Machine Learning Specialization on Coursera | Review

Summary

This video reviews the updated Machine Learning Specialization on Coursera, created by Andrew Ng, co-founder of Coursera and head of Google Brain. The course, now using Python instead of Octave, covers supervised learning, including linear and logistic regression, and delves into neural networks with TensorFlow. It also touches on unsupervised learning, recommender systems, and reinforcement learning. The course is praised for its comprehensive content, engaging teaching style, and practical applications, making it a must-take for anyone interested in machine learning.

Takeaways

• 🎓 The Machine Learning Specialization on Coursera is a revamped and enhanced version of the original course created by Andrew Ng in 2012.
• 👨‍🏫 Andrew Ng is a renowned figure in the field of machine learning, being the co-founder of Coursera and head of Google Brain.
• 🐍 The course has upgraded its programming language from Octave to Python, which is a significant advantage for many learners.
• 💡 The specialization is beneficial for data analysts to understand machine learning concepts and terminologies used by data scientists.
• 🌟 The course covers a range of topics from supervised learning, linear and logistic regression, to advanced algorithms and TensorFlow.
• 📊 The instruction includes a lot of math, but it's designed to be understandable even for those who aren't mathematically inclined.
• 🔍 The course dives deep into machine learning models, including best practices for development and recommendation systems.
• 📚 The specialization consists of three courses: Supervised Machine Learning, Advanced Learning Algorithms, and Unsupervised Learning, Recommenders, and Reinforcement Learning.
• 🛠️ The course is very hands-on, with a focus on building models and understanding how to train them.
• 📈 The course includes interactive videos with Andrew Ng, who explains complex concepts in an accessible manner.
• 📅 The third course of the specialization was scheduled to be released on July 19th, focusing on unsupervised learning, recommender systems, and reinforcement learning.

Q & A

Who is Andrew Ng and what is his significance in the field of machine learning?

Andrew Ng is a renowned expert in the field of machine learning and artificial intelligence. He is the co-founder of Coursera and was also the head of Google Brain. His significance lies in his contributions to the field, including creating the popular Machine Learning course on Coursera.

What is the updated version of Andrew Ng's Machine Learning course on Coursera?

The updated version of Andrew Ng's Machine Learning course on Coursera is a revamped, updated, and more enhanced version of the original course he created back in 2012. It includes new content and uses the Python programming language instead of Octave.

What programming language was used in the original Machine Learning course, and what is used in the updated version?

The original Machine Learning course used the programming language Octave. The updated version now uses Python, which is a significant upgrade for many learners, especially those already familiar with Python.

As a data analyst, why is it beneficial to understand machine learning?

Understanding machine learning is beneficial for data analysts because they often work with people who use machine learning models. Knowing the terminology and concepts can help data analysts communicate effectively and understand the work of their colleagues in data science.

What is the focus of the first course in the Machine Learning Specialization on Coursera?

The first course in the Machine Learning Specialization focuses on supervised machine learning, including logistic and linear regression. It covers the basics of machine learning development, training models with gradient descent, and understanding overfitting.

What is the main upgrade from the original Machine Learning course to the new version?

The main upgrade from the original Machine Learning course to the new version includes a change in the programming language from Octave to Python, and the addition of more in-depth content, including advanced machine learning techniques and practices.

What are some of the topics covered in the second course of the Machine Learning Specialization?

The second course, Advanced Learning Algorithms, covers topics such as neural networks, implementing them using TensorFlow, activation functions, multi-class classification, bias and variance, and different machine learning models like decision trees, random forests, and XGBoost.

What is the third course in the Machine Learning Specialization, and when was it released?

The third course is on Unsupervised Learning, Recommenders, and Reinforcement Learning. It was scheduled to be released on July 19th, as of the recording date mentioned in the script.

What does the instructor emphasize about the math behind machine learning in the course?

The instructor emphasizes that while the math behind machine learning is important, it's not necessary to be able to do the math itself. Understanding some of the concepts is valuable, even for those who are not going into machine learning or do not need all the mathematical details.

How does Andrew Ng teach complex mathematical concepts in the course?

Andrew Ng teaches complex mathematical concepts by breaking them down and using visualizations to help students understand how they apply to machine learning. He makes the content interactive and accessible, even for those who may not have a strong math background.

What is the recommendation for someone interested in machine learning according to the script?

The script strongly recommends that anyone remotely interested in machine learning should take the Machine Learning Specialization course on Coursera, as it is considered one of the best courses available on the subject.

