The Central Limit Theorem - With Examples in Python

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  • Опубліковано 18 гру 2024

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  • @Hsnfci83
    @Hsnfci83 2 роки тому +1

    Thanks Jon .. This was quick and fast and to the point directly.

    • @JonKrohnLearns
      @JonKrohnLearns  2 роки тому +1

      You're welcome, Hassan! Glad the pacing was right for you :)

  • @claudiomarcio7579
    @claudiomarcio7579 3 роки тому +2

    Very good explanation, about how to implement the central limit in python. I'm starting to learn data science and I have seen that some courses have a lack of statistics base, so it drives to a lack of how to implement statistics in python, like hypothesis test, probabilities, chi-square test, and so on.

    • @JonKrohnLearns
      @JonKrohnLearns  3 роки тому +1

      As part of my 8-subject Machine Learning Foundations series, subject 5 is on Probability and subject 6 is on Statistics. We will cover all of these topics!
      More detail on the series here: github.com/jonkrohn/ML-foundations
      (Growing) UA-cam playlist here: ua-cam.com/play/PLRDl2inPrWQW1QSWhBU0ki-jq_uElkh2a.html

  • @pythoncourse3102
    @pythoncourse3102 3 роки тому +1

    Awesome video, I just have one doubt, what it the logic behind mentioning _=sns.ditplot()?, we can even use sns.ditplot() directly. is there any specific meaning for prefixing _=?

    • @JonKrohnLearns
      @JonKrohnLearns  3 роки тому

      Yep! Try removing it. It's cosmetic, but if you do not include the "_ =" prefix, then the Jupyter notebook creates an ugly line of output code alongside the desired plot.

  • @SwavimanKumar
    @SwavimanKumar 4 роки тому +2

    You are making super awesome videos. You made it so simple to understand using notebook. Thanks for creating such content. Please make videos on NLP. Keep growing.

    • @JonKrohnLearns
      @JonKrohnLearns  4 роки тому

      So glad you're enjoying the videos, Swaviman!
      Have you already seen this two-hour NLP tutorial I have on UA-cam? ua-cam.com/video/rqyw06k91pA/v-deo.html
      I'm focusing on ML Foundations videos right now (linear algebra, calculus, stats, computer science, etc.) but will definitely get to more NLP videos after that.

  • @amrellabban7636
    @amrellabban7636 4 роки тому

    Great explanation of what is a core foundational concept in ML and stats in general - one of the best I've seen. Easy to follow, and Jon's Canadian accent is eerily soothing!

  • @oscarlamelo
    @oscarlamelo 3 роки тому +2

    Thank you very much, i am a probability student in France and this help a lot

    • @JonKrohnLearns
      @JonKrohnLearns  3 роки тому +1

      You're most welcome, Oscar! Inspired by feedback like yours on this video, I've recently been releasing a comprehensive series of video tutorials on all of the foundational subjects for data science and machine learning, including probability theory. You can read all about it in GitHub (www.github.com/jonkrohn/ML-foundations) or jump straight to the UA-cam playlist (ua-cam.com/play/PLRDl2inPrWQW1QSWhBU0ki-jq_uElkh2a.html)

  • @kisholoymukherjee
    @kisholoymukherjee 2 роки тому +1

    Really well explained. Thanks

  • @ekta9197
    @ekta9197 2 роки тому

    thank you, Jon. But, according to the theory there should be with replacement??? you did it without replacement? please answer..does it make any difference with or without replacement?

    • @JonKrohnLearns
      @JonKrohnLearns  2 роки тому

      Ah great question, Kashif! As long as the size of the samples are very small relative to the source distribution, I don't think it makes a difference. Check out the discussion here: www.researchgate.net/post/To-ensure-independence-in-central-limit-theorem-we-need-sample-size-to-be-less-than-10-of-the-population-size-if-sampling-without-replacement-Why#:~:text=Finance%20and%20Economics-,To%20ensure%20independence%20in%20central%20limit%20theorem%2C%20we%20need%20sample,size%20if%20sampling%20without%20replacement.

  • @PLara-tf4eg
    @PLara-tf4eg 2 роки тому +1

    Thank you, just what i needed

  • @zeesh_ali
    @zeesh_ali 4 роки тому

    Great explanation about central limit theorem and thank you for providing the code as well.

    • @JonKrohnLearns
      @JonKrohnLearns  4 роки тому

      Thank you, Zeeshan! You're most welcome :)

  • @katiewu3000
    @katiewu3000 4 роки тому

    Very well explained and demonstrated with clear python code.

