#3 Machine Learning Specialization [Course 1, Week 1, Lesson 2]

Summary

This video script introduces machine learning as the field that enables computers to learn without explicit programming, exemplified by Samuel's self-learning checkers program from the 1950s. It emphasizes the importance of applying machine learning algorithms effectively, with a focus on supervised and unsupervised learning as the main types. The script promises practical advice on developing valuable machine learning systems, aiming to equip viewers with the skills to build serious machine learning applications and avoid common pitfalls.

Takeaways

• 📚 Machine learning is defined as the study that enables computers to learn without being exclusively programmed, as attributed to author Samuel.
• 🎲 Samuel's checkers game program is highlighted as an early example of machine learning, where the computer learned from playing thousands of games against itself.
• 🤖 The importance of the number of games played is emphasized; the more games the algorithm learns from, the better its performance.
• 🧠 The script introduces quizzes to help viewers understand and practice the concepts of machine learning discussed in the video.
• 🔍 Two main types of machine learning are mentioned: supervised learning and unsupervised learning, with supervised learning being more commonly used in real-world applications.
• 🛠️ The class focuses not only on providing tools (learning algorithms) but also on teaching how to apply them effectively, which is considered equally, if not more, important.
• 🏗️ The script stresses the importance of knowing how to apply machine learning tools to avoid ineffective approaches and to build practical and valuable machine learning systems.
• 👨‍🏫 The instructor shares insights from visiting top tech companies, noting that even experienced teams can struggle with applying machine learning algorithms correctly.
• 🛑 The class aims to teach best practices for developing a practical machine learning system, to avoid common pitfalls and to increase the chances of success.
• 🚀 The goal of the class is to help learners become skilled machine learning engineers who can design and build serious machine learning systems.
• 🔜 The next video will delve deeper into supervised and unsupervised learning, explaining what they are and when to use each.

Q & A

What is the definition of machine learning according to the author Samuel?

Machine learning is defined by Samuel as the field of study that gives computers the ability to learn without being exclusively programmed.

What was the significance of Samuel's checkers game program in the 1950s?

Samuel's checkers game program was significant because it was programmed to play against itself, learning from its wins and losses, and eventually becoming a better player than Samuel himself.

How does a learning algorithm improve its performance in general?

A learning algorithm improves its performance by having more opportunities to learn, which allows it to refine its understanding and decision-making over time.

What is the purpose of the quiz questions in the video?

The purpose of the quiz questions is to help viewers practice and understand the concepts they are learning, rather than just testing their ability to answer questions correctly.

What are the two main types of machine learning mentioned in the script?

The two main types of machine learning mentioned are supervised learning and unsupervised learning.

Why is supervised learning more commonly used in real-world applications?

Supervised learning is more commonly used in real-world applications because it has seen the most rapid advancement and innovation, and is the focus of the first two courses in the specialization.

What is the importance of learning how to apply machine learning algorithms effectively?

Learning how to apply machine learning algorithms effectively is crucial because having the tools alone is not enough; one must also know how to use them to build practical and valuable machine learning systems.

What does the instructor emphasize about the application of machine learning algorithms?

The instructor emphasizes the importance of practical advice for applying machine learning algorithms, stating that knowing how to use these tools effectively is as important as having the tools themselves.

What is the goal of the specialization mentioned in the script?

The goal of the specialization is to provide not only the tools of machine learning but also the skills to apply them effectively, helping learners become skilled machine learning engineers capable of designing and building serious machine learning systems.

What is the focus of the third course in the specialization?

The third course in the specialization focuses on unsupervised learning, recommender systems, and reinforcement learning.

What is the potential issue with experienced machine learning teams not applying machine learning algorithms correctly?

The potential issue is that even experienced teams may spend significant time working on a problem without much success because they might be using the wrong approach or not applying the tools effectively.

Outlines

00:00

🤖 Introduction to Machine Learning

This paragraph introduces the concept of machine learning, providing a definition attributed to Samuel, who described it as the study that enables computers to learn without being exclusively programmed. The example of Samuel's checkers game program is given to illustrate how a computer can learn from playing thousands of games against itself, eventually surpassing the skill of its programmer. The paragraph also mentions the importance of quizzes for understanding and practicing concepts, and hints at the exploration of different machine learning algorithms in upcoming videos, focusing on supervised and unsupervised learning.

