In recent weeks, my YouTube videos have covered Probability concepts like Events, Sample Spaces, and Combinatorics. Today's video features exercises to test and cement your understanding of those concepts.
We will publish a new video from my "Probability for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
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Combinatorics
Combinatorics is a field of math devoted to counting. In this week's YouTube video, we use examples with real numbers to bring Combinatorics to life and relate it to Probability Theory.
We will publish a new video from my "Probability for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
Multiple Independent Observations
In this week's YouTube tutorial, we consider probabilistic events where we have multiple independent observations — such as flipping a coin two or more times instead of just once.
We will publish a new video from my "Probability for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
Events and Sample Spaces
In this week's YouTube tutorial, I introduce the most fundamental atoms of probability theory: events and sample spaces. Enjoy 😀
We will publish a new video from my "Probability for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
What Probability Theory Is
This week, we start digging into the actual, uh, theory of Probability Theory. I also highlight the field's relevance to Machine Learning and Statistics. Enjoy 😀
We will publish a new video from my "Probability for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
A Brief History of Probability Theory
This week's YouTube video is a quick introduction to the fascinating history of Probability Theory. Next week, we'll actually start digging into Probability Theory, uh, theory 😉
We will publish a new video from my "Probability for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
Probability & Information Theory — Subject 5 of Machine Learning Foundations
Last Wednesday, we released the final video of my Calculus course, so today we begin my all-new YouTube course on Probability and Information Theory. This first video is an orientation to the course curriculum, enjoy!
We will publish a new video from my "Probability for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
My Favorite Calculus Resources
It's my birthday today! In celebration, I'm delighted to be releasing the final video of my "Calculus for Machine Learning" YouTube course. The first video came out in May and now, ten months later, we're done! 🎂
We published a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday since May 6th, 2021. So happy that it's now complete for you to enjoy. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Probability, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
Starting next Wednesday, we'll begin releasing videos for a new YouTube course of mine: "Probability for Machine Learning". Hope you're excited to get going on it :)
Finding the Area Under the ROC Curve
In this week's tutorial, we use Python code to find the area under the curve of the receiver operating characteristic (the "ROC curve"). This is a machine learning-specific application of integral calculus.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
This is the penultimate video in my Calculus course! After ten months of publishing it, the final video will be released next week :)
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Definite Integral Exercise
My recent videos have covered how to find Definite Integrals manually as well as how to find them computationally using Python code. This week's video is an exercise that tests comprehension of both approaches.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Numeric Integration with Python
Having detailed how to integrate equations by hand over the past few weeks, this week's video tutorial uses Python code to introduce how to find Definite Integrals computationally — and therefore automatically.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Definite Integrals
In recent weeks, my videos introduced Indefinite Integration. Today, we go a step further to calculate *Definite* Integrals. This allows us to find the area under a curve, which is essential for many machine learning models.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Indefinite Integral Exercises
If you watched last week's video on the integral calculus rules (or if you already feel confident about them!), you can use this week's video to test your comprehension of the topic.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
The Integral Calculus Rules — Topic 86 of Machine Learning Foundations
Integral Calculus Rocks! I mean, Integral Calculus Rules! I mean, this video covers the Integral Calculus Rules 😉... namely, the Power Rule, the Constant-Multiple Rule, and the Sum Rule.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
What Integral Calculus Is
What is Integral Calculus and why is it essential to Machine Learning? This week's video answers those questions while also explaining how integral calculus works at a high level and detailing its characteristic notation.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
The Receiver-Operating Characteristic (ROC) Curve
In this video, we work through a simple example — with real numbers — to demonstrate how to calculate the ROC Curve, an enormously useful metric for quantifying the performance of a binary classification model.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Gradient Descent (Hands-on with PyTorch)
In my preceding YouTube videos, we detailed exactly what the gradient of cost is. With that understanding, today we dig into what it means to *descend* this gradient and fit a machine learning model.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.