Outlines

00:00

🎓 Introduction to the Machine Learning Specialization on Coursera

The video introduces the Machine Learning Specialization on Coursera, created by Andrew Ng, a renowned figure in the field of machine learning and co-founder of Coursera. The course has been updated from its original 2012 version, with significant enhancements. A major upgrade is the shift from using the Octave programming language to Python, which is a boon for many learners. The course is aimed at providing an in-depth understanding of machine learning, even for those who may not directly use it in their roles, such as data analysts. The video also acknowledges the support of Coursera as the sponsor and highlights the benefits of learning on the platform, which offers a wide range of data-related courses.

05:01

📚 Overview of the Machine Learning Specialization Courses

The script provides an overview of the three courses within the Machine Learning Specialization. The first course focuses on supervised machine learning, covering regression and classification. The second course is more hands-on, diving into advanced learning algorithms, including model building and training. The third course, not yet released at the time of recording, is expected to cover unsupervised learning, recommender systems, and reinforcement learning. The courses are designed to build on the foundational knowledge of supervised learning with a focus on logistic and linear regression and then expand into more advanced topics.

10:02

🤖 Deep Dive into the Advanced Learning Algorithms Course

The video script delves into the details of the Advanced Learning Algorithms course, which is part of the Machine Learning Specialization. The course starts with an introduction to neural networks, including their intuition, math behind them, and implementation using TensorFlow. It progresses to cover activation functions, multi-class classification with neural networks, and advice on applying machine learning effectively. The course also addresses bias and variance, the machine learning development process, and concludes with decision trees and advanced algorithms like random forests and XGBoost. The instructor's teaching style is praised for being engaging and effective, making complex topics accessible.

Keywords

💡Machine Learning

Machine learning is a subset of artificial intelligence that involves algorithms that improve automatically through experience. In the context of the video, machine learning is the central theme, with the specialization course on Coursera aiming to teach viewers how to build and understand machine learning models. The script mentions machine learning several times, emphasizing its importance in modern data analysis and the significance of the course in teaching these skills.

💡Coursera

Coursera is an online learning platform that offers courses from universities and companies worldwide. The video discusses the machine learning specialization available on Coursera, highlighting it as a resource for learning machine learning from experts in the field. The platform is presented as a go-to place for anyone looking to advance their skills in data-related fields.

💡Andrew Ng

Andrew Ng is a renowned computer scientist known for his work in machine learning and artificial intelligence. He is mentioned in the script as the co-founder of Coursera and the head of Google Brain. Ng is also the creator of the machine learning specialization course on Coursera, which the video discusses in detail. His reputation and expertise lend credibility to the course and make it an attractive option for learners.

💡Python

Python is a popular programming language known for its readability and versatility. In the video script, Python is highlighted as the programming language used in the updated machine learning specialization course on Coursera. The switch from Octave to Python is noted as a significant upgrade, as Python is widely used in the field of data science and machine learning, making the course more relevant and practical for learners.

💡Data Analyst

A data analyst is a professional who collects, processes, and interprets data to help businesses make decisions. The script's narrator identifies as a data analyst and discusses the importance of understanding machine learning for data analysts, even if they do not directly use it in their work. This underscores the interdisciplinary nature of data work and the value of a well-rounded skill set.

💡Supervised Learning

Supervised learning is a type of machine learning where the model is trained on labeled data. In the video, supervised learning is a major focus of the machine learning specialization course, with a significant portion of the curriculum dedicated to teaching linear and logistic regression, which are key techniques in supervised learning.

💡Unsupervised Learning

Unsupervised learning is a type of machine learning where the model is trained on unlabeled data. The script mentions that while the course primarily focuses on supervised learning, it also touches on unsupervised learning. This aspect of the course provides a broader understanding of machine learning techniques beyond just those that rely on labeled data.

💡Neural Networks

Neural networks are a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. They are mentioned in the script as a topic covered in the 'Advanced Learning Algorithms' course. Neural networks are a fundamental concept in machine learning, and the course aims to teach viewers not just the theory but also the practical implementation using TensorFlow.

💡TensorFlow

TensorFlow is an open-source machine learning framework developed by Google. It is highlighted in the script as the tool used to teach neural network implementation in the course. TensorFlow's popularity and versatility make it a valuable skill for learners to acquire, and the course's inclusion of TensorFlow tutorials makes it a comprehensive learning resource.

