Practical Machine Learning Tutorial with Python Intro p.1

Practical Machine Learning Tutorial with Python Intro p.1

Well, girls and guys and welcome to an in-depth machine learning tutorial series. The objective of this tutorial series is
to give you a holistic understanding of machine learning and how it works and
we’re going to be doing this by covering a variety of algorithms so first we’re
gonna be covering regression then we’re going to be moving into classification
with k-nearest neighbors and support vector machines and then we’re going to
get into clustering with flat clustering a hierarchical clustering and then
finally we’ll be getting into deep learning with neural networks. So, each step of the way with each of the major algorithms we’re going to cover a theory,
application and then we’re going to dive in deep to the inner workings of each of
them so with theory this is the high-level
intuitions it’s actually pretty quick to digest the theory of all the algorithms.
Most of the algorithms are actually fairly basic since they need to be able
to scale to very large amounts of data. Then we move on to application this is
also fairly quick we’re going to use a module like
scikit-learn and apply these algorithms to real-world data to see exactly how
they work what kind of input they expect from us and what kind of output we
should expect back from them. Then finally we dive into the inner workings
in the way that we’re going to do this is by recreating the algorithms
themselves from scratch in code including all the math and all that so
when you do this you’ll be able to have a truly complete understanding of how
these algorithms work which is going to help you down the line So, in order to
fall along with a series i strongly suggest you at least have the basics of
Python 3 down understood if you don’t I do have a Python 3 basics tutorial
series here check that out you really just need to
get at least to the point where we install modules with pip. After that you can continue on but you
really need those that initial I think it’s like maybe 10 or 15 videos of core
understanding of Python. We’ll also be covering a healthy amount of math here
but we’ll be talking about the matting and explaining that as we go for sure so
you’re not really expected to know much about math becoming mostly algebra and geometry
that comes up. So, machine learning was really came
about in the fifties so like more than a half century ago now and it was defined
in 1959 by Arthur Samuel as the field of study where we give machines the ability
to learn without being explicitly programmed to do so. So, the way I kind of like to think of it
is it’s in viewing knowledge to a machine without hard coding that
knowledge so interestingly enough when I talk to people both programmers and
non-programmers and and and find out what they know about machine learning most people think machine learning is
hard-coded so when you ask them what’s wrong with machine learning or how is it
different from actual learning that’s where you find that most people think
it’s hard coded so kind of interesting issue there that most people are
completely unaware that this field exists and it’s actually not being hard
coded so that was one machine learning was actually kind of defined in 1963
Vladimir Vapnik came up with the support vector machine but this really went
pretty much overlooked until the 90s so Vladimir Vapnik was in the
Soviet Union and then in the 90s he was actually scooped out of the Soviet
Union by bell labs and that’s when he showed that that the support vector
machine was better than the neural network at the time doing handwritten
character recognition so it’s handwritten digits of our call right but
anyway it was better than the support vector a better than the neural network
at that task the support vector machines really took the lead for quite some time
really up until very recently when Google basically has kind of come back
to really put some weight behind the neural network specifically with deep
learning. But if you think you’re kind of late to the to the party so to speak
with machine learning I assure you you’re not because I mean think about
computers in the 95s i mean we’re talking we just started putting transistors on
printed circuits instructions to your computer was a maximum of a handful of
bits at a time so pretty bad i mean even think about your computers in the 90s this was very hard at even if you were like a PhD student is
very difficult to even get access to a machine that could run significantly a
support vector machine at scale [let’s say]. Whereas nowadays we live in a time where
you can engage in deep learning with neural networks on like gigabytes or
even terabytes of data and what you can do is you can spin up a you know
hundred-thousand-dollar GPU cluster on amazon web services and basically just
rent it for a few dollars an hour and then be done with it that’s incredible like we living an
incredible time this is the best time to be alive is I think right now is the
first time we’ve really been able to really stretch and flex the muscles of
machine learning up to this point it’s really been learning without the machine
part so we also so much to the point where like with scikit-learn you can you
can use scikit-learn with almost no understanding at all you just apply it
and you can usually get about 90~95% accuracy without
messing with the default parameter yeah you can just get it with the
default parameters so that’s also pretty crazy right you want to push the limits and get more
accuracy out of it then you need to learn how they work and how you can
tweak those parameters so you’re working on self-driving car getting 90 to
95% accuracy and identifying the difference between like
a blob of tar and a child in a blanket that’s not good enough you need much any
more accuracy than that so anyway that’s what this series is for
is for the people are really looking to push the limits on what’s available so
if you really just want to learn the basics actually already have some simple
machine learning tutorials out there for just applying machine learning to a
dataset you can do this actually very very fast. So anyways the first topic that we’re
gonna be covering is regression and let’s go ahead and get into it.

100 thoughts to “Practical Machine Learning Tutorial with Python Intro p.1”

  1. hello very good videos, congratulations, I write to make a request please .. upload to video recommending books to deepen python and learn machine learning, I would appreciate it of heart, thank you.

  2. Check out Google's Maching Learning Crash Couse also. I was a bit confused about all of the terminology that he was using and this helped out a lot.

  3. I want to figure out to learn math for machine learning have to learn all contents linear algebra and calculus in khanAcadmy ?

  4. Hi All..

    I need to guide/help to install all these belows modules in my laptop.
    pip install numpy

    pip install scipy

    pip install scikit-learn

    pip install matplotlib

    pip install pandas.

    Please do me this favor.


    A machine learning algorithm demo purely in Python — without any other modules than math and random. Simplified, it still does the trick… results 1.0 (or as slightly above) soon matches up.

