same way,
Artificial Intelligence also comes from a network of interconnected nodes called
Artificial Neurons. And these networks are also called Artificial Neural Networks or
simply Neural Networks. To be able to build AI tools like ChatGPT, we need to learn how to build
these Neural Networks. But to get there, we will have to take many smaller steps.
You see, neural networks are part of this field called Deep Learning. In Deep Learning,
we take a neural network, train it by showing a lot of data and use the trained network to
predict something. What to predict can vary depending on the task you assigned to the
network when you trained it. In the case of ChatGPT, the network is actually predicting
the next word of a sentence. I know it doesn’t make sense right now, but stay with me until the
end and I’ll explain this in much more detail. Moving on, Deep Learning is a subset of this
field called Machine Learning in which machines acquire the ability to learn. In other words,
Deep Learning is not the only way a machine can learn to predict. There are many other
ways to do that and all of them combined are part of Machine Learning. To be able
to learn Neural Networks and Deep Learning, we need to master Machine Learning first.
But how do we do that? Well, Machine Learning has 3 pillars. Mathematics,
Statistics and Programming. Let’s tackle them one by one.
Let’s start with Mathematics because you will need good knowledge of Math to learn Statistics. Linear
Algebra, Calculus and Probability theory sit at the core of Machine Learning. And most of us
have been burnt by these either in high school or college. But the good news is that you need
these concepts only to learn how different Machine Learning algorithms work. Once you
have learnt that, you will just write programs that implement these algorithms for you. So,
I don’t believe that you have to be a Math genius to be able to do Machine Learning. To
learn Math for Machine Learning, I recommend this specialization called Mathematics for
Machine Learning and Data Science on Coursera. This course is created by DeepLearning.ai which
was founded by Dr. Andrew Ng who is arguably the most well known professor of Machine Learning.
Later in the video, we will go back to Prof. Ng when we want to study Machine Learning. Anyway,
this specialization consists of 3 courses. One for Linear Algebra, another for Calculus and the last
one for Probability. If you think all of this is too overwhelming for you, you can also check out
this Data Science Math Skills by Duke University. This course is not as comprehensive as my other
recommendation. If you are someone who doesn’t want to get too involved with the Math behind
Machine Learning or already knows the Math and just wants to brush up, this course is for you.
Now that you know Math, let’s move on to Statistics. Now, Statistics is a very vast
field and it requires many many years to fully understand it. But luckily for us,
we don’t need to know everything for Machine Learning. For Statistics,
we will use a Breadth First Approach for learning. We will learn some basic core concepts and then
build upon them as we encounter new Machine Learning algorithms later. To learn all the
key concepts that you actually need, I recommend this course called Introduction to Statistics by
Stanford University. This course covers all the important ideas like Probability Distributions,
Central Limit Theorem, Confidence Intervals and Regression etc. By the end of this course,
you would know all the Statistics you need to get started with Machine Learning.
Before we can finally do some Machine Learning, we need to learn some programming. To be more
specific, we need to learn programming in Python because it’s the most popular choice when it comes
to Machine Learning. Now, there is some talk in the town about this new programming language
called Mojo which is compatible with Python and is 35,000 times faster. But it’s still too early
to predict the future of Mojo. So, we are going to stick with the time tested Python for now. For
the purpose of Machine Learning, we don’t need advanced level programming skills. If you know
the basics like if statements, loops, functions and classes etc., you should be able to pick
Machine Learning easily. So, we are not going to build any crazy projects in Python at this step
and would focus on the basics. But we will build some Machine Learning projects using Python later
in the video. To learn these basics, simply go to learnpython.org and do some hands-on exercises.
At last, we have reached a place where we can start doing some Machine Learning. We are just
one step away from building tools like ChatGPT now. For Machine learning, we need to go back to
Prof. Andrew Ng. Check out his Machine Learning Specialization on Coursera. This specialization
is divided into 3 courses. One for supervised learning algorithms like Linear and Logistic
regression. Another one for unsupervised learning algorithms like Clustering. And
the last one for advanced algorithms that also introduces you to Neural Networks. If
you really want to have a deep understanding of ML, this is the best course out there. The
only caveat I would like to mention here is that the code samples and the Jupyter notebooks that
let you actually play with the code are not available for free with this course.
Once you are done with the course, head over to Kaggle and do some hands-on practice. On Kaggle
you can see the projects that other people have built. You follow along in the beginning and build
some confidence. When you are comfortable, you can participate in one of their competitions. This
will do 2 things. One, It will give you confidence that you can complete Data Science projects
independently. Two, you will build a portfolio of projects that you can write in your resume.
Now that you feel confident about your Machine Learning skills, let’s go back to our original
goal which was to build AI tools like ChatGPT using Neural Networks and Deep
Learning. Machine Learning specialization by Dr. Ng already gives you an introduction to Neural
networks. But it’s not comprehensive enough for you to be able to understand advanced AI
systems like ChatGPT. For that, you will have to develop your skills in Deep Learning. But
there’s no need to worry because Dr. Ng also offers a specialization in Deep Learning.
First 3 courses in this specialization cover basics of Deep Learning which is basically how
to train Neural Networks. In the fourth course, you will learn about Convolutional
Neural Networks which is basically Computer Vision. In Computer Vision, you will learn
how to train machines to recognize patterns in images which has applications in Autonomous
Driving and Face Recognition etc. But if your main goal is to understand how ChatGPT works,
that’s part of Natural Language Processing which is the last course in this specialization. This
course covers Transformer architecture which is what Chat GPT uses. By the end of
this specialization, you will know everything you need to have a successful career in AI.
I know that this path can seem very long to many people. But, that’s the cost you pay to work on
the next generation technology. Another path that is closely related to Machine Learning is
that of Data Science. In Data Science, you use data to develop insights but you don’t need to
be a Machine Learning expert. If you want to know the fastest way to learn Data Science,
watch this video. My name is Sahil and I will see you in the next one.
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