💡Recommender Systems

Recommender systems are a subclass of information filtering system that seek to predict the 'rating' or 'preference' a user gives to an item. The script mentions that the course covers the math behind recommender systems, indicating that the course dives into not just the practical aspects but also the theoretical foundations of machine learning applications.

💡Mathematics

Mathematics plays a foundational role in machine learning, with many algorithms relying on complex mathematical concepts. The script acknowledges the importance of math in machine learning, noting that while it's not necessary for learners to be able to perform all the math, understanding the concepts can be beneficial. The course is designed to make these concepts accessible, even to those without a strong math background.

Highlights

Introduction to the machine learning specialization on Coursera

Andrew Ng's background and his role in creating the course

The updated version of the course includes a switch from Octave to Python

Importance of understanding machine learning for data analysts

Sponsorship acknowledgment for the video by Coursera

Overview of the machine learning specialization and its association with Andrew Ng and Stanford

The specialization covers supervised learning, unsupervised learning, and recommendation systems

Emphasis on the math behind machine learning and its importance

The courses are designed for hands-on learning with a focus on implementation

Introduction to Jupyter notebooks in the first week of the course

In-depth teaching of gradient descent and its application in training models

Coverage of multiple linear regression and its implementation

Detailed week on logistic regression, including classification and cost functions

Discussion on overfitting and its mathematical explanation

Hands-on approach in the advanced learning algorithms course

Teaching of neural networks using TensorFlow

Practical advice on applying machine learning and avoiding common mistakes

Introduction to decision trees and advanced variations like random forests and XGBoost

Upcoming third course on unsupervised learning, recommender systems, and reinforcement learning

Strong recommendation for the course for anyone interested in machine learning

Final thoughts and call to action to take the course

Transcripts

what's going on everybody welcome back

to another video today we're going to be

taking a look at the machine learning

specialization on coursera

[Music]

