How to learn AI and get RICH in the AI revolution

Summary

This 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

00:00

🤖 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.

05:02

📚 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.

Keywords

💡AI tools

AI tools refer to software applications that incorporate artificial intelligence to perform tasks such as language processing, image recognition, or data analysis. In the video, AI tools like ChatGPT are highlighted as essential for increasing productivity and job security, with building AI tools being a job that AI is less likely to replace.

💡Productivity

Productivity refers to the efficiency with which work is done, often measured by the output per unit of input. The script emphasizes that learning to use AI tools can enhance one's productivity at work, making it a valuable skill in the modern job market.

💡Artificial Intelligence (AI)

Artificial Intelligence, or AI, is the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The video discusses AI in the context of job security and the importance of understanding how to build AI systems like ChatGPT.

💡Artificial Neural Networks

Artificial Neural Networks, or ANNs, are a set of algorithms designed to recognize patterns. They are inspired by the human brain's neural networks and are fundamental to AI. The script explains that building AI tools like ChatGPT requires understanding these networks.

💡Deep Learning

Deep Learning is a subfield of machine learning that uses neural networks with many layers to learn and make decisions. It is part of the process of building advanced AI tools, and the script mentions it as a necessary step in learning to create AI systems.

💡Machine Learning

Machine Learning is a broader field where machines learn from data and improve their accuracy in predicting outcomes without being explicitly programmed. The video outlines the importance of mastering machine learning to understand and build neural networks.

💡Mathematics for Machine Learning

The script mentions that a good knowledge of mathematics, including linear algebra, calculus, and probability theory, is essential for understanding machine learning algorithms. It suggests specific courses for learning the necessary mathematical concepts.

💡Statistics

Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In the context of the video, statistics is one of the three pillars of machine learning, necessary for understanding patterns and making predictions.

💡Programming

Programming, specifically in Python as mentioned in the script, is a key skill for implementing machine learning algorithms. The video suggests that basic programming skills are sufficient to start with machine learning and that more complex projects will be built later.

💡Neural Networks

Neural Networks are a core concept in the video, described as interconnected nodes or 'artificial neurons' that mimic the human brain's structure. They are the building blocks of deep learning and are crucial for creating AI tools like ChatGPT.

💡Natural Language Processing (NLP)

Natural Language Processing is a field of AI that focuses on the interaction between computers and human languages. The script mentions NLP in the context of understanding how AI systems like ChatGPT, which predict the next word in a sentence, work.

💡Data Science

Data Science involves using data to develop insights and make decisions. While not the main focus of the video, it is mentioned as a related field to machine learning where one does not necessarily need to be an expert in machine learning algorithms.

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.