Statistics: Sampling and Describing Data | Math for ML (Part 2)

This video provides an introductory crash course to statistics, with the intention of teaching concepts that are foundational to machine learning. Elevate your workstation with FlexiSpot:
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In this course, we will discuss concepts such as sampling from a population, measures of central tendency, and measures of dispersion.

Timestamps
00:00 Introduction
00:28 Population and Sample
04:12 Measures of Central Tendency
04:34 Central Tendency: Mean (average)
10:02 Central Tendency: Median
12:26 Central Tendency: Mode
15:35 Measures of Dispersion
17:11 Dispersion: Range
22:05 Dispersion: Variance and Standard Deviation

Feel free to leave any questions.

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Thanks for watching everyone!
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[devesh]

Understood the concept✅
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[worknevr]

Very well organized, succinct, with clear, relatable examples. After 25 years, I'm shifting my career from software engineering to data science because I need a new challenge. Word of caution to those reading this: DS is a heavy lift. It's not something you can "youtube crash course" over a weekend. It takes a dedication to learning math, of course, statistics, and a sustained practice effort. The best practice course I've found so far is Kaggle (not throwing shade on others).

If you're serious about DS as a career path, be prepared to climb that learning curve. You'll get out of it what you put in.

Thanks very much for your videos, Kylie!

[anthonyhernandez]

Perfect timing for me! Looking forward to the rest of this series!

[slamani]

Thank you Kylie..awaiting future series!!

[yeshengwei]

Your course is far more understandable than those given my the maths lecturers when I was an undergraduate 👍 Well done! Really appreciate it!😊

[Playfortmrw]

Hey Kylie. Great video! I just recently found your channel and I've been binge watching all your vids and trying to absorb everything you teach here. I'm starting out my career as a data scientist and trying to learn more about machine learning. I'm hoping you could one day make a video on how to handle categorical data (both ordinal and nominal) when doing machine learning/modelling. Appreciate what you do here. Keep it up!

[cerealport]

Thanks Kylie, this is very necessary, It continually surprises me just how many new-hires in my company claim to have science or engineering degrees, but do not understand what a median is, let alone the usefulness and significance of standard deviation, or how to interpret percentiles.

I'm not sure how this is possible, given that fundamental statistics was high-school-level stuff when I was there, but I guess every country has different standards...

[akhlakhasanjian.shadow]

I wish you were my high school teacher 😊

[NaingJOURNEY]

Hello I'm a new Subscriber IT student

[_irfanahmad]

Please complete this playlist.

I want to learn ds, ml, ai.

[AlexandreSilva-qwpd]

Do you use an IPad for record this? What is the app?

[yapping_gaming]

i have question why do you make this type of question
, pls comment
🥺

[Gatehouse]

Hello Kylie,

I did this in Intro to A.I.

You know you should've responded to me when I first messaged to teach me how to talk to you. Now you seem mad I was flirting when I'm looking at how bombshell you are working on this the long way‼
Don't this just feed through a Neural Network⁉

[Mala-wpfg]

Wish I could have a smart girlfriend like you 😅

[mladenjanjic]

@KylieYYing Please, go for a PhD and become professor – for the sake of future generations of students
You are exceptionally talented for presenting complicated and demanding topics in a very clear and understandable way, so it should be Your mission to become university professor and provide new generations of student with an opportunity to attend the best AI/ML university course on Earth

You could also make invaluable contribution to the application of AI in astronomy, astrophysics, development of new propulsion technologies, etc. So, please, go for PhD studies 🌌