angering needs no introduction but if

you have not heard of him andrewing is

the co-founder of coursera the very

platform that we're going to be looking

on today he was also the co-founder and

the head of google brain so this guy

knows what he's talking about and he is

extremely extremely intelligent i

mentioned andrew ing because he is the

creator of this machine learning chorus

but he created it back in 2012 but this

one that we're gonna be looking at today

is a revamped updated and more enhanced

version of the one that he originally

created and we're gonna be diving into

the courses so you can see what you're

actually gonna get if you decide to take

this course but from everything i've

seen is absolutely phenomenal one of the

biggest upgrades that they made from the

original course is that they changed the

programming language that they used in

it originally they were using a

programming language called octave and

now they're using python so if you are a

python fanatic like myself that is a

huge huge upgrade now i'm a data analyst

and my audience is mostly data analysts

as well so you may be wondering why

we're even looking at machine learning

specializations because that's more at

data science well

one of the biggest things that i usually

tell people is that you may not use

machine learning as a data analyst but

you're going to be working with people

who do use it and so understanding how

it works understanding the terminology

behind it can really help you go a long

way and it really helped me out about

two years ago when i was on a data

science team and they were talking about

models and all these different things

that they were doing and i felt like i

actually knew what they were talking

about and i could kind of keep up with

them it was really beneficial to at

least know the basics of machine

learning now before we jump onto my

screen and start taking a look at the

specialization i want to give a huge

shout out to the sponsor of this video

and that is coursera corsair is one of

the best platforms just about anything

data related so if you want to learn sql

all the way to machine learning you can

do that on coursera or if you want to

become a data analyst a data scientist

or a data engineer you can do all of

your learning right here on coursera it

has been one of my favorite platforms to

learn on for the past five years since i

became a data analyst and i still love

it today thank you again to coursera for

sponsoring this video and without

further ado let's jump on my screen and

take a look as you can see on my screen

this is the machine learning

specialization it is brought to you by

deeplearning.ai

and stanford

both things that are associated with

andrew eng now

there are a few other instructors on

here but andrew's the one who's actually

doing almost every single one of the

videos goes through all the math walks

through everything

and i mentioned him specifically because

he's just such a huge name in machine

learning and artificial intelligence

he's just

incredibly popular and um you know the

original course that he did back in 2012

was like a staple in the machine

learning community i mean just about

everybody who got started walked through

it and it's just because he's so smart

and he

talks about it so well

so if you haven't taken his original one

this new one is just enhanced it just

goes above and beyond with that original

one did so if you haven't taken it then

this is like the time to take it and i'm

gonna give you a spoiler

uh it's amazing and so

with that being said let's jump into it

so it just came out

um

like a couple weeks ago from when i'm

recording this like the middle of june

is when it came out and you're going to

learn a ton of stuff in here um right

down here you're going to build machine

learning models with numpy and scikit

learn

building training

you work mostly with supervised learning

andrew goes a little bit into the

unsupervised learning as well

but he'll even tell you he primarily is

looking at linear and logistic

regression those type of machine

learning models so

that this is like i would say

the nuts and bolts of what you're going

to learn

he teaches you about the best practices

for machine learning development

how to train neural networks using

tensorflow and tensorflow is fantastic

if you've never used it i mean he goes

really in depth into all these things

like none of these are just like brushed

off or like the basics he goes like

really in depth in almost all these

things as well as recommendation systems

and how they work and the math behind

them and everything it's really

fascinating now one thing i want to

mention before we get into like the

actual course is that

he goes into a lot of math

if you don't know the math behind this

stuff that is okay in fact

it's pretty common for most people who

take this to not understand the math

behind it and he talks about that in one

of his videos he's like hey listen the

math behind this is really important but

it doesn't mean you have to go and be

able to do the math just maybe

understand some of the concepts

and

it's true a lot of this math is really

advanced um and so

again that's why it's like if you aren't

going into machine learning and you

don't need in all this math still

walking through these videos and

understanding these concepts i mean it

is

phenomenal let's go down really quickly

to the courses there's three courses

the biggest one is the supervised

machine learning regression and

classification again that's all

supervised machine learning the second

course is the advanced learning

algorithms this one's very hands-on so

it's a lot of building the models

understanding how to train your models

and things like that and then we have

the third course which this one has not

been released yet as of when i'm

recording it although uh it may be

when i release this video it may be

available but

this goes into the unsupervised learning

recommenders and reinforcement learning

so again the bulk of what the original

course and this is built on and what you

are going to learn is the supervised

machine learning the logistic and linear

regression that is what this is

primarily focused on but the third

course is on kind of the other things

that are still popular which is not as

heavily used like unsupervised learning

and recommendation systems or

recommenders so let's go and take a look

at this first course and see what is

actually in there all right if we take a

look at week one this is basically

andrew kind of walking you through the

basics andrew is like the ideal

professor i mean just look at him he's

just like the happiest guy and you can

tell he just loves what he does he's

just like that professor in college that

you want to take his course or his class

over and over again but you already

passed six he was such a good teacher

that's how andrew was this entire time

he's just a fantastic teacher and he

teaches it extremely well so in this

first week you're gonna go through the

supervised and unsupervised learning

just what are what is it how does it

work

um and the math behind it and then he's

even gonna introduce jupiter notebooks

now most of these videos whoops

most of these videos are exactly like

how he has them right here so he's

talking

and then he'll have some type of kind of

white board where he's talking over it

and and reading through it asking

questions it's very interactive

um and let me find one really quickly

because he's going to go into a lot of

the math as well so let me find the math

really quickly i'll kind of show you how

he does that also so this is a good

example of how he teaches the math in

his course so he usually has some type

of visualization then he has the

equation that we're looking at and

typically the equation is

looks really complex like this one to me

i would not understand it at all if i

was just taking a look at it but andrew