05:01

📚 Deep Dive into Supervised and Unsupervised Learning

The second paragraph delves into the types of machine learning, specifically supervised and unsupervised learning, and sets the stage for further exploration in subsequent videos. It emphasizes the prevalence of supervised learning in real-world applications and its rapid advancement. The paragraph also underscores the importance of practical advice for applying machine learning algorithms effectively, comparing it to having the right tools and knowing how to use them. The author shares insights from interactions with top tech companies, highlighting common pitfalls and the value of best practices in developing practical machine learning systems.

Keywords

💡Machine Learning

Machine Learning is a subset of artificial intelligence that provides systems the ability to learn from data, improving their accuracy and performance without being explicitly programmed. In the video, it is defined by Arthur Samuel as the field that enables computers to learn without being exclusively programmed. The theme revolves around understanding this concept and its applications, as exemplified by Samuel's checkers-playing program that learned from playing thousands of games against itself.

💡Arthur Samuel

Arthur Samuel was a pioneer in the field of computer gaming and artificial intelligence. He is known for his work on machine learning through his checkers-playing program in the 1950s. In the script, Samuel's definition of machine learning is highlighted, and his checkers program is used as an example of a machine learning system that improved its performance through self-play.

💡Checkers Program

The Checkers Program mentioned in the video is an early example of a machine learning system developed by Arthur Samuel. It learned to play checkers by playing numerous games against itself, analyzing which moves led to wins and losses. This program is a key example in the video that illustrates how machine learning can enable a computer to improve its performance over time through experience.

💡Learning Algorithm

A learning algorithm in the context of machine learning is a set of rules or procedures that enable a computer to learn from data. The video emphasizes the importance of learning algorithms, explaining how they can improve their performance given more opportunities to learn, as illustrated by the checkers program that played tens of thousands of games.

💡Supervised Learning

Supervised learning is a type of machine learning where an algorithm is trained on labeled data. The video mentions it as one of the two main types of machine learning, and it is highlighted as the most commonly used in real-world applications, with the first two courses of the specialization focusing on this type.

💡Unsupervised Learning

Unsupervised learning is another main type of machine learning where an algorithm learns patterns from unlabeled data. The video script introduces this concept as the focus of the third course in the specialization, contrasting it with supervised learning.

💡Recommender Systems

Recommender systems are a type of machine learning system that seeks to predict what products or services a user might like based on their past behavior. In the video, they are mentioned as one of the most used types of learning algorithms today, indicating their importance and prevalence in various applications.

💡Reinforcement Learning

Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward. Although not deeply explained in the script, it is mentioned as another area of focus in the specialization.

💡Practical Advice

The video emphasizes the importance of not only having the tools of machine learning but also knowing how to apply them effectively. Practical advice refers to the guidance provided throughout the video series on how to apply machine learning algorithms effectively in various scenarios.

💡Best Practices

Best practices in the context of the video refer to the recommended methods and techniques for developing and implementing machine learning systems. The script mentions that learning these best practices is crucial to avoid common pitfalls and to increase the likelihood of success when building machine learning systems.

💡Quiz Questions

Quiz questions are used in the video as a tool to help viewers understand and practice the concepts being taught. They serve as a way to engage the audience and reinforce their learning, as demonstrated when the video asks viewers to consider the impact of a computer playing fewer games on its learning performance.

Highlights

Machine learning is defined as the study that gives computers the ability to learn without being exclusively programmed.

Arthur Samuel, the author of the definition, wrote a checkers-playing program in the 1950s that learned from playing thousands of games against itself.

The checkers program improved by analyzing which board positions led to wins and losses, thus learning over time.

The importance of the number of games played for the learning algorithm's performance was discussed through a quiz.

Quizzes are used to practice concepts rather than to test knowledge, emphasizing understanding over correctness.

Two main types of machine learning are supervised learning and unsupervised learning.

Supervised learning is the most commonly used in real-world applications and has seen the most advancement.

The specialization focuses on supervised learning in the first two courses and unsupervised learning in the third.

Supervised learning algorithms are the most used types today, along with unsupervised learning and recommender systems.

