Video Index

There are two ways to see all of my videos and navigate them. Probably the best way is to use this Learney Flow Chart, which was created by my friends at Learney.me. What makes it so awesome is that you can easily pick the general topic you are interested in and then see all of the relevant videos and their dependencies. Alternatively, you can find everything right here, just not as well organized.

This page contains links to playlists and individual videos on Statistics, Statistical Tests, Machine Learning, Webinars and Live Streams, organized, roughly, by category. Generally speaking, the videos are organized from basic concepts to complicated concepts, so, in theory, you should be able to start at the top and work you way down and everything will make sense.

Playlists:

  • Statistics Fundamentals – These videos give you a general overview of statistics as well as a be a reference for statistical concepts. Topics include:
    • Histograms
    • What is a statistical distribution?
    • And many more!!!
  • Linear Regression and Linear Models – These videos teach the basics relating to one of statistics most powerful tools.  Linear Regression and Linear Models allow us to use continuous values, like weight or height, and categorical values, like favorite color or favorite movie, to predict a continuous value, like age.
  • Logistic Regression – These videos pick up where Linear Regression and Linear Models leave off. Now, instead of predicting something continuous, like age, we can predict something discrete, like whether or not someone will enjoy the 1990 theatrical bust Troll 2.
  • Machine Learning – Linear Models and Logistic Regression are just the tips of the machine learning iceberg. There’s tons more to learn, and this playlist will help you trough it all, one step at a time.
  • Neural Networks – Everything you need to know, from the basics, all the way to image classification with Convolutional Neural Networks, presented one step at a time so that it is easily understood.
  • High Throughput Sequence Analysis – If you do high-throughput sequence analysis, this playlist is for you!
  • Statistics in R – If you want to do any of this stuff in R, this playlist is for you, and you only. No one else is allowed to watch it.
  • #66DaysOfData – If you want to participate in Ken Jee’s #66DaysOfData and are having trouble thinking of new stuff to learn, here’s a playlist that covers everything from the basics to the fancy stuff.

Statistics Fundamentals:

Statistical Tests:

Machine Learning and Dealing with large datasets that have lots and lots of measurements per sample:

(NOTE: All of the linear model and curve fitting stuff in the “Basics” section is also considered Machine Learning, so make sure you check out those videos).

Webinars

High-throughput Sequencing Analysis:

Live Streams:

  • 2020-01-06
    • 0:00 Introduction
    • 1:52 Comment #1 – What’s the difference between a single sample of 20 measurements and 4 samples of 5 measurements each.
    • 6:20 Comment #2 – Is machine learning a subset of statistics?
    • 9:59 Comment #3 – A poem!!!
    • 10:51 Viewer Questions/Comments Also, here’s the link to the Introduction to Statistical Learning: http://faculty.marshall.usc.edu/gareth-james/ISL/
  • 2020-01-20
    • 0:00 Introduction
    • 1:04 Comment #1 – What is your favorite machine learning algorithm
    • 4:40 Comment #2 – What is data leakage in machine learning?
    • 8:39 Comment #3 – Where do you learn these nitty gritty details?
    • 13:37 Live Question #1 – R-squared and Adjusted R-squared
    • 17:23 Live Question #2 – How are the videos arranged on https://statquest.org/video-index/ (simple to complex)
    • 18:26 Live Question #3 – Is it important to learn all of the formulas and equations even though we have advanced software that does the work?
  • 2020-02-03
    • 0:00 Silly Song and Introduction
    • 0:18 A big huge announcement
    • 3:14 Question #1 – Do we use statistical models to predict or explain stuff?
    • 8:31 Question #2 – Can you show the effects of regularization?
    • 9:42 My cat, Poe
    • 15:04 Question #3 – How do I choose the best machine learning algorithm for my data?
    • 21:17 Live Questions
  • 2020-02-17
    • 0:00 Silly Song and Introduction
    • 0:46 Question #1 – What do we do with imbalanced data?
    • 13:24 Question #2 – Post-Hoc Tests for ANOVA
    • 22:11 Live Questions
  • 2020-03-02
    • 0:00 Silly Song
    • 1:35 Question #1 – What is the best model for the corona virus epidemic?
    • 4:34 Question #2 – Two types of p-hacking explained and what to do about them:
    • 20:31 Live Questions:
  • 2020-03-16 – Naive Bayes
    • 0:00 Silly Song and Introduction
    • 2:02 Naive Bayes
  • 2020-04-06 – Gaussian Naive Bayes
  • 2020-04-20 – Expected Values (NOTE: There is now a full StatQuest video on Expected Values that revises and updates this material). 
  • 2020-05-04 – Conditional Probability
  • 2020-05-18 – Bayes’ Theorem
  • 2020-06-01 – Hypothesis Testing
  • 2020-06-15 – Bootstrapping Main Ideas