has a really unique way of just breaking

things down helping you understand it

and kind of showing you usually on some

type of visualization how it actually

works in machine learning so if you're

not a math person and you don't care

about the math behind it you know you

don't have to skip these sections

because they're just so good you'll feel

like you understand it because andrew

just teaches it really well but this is

still week one and so in this one you're

learning how to train models with

gradient descent and kind of

understanding what gradient descent is

along the way you have these quizzes all

really great i can't speak highly enough

of this course it's just absolutely

phenomenal in week two we have multiple

linear regression which is something he

mentions in the first one but then you

actually learn how to implement it and

use it uh in week two and we also go

into gradient descent and practice so it

kind of shows you how it's actually used

and not just you know what it is like he

did in the second week and then week

three we have a lot of logistic

regression it's basically the entire

week this is what you cover so you have

classification with logistic regression

cost function with logistic regression

gradient descent for logistic regression

and then at the very end you have the

problem with overfitting now this one

was actually the math was

the most interesting in this section to

me in this first couple weeks because

this one the visual is just phenomenal

about how overfitting actually occurs

and how to fix it

using math uh i thought it was just

extremely interesting so again if you're

not a math person you don't have to take

it but i love the math behind this

overfitting stuff it was extremely

interesting now let's take a look at the

advanced learning algorithms course and

i think this is the one that most people

are going to

really enjoy especially if you want to

get more hands-on so in this course in

the first week we're going to be looking

at neural networks and then we'll

actually be implementing it using

tensorflow and so in this first week we

look at neural network's intuition so

what is neural networks how is it used

let's look at the math behind it that's

kind of what you're going to be looking

at and then we have neural network

models and then we have the tensorflow

implementation this is where they

actually walk you through how to set up

tensorflow how to actually use it to

create your first neural network and

right down here we have neural network

implementation in python again

super happy that they're using python i

think that's just a huge upgrade from

the previous course is that a lot of

people and companies are using python

for machine learning and so i think it

just obviously shows that they're

keeping up with things they're improving

and making things better which is just

better for everyone in the long run in

week two we're still looking at neural

networks again this stuff is a lot more

of the advanced as the course says it's

a lot more the advanced stuff so it's

not as introductory like the first

course where he's kind of walking

through here's what machine learning is

here's how it's used these are like

let's dive into the math let's look how

to implement let's start using it it

goes from beginner to advanced i think

pretty quickly at least advanced for me

so you know it's kind of like a diving

head first into this stuff and just

really trying to learn it as you go so

we have these activation functions

we also have multi-class classification

and neural networks and how those work

together now in week three he kind of

takes a step back and he's like whoa you

know i know i said let's just dive in

and learn it and get our hands dirty but

we kind of need to take a step back and

learn a little bit more so that when

you're actually using this you don't

just dive in and make a bunch of

mistakes so here are some of the things

you should look at so we look at some of

his advice for applying machine learning

on what models to use and evaluating how

if it's good or not we also look at bias

and variance and then the machine

learning development process and how to

actually implement your machine learning

models once you've actually developed

them and then in the very last week you

learn about decision trees and a few

different variations and ways to do them

so you have decision trees and kind of

what they are

and on the surface they're pretty simple

but they get pretty advanced that's why

it's in this advanced course but we have

the decision tree

section right there

then we kind of

look at how to use them how to implement

them and then he walks you through how

to actually set it up in the code and

then at the very bottom we have a few

different options things like multiple

decision trees sampling with replacement

random forest algorithm and xg boost

xgboost is all over i see it everywhere

so again these are things that if you're

not going to be using them if you're not

going to be implementing these things

these are really great things to just

know what they are

and how they work so that if you are

working with somebody who's using these

you can at least understand them and

work with them instead of just being

confused and asking a million questions

so the third and final course is the

unsupervised learning recommenders and

reinforcement learning again this has

not been released yet it's not released

until july 19th which if you're watching

it after july 19th there you go it's

already out there but i plan on

releasing this at the first week of july

so as of when i'm releasing this this

most likely will not be out yet but we

can get a sneak peek really quickly into

what you're going to be learning in here

so you're going to be learning how to

use unsupervised learning techniques for

unsupervised learning including

clustering and anomaly detection

building a deep reinforcement learning

model and building recommender systems

with collaborative filtering approach

and a content-based deep learning method

so there you go

again we just don't have it yet so i

can't dive into it but

it's going to be phenomenal and i'm

gonna take the whole thing again i am

not a machine learning expert but it has

really helped me work within my team or

my old team when i was working with the

data scientists to really understand

this stuff so if you're somebody who

wants to get into machine learning 10

out of 10 100 you need to take this

course um i and i don't say that for

every course

usually there's a lot of qualifiers it's

like if this if that

i think that if you have a pulse

and you are even remotely interested in

machine learning you should take this

course it is by far hands down one of

the best courses that you will ever take

on machine learning and i've taken

several other ones and i've learned a

lot more about the implementation how to

actually create it and do that

but in this one you're going to learn

some of that as well but you're also

going to learn a lot of the math behind

it how it actually works and for the

original course back in 2012 i mean that

thing was a staple every machine

learning person every data scientist had

taken it because it was just phenomenal

and this just builds on it and makes it

better so if you've gotten this far and

you are interested in this at all even

in the slightest i think you should

absolutely take it again i would not be

saying that if i did not believe it it's

just

phenomenal it's one of the best courses

on machine learning that you'll find out

there anywhere so with that being said i

hope that this video was helpful i hope

that you're as excited about this as i

am i think it's just a fantastic reboot

and upgrade of the original course it's

just really great to see them continuing

to improve and make things better over

time so thank you again and if you like

this video be sure to like and subscribe

below and i'll see in the next video

[Music]

you