    • @JonKrohnLearns
      @JonKrohnLearns  4 роки тому

      Thanks, Katie! If you like that tutorial, you might also like my ML Foundations series, which I've begun publishing in the past couple of weeks and will cover over a hundred of the most important subjects for deeply understanding ML, one of which is the Central Limit Theorem: ua-cam.com/play/PLRDl2inPrWQW1QSWhBU0ki-jq_uElkh2a.html

  • @simenandreasknudsen9272
    @simenandreasknudsen9272 3 роки тому +1

    This is great, thanks! Could you also do an example with a real world dataset? Like using the theorem to solve a real problem instead of with generated numbers.. Thank you!

    • @JonKrohnLearns
      @JonKrohnLearns  3 роки тому +2

      Yep, you bet, Simen! The Theorem on its own can't be used to solve any real-world problems that I'm aware of, but it is a critical concept underlying countless statistical and machine learning models (which can themselves be used to solve real-world problems). Eventually, I will record my own "Probability and Statistics for Machine Learning" videos at home for release on UA-cam, which will contain many examples with real-world data. In the meantime, the professionally-recorded version of this same content is available via O'Reilly: learning.oreilly.com/videos/probability-and-statistics/9780137566273/

  • @davida99
    @davida99 3 роки тому +1

    Thank you so so much for this vid

    • @JonKrohnLearns
      @JonKrohnLearns  3 роки тому

      You're most welcome, David! Hopefully you've already noticed that I've been releasing tons of math videos lately, particularly on linear algebra and calculus. More probability and stats videos to follow after that, including several around the central limit theorem!

  • @dmitriichuprakov3282
    @dmitriichuprakov3282 2 роки тому +1

    Confidence interval with examples in Python! plz

    • @JonKrohnLearns
      @JonKrohnLearns  2 роки тому

      You bet, Dmitrii! That's on its way. It'll be covered in the "Statistics" playlist that I hope to begin releasing on UA-cam (and Udemy) in 2023.
      In the meantime, it's available via oreilly.com and the code is open-source here; search for "confidence interval" in this Jupyter notebook: github.com/jonkrohn/ML-foundations/blob/master/notebooks/6-statistics.ipynb

  • @jheel-patel
    @jheel-patel 4 роки тому +2

    How do you use it in machine learning? Is it for outlier detection?

    • @jheel-patel
      @jheel-patel 4 роки тому

      I can think of these. Can you confirm my understanding
      1. It is used to check whether our sample size is sufficient for modeling
      or we need more data
      2. Do we need to remove the outlier to make our data normally distributed?

    • @JonKrohnLearns
      @JonKrohnLearns  4 роки тому +1

      @@jheel-patel 1: yes absolutely.
      2: Not necessarily as you might be able to use a "Box-Cox Transformation" to transform toward normal without outlier removal... however, I do recommend removing outliers -- or at least investigating them to check if anything's fishy with your data collection -- before transforming toward normal.
      I've been releasing a "Machine Learning Foundations" series of courses in recent months (see github.com/jonkrohn/ML-foundations/). I've published the first course -- Intro to Linear Algebra -- on UA-cam and am now filming the second course. The fifth and sixth course (on probability and statistics, respectively) will address directly address your questions related to the Central Limit Theorem in quite a lot of detail.

    • @jheel-patel
      @jheel-patel 4 роки тому

      What are some other uses of it in data science?

    • @jheel-patel
      @jheel-patel 4 роки тому

      @@JonKrohnLearns Thanks for quick response. appreciate 🙌

    • @JonKrohnLearns
      @JonKrohnLearns  4 роки тому

      @@jheel-patel The Central Limit Theorem underlies all of probability and statistics, as well as much of machine learning. It is perhaps the single most fundamental concepts underlying all of data science. Almost every class of predictive model depends on it. I will explain more in subjects 5 and 6 of my ML Foundations series (github.com/jonkrohn/ML-foundations). Those videos should be released on UA-cam early next year; I'll send updates via my email newsletter, which you can sign up for on jonkrohn.com :)

  • @claudiomarcio7579
    @claudiomarcio7579 3 роки тому

    First of all, sorry to contact you by this channel... I did an inferential statistical analysis on stroke data available in Kaggle, in order to select the variables to a predictive model. I used the chi-square test and independent mean test. It's available on my GitHub github.com/cmapereira/stroke_analysis if someday you could take a look I will appreciate that.