  6. 5:35 "if u just want to learn the basics i have a simple machine learning tutorials that u can apply to a data set…" can u provide the link for the tutorials? before diving in deeper,i just want to assume the algorithms as black boxes and then just want to know how to feed data in to the algorithm and then getting results from it. and then i want to understand the algorithms deeply and then go all the way down to Statistics and Calculus.

  7. quandl thing was really very different from what it is now . i left to search for other videos on machine learning but no one was to the point therefore i landed back here 🙂

  8. Thank you so much you add unmeasurable value and inspiration to my life. Through your tutorials I believe I can achieve so much more one day.

  9. I opened a repository for everyone who wants to go through the code step-by-step with some small explanations 🙂

    A sub would be great as well! Harrison motivated me to create me own Python series starting soon on my channel 🙂

    pls reply asap

  11. Hi from Columbia, let me give you my feedback. Honestly, you're so kind to share it with all people. Right now, I'm so grateful to you, I really apraciate it. Thank you so much @sentdex.

  12. Algorithm News! Method of naming items to make them sortable: Discovered!
    A joint announcement by the CIA and NASA today revealed that the inhabitants of "a planet far far away" have been found to use a method of putting titles on items to that they can be sorted in order by common algorithm-using apps, e.g. browsers. The newly discovered method involves using a "Roman numeral" on the left-hand, or "big end" of the title.

    These aliens number things like videos as follows: 1.) title. 2.) nuther title, 3.) stuff, 4.) freeble, 5.) mumf…
    This amazing breakthrough method means that when sorted the videos will come out "sorted" in order 1 then 2, then 3!

    President Trump announced that he thinks he understands the idea and has referred it to a Cabinet sub-committee for further study.

  13. lmao for some reason youtube has decided that this video should pop up every time i open youtube, the phrase "hello girls and guys, and welcome to an IN DEPTH machine learning tutorial series" is echoing in my head every night before i fall asleep

  14. I was going to build a cluster like that until I realized my gtx 950 = like 50+ sbc nodes, and now i'm selling my gtx 950 and getting 1060 6gb, those things are insane

  15. Thank you so much sentdex for all your helpful machine learning tutorial. Very handy tutorial and easy to understand. Would you mind help me how to get the lung cancer dataset images that you are using on your last video series of this course. I need them to train my model (i Mean the DICOM files ).

  16. Can you please turn off the terrible automatic translation for the video titles and descriptions? I'm from Germany, but I understand English just fine, so these awkward translations add literally nothing of value. Someone who doesn't understand English also won't benefit, since the video itself is in English. These translations are slowly appearing mostly on videos from big channels, so I assume that this has some kind of opt-in or opt-out mechanism.

    I have not met a single person who was happy about these translations, there are also many complaints on reddit and other internet forums.

    Unfortunately, it is not possible for a user to disable these things. Please consider doing it for your viewers or at lest do a survey to find out if people like it or not. I really like your videos, but this issue is bothering me quite a lot.

    Thank you!

  17. i immediately hit subscribe when i noticed you are the guy helping other small code related channels such as tech with tim. keep up the good work.i'm gonna binge watch this whole playlist now.

  18. Wow. Three years ago, this video popped up in my recommended. I had just finished my High School Computer Science class and I was interested in learning more about computers. After watching this video, I decided that before I began learning something like machine learning, I should learn something like Python.
    Welp, here I am today after three years of Python where I went down rabbit holes in web design/scraping, bots, data analysis, and a handful of other languages. And now I'm back here; full circle.
    Seriously though, thank you for putting me down this route. I 100% wouldn't be in CS as much as I am without this video. 🙂

  19. 3b1b and this are the only channels you need to learn neural networks. I recommend watching 3b1b neural network series before this

  20. Can someone please advise on what laptop is best suited for machine learning. I know you could train your models on AWS, taking advantage of the high computing GPUs they provide, but in a case where one hasn't got internet access and you have a significantly large dataset, what laptop is best suited for this scenarios?

  21. Hi sir,
    My name is jyoti kant from india, I am a job seeker here and I don't have so much money ₹.25m do ai courses will you please guide me to what to do to learn Python or any other ai supported language free.

  22. I want to be a drone maker. What do I need to know for that? Right now I know python 3. Thank you in advance for the answer.

  23. Hi,

    before to take up these videos. is there anything to learn basic of Machine Learning prior to these video, because am new to ML and DL.

    i need to learn basic also, notes or videos link of Basics. Please and Thanks.

  24. I'm trying to learn this. I have been working with python for two to three years and love it!!

  25. I see that this video was filmed over 3 years ago. I've been in college for only 1 year now and I know that everything in programming changes a lot everyday. Can you tell me if this course is still up to date ? If yes I will watch all of it and like every single video. Thank you 🙂

  26. Where can I find the dataset used in this video?? It seems it has been removed, any other alternative available?

  27. A few months ago I was watching your coding tutorials, they got me interested in python and other languages. Fast forward 3 months I've discovered how cool Machine Learning is. I've been struggling with it for a two weeks and here I am back at your videos!

  28. Do i Need to learn MatPlotLib, SKlearn…etc??
    I know python more than basic..with a basic knowledge of numpy
    I have learnt KIVY from you as well

  29. 92% discount #coupon #udemy #course for

    #Clustering & #Classification With #Machine Learning In #Python


  30. Guys, should i take this course first then andrew's or the latter first. What are the pros and cons? Thanks a lot, i am confused on what to take first. 🙁

  31. What are the prequisites for starting this course?
    Is there any course of Python on Your Channel, so I should learn it first and then jump on to this course?
    P.S- I am complete Beginner

  32. Are there any prequisites to this, or can I just start it if I don't know anything about the Scikit-learn?
    Just note that I do know the basics of python (I learnt from your videos so thanks a lot :)).

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