#3 Machine Learning Specialization [Course 1, Week 1, Lesson 2]
Summary
TLDRThis 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
🤖 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.
📚 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.
Mindmap
Keywords
💡Machine Learning
💡Arthur Samuel
💡Checkers Program
💡Learning Algorithm
💡Supervised Learning
💡Unsupervised Learning
💡Recommender Systems
💡Reinforcement Learning
💡Practical Advice
💡Best Practices
💡Quiz Questions
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
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