Jake VanderPlas - Machine Learning with Scikit-Learn (I) - PyCon 2015

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  • čas přidán 8. 04. 2015
  • "Speaker: Jake VanderPlas
    This tutorial will offer an introduction to the core concepts of machine learning and the Scikit-Learn package. We will introduce the scikit-learn API, and use it to explore the basic categories of machine learning problems and related topics such as feature selection and model validation, and practice applying these tools to real-world data sets.
    Slides can be found at: speakerdeck.com/pycon2015 and github.com/PyCon/2015-slides"

Komentáře • 25

  • @smarwaha6
    @smarwaha6 Před 8 lety +22

    This is an excellent tutorial for learning scikit - machine learning in python. Jake is a great speaker and will ignite your interest in python & ML if you are not already excited about it ;).
    Thanks Jake!

  • @jakevanderplas2418
    @jakevanderplas2418 Před 9 lety +33

    Notebooks and other materials can be found here: github.com/jakevdp/sklearn_pycon2015
    Also, you can view the notebooks statically on nbviewer: nbviewer.ipython.org/github/jakevdp/sklearn_pycon2015/blob/master/notebooks/Index.ipynb

    • @sidmitradel
      @sidmitradel Před 9 lety

      Jake Vanderplas Hey this was great, made my first entry to Kaggle, based on what i learn of your tutorial. Good stuff !

    • @TheSirDorian
      @TheSirDorian Před 9 lety

      Sivathanu Kumaraswamy I think the fig_code module is in his github, you need to get it seperately from scikit learn.

  • @Dwright3316
    @Dwright3316 Před 8 lety

    This tutorial was great, Jake. Thank you for sharing your knowledge and skills!

  • @rodrigocaetano735
    @rodrigocaetano735 Před 6 lety

    Excellent lecture. Jake did a fabulous job presenting basic though fundamental ideas about machine learning and scikit implementation. Thank you very much!

  • @vector4067
    @vector4067 Před 8 lety +2

    I learned a lot from this 3 hours video.

  • @captainarf
    @captainarf Před 8 lety

    Excellent talk! Having the ipython notebooks to follow along and experiment with is also a huge bonus for learning the material. Thanks Jake!

  • @xianlonghou
    @xianlonghou Před 8 lety

    Awesome presentation. I learnt a ton. Thanks Jake!

  • @PanWu
    @PanWu Před 8 lety +1

    This is great Video about Python for Machine Learning, very informative, thank you Jake!

  • @user-rr8er4nu8y
    @user-rr8er4nu8y Před 7 lety

    this is a great tutorial to get a concept of sklearn. Thank you;)

  • @prashanthgedde
    @prashanthgedde Před 8 lety

    Excellent talk and content!

  • @SepehrMaleki
    @SepehrMaleki Před 8 lety +8

    skip to 11:17

  • @cmos1313
    @cmos1313 Před 7 lety +1

    Hey, great tutorial. However, I am getting the DeprecationWarning for your 03.1 notebook. It is asking me to reshape the "X" set. Could you help me understand why it going on with that error?
    Thanks

  • @rajuchoudhari2409
    @rajuchoudhari2409 Před 8 lety

    Nice presentation Jake. where could I find the code presented?

  • @lazypunk794
    @lazypunk794 Před 7 lety

    why am I getting the linear regeression coef_ as 2.000002 and intercept as 0.999999

  • @_asim_ktk
    @_asim_ktk Před 6 lety

    code runs into error for SVM, Decision tree, Random Forests etc seems this code doesn't work with python 3.6

  • @rajuchoudhari2409
    @rajuchoudhari2409 Před 8 lety +1

    ok...I found the code now. thanks.

  • @user-sd2en6pn3z
    @user-sd2en6pn3z Před 4 lety

    Five years later, it seems there are several things in the notebooks that have been deprecated and will not run unless you install the older versions. Is there an easy way to update the notebook code?

  • @MonalJain94
    @MonalJain94 Před 9 lety

    Github and slides link(not showing any uploaded slides) is not working. Please fix. Thanks

    • @kmbd1234
      @kmbd1234 Před 8 lety

      +Monal Jain just checked, it works ...

  • @abraham.menyhart
    @abraham.menyhart Před 8 lety +2

    Under the hood :P

  • @MrEternalFool
    @MrEternalFool Před 8 lety

    Somehow I don't like this style of explaining.