Practical advice for applying machine learning algorithms is a key focus of the class.

The class emphasizes the importance of knowing how to apply machine learning tools effectively.

The instructor shares insights from top tech companies about the practical application of machine learning algorithms.

The class aims to teach best practices for developing a practical and valuable machine learning system.

Students will learn how skilled machine learning engineers build systems to avoid common pitfalls.

The goal is for students to become experts in designing and building serious machine learning systems.

The next video will delve deeper into supervised and unsupervised learning and their practical applications.

Transcripts

so what is machine learning in this

video you learn the definition of what

it is and also get a sense of when you

might want to apply it let's take a look

together

here's the definition of what is machine

learning that is attributed to author

Samuel he defined machine learning as

the few the study that gives computers

the ability to learn without being

exclusively programmed

Samus claim to fame was that back in the

1950s he wrote the checkers flame

program and the amazing thing about this

program was that author Samuel himself

wasn't a very good Checkers player

what he did was he had programmed the

computer to play Maybe tens of thousands

of games against herself and by watching

what source of board positions tended to

lead to wins and what position is tend

to delete the losses the checkers flame

program learns over time what a good or

bad old positions by trying to get to

goods and avoid bad positions his

program learned to get better and better

at playing checkers

because the computer had the patience to

play tens of thousands of games against

itself it was able to get so much

Checkers playing experience that

eventually it became a better Checkers

player than author Samuel himself

now throughout these videos besides me

trying to talk about stuff I'll

occasionally ask you a question to help

make sure you understand the content

here's one about what happens if the

computer had played far fewer games

please take a look and pick whichever

you think is a better answer

thanks for looking at the quiz

and so if you have selected this answer

would have made it worse then you got it

right

in general the more opportunities you

give a learning algorithm to learn the

better it will perform if you didn't

select the correct answer the first time

that's totally okay too the point of

these quiz questions isn't to see if you

can get them all correct in the first

try these questions are here just to

help you practice the concepts you're

learning

author Samuel's definition was

surrounded in formal one but in the next

two videos we'll dive deeper together

into one of the major types of machine

learning algorithms

in this class you learn about many

different learning algorithms the two

main types of machine learning are

supervised learning and unsupervised

learning we'll Define what these terms

mean more in the next couple videos

of these two

supervised learning is the type of

machine learning that is used most in

many real world applications and that

has seen the most rapid advancement and

innovation

in this specialization which has three

causes in total the first and second

causes will focus on supervised learning

and the third will focus on unsupervised

learning recommender systems and

reinforcement learning

by far that most used types of learning

algorithms today are supervised learning

unsupervised learning and recommend

those systems

the other thing we're going to spend a

lot of time on in this specialization is

practical advice for applying learning

algorithms this is something I feel

pretty strongly about teaching about

learning algorithms is like giving

someone a set of tools and equally

important so even more importance than

making sure you have great tools is

making sure you know how to apply them

because you know what good is it if

someone were to give you a Steelyard

hammer or a state of the art hanger and

say good luck now you have all the tools

you need to build a three-story house it

doesn't really work like that and so too

in machine learning making sure you have

the tools is really important and so is

making sure that you know how to apply

the tools of machine learning

effectively so that's what you get in

this class the tools as well as the

skills with applying them effectively

I regularly visit with friends and teams

in some of the top tech companies and

even today I see experienced machine

learning teams apply machine learning

algorithms to some problems and

sometimes they've been going at it for

six months without much success and when

I look at what they're doing I sometimes

feel like I could have told them six

months ago that the current approach

won't work and there's a different way

of using these tools that will give them

a much better chance of success

so in this class one of the relatively

unique things you learn is you learn a

lot about the best practices for how to

actually develop a practical valuable

machine Learning System

this way you're less likely to end up in

one of those teams that end up losing

six months going in the wrong direction

in this class you gain a sense of how

the most skilled machine learning

engineers build systems and I hope you

finish this class as one of those very

rare people in today's world that know

how to design and build serious machine

learning systems

so that's machine learning in the next

video Let's look more deeply at what is

supervised learning and also what is

unsupervised learning in addition you

learn when you might want to use each of

them supervised and unsupervised

learning I'll see you in the next video