Sports Analytics and 66 Days of Data with Ken Jee

Ken Jee — sports analytics leader, originator of the ubiquitous #66daysofdata hashtag, and data-science YouTube superstar (190k subscribers) — is the guest for this week's fun and candid episode ⛳️🏌️

In addition to his YouTube content creation, Ken:
• Is Head of Data Science at Scouts Consulting Group LLC.
• Hosts the "Ken's Nearest Neighbors" podcast.
• Is Adjunct Professor at DePaul University.
• Holds a Masters in Computer Science with an AI/ML concentration.
• Is renowned for starting #66daysofdata, which has helped countless people create the habit of learning and working on data science projects every day.

Today’s episode should be broadly appealing, whether you’re already an expert data scientist or just getting started.

In this episode, Ken details:
• What sports analytics is and specific examples of how he’s made an impact on the performance of athletes and teams with it.
• Where the big opportunities lie in sports analytics in the coming years.
• His four-step process for how someone should get started in data science today.
• His favorite tools for software scripting as well as for production code development.
• How the #66daysofdata can supercharge your capacity as a data scientist whether you’re just getting started or are already an established practitioner.

Thanks to Christina, 🦾 Ben, Serg, Arafath, and Luke for great questions for Ken!

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

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.

The Statistics and Machine Learning Quests of Dr. Josh Starmer

Holy crap, it's here! Joshua Starmer, the creative genius behind the StatQuest YouTube channel (over 675k subscribers!) joins me for an epic episode on stats, ML, and his learning and communication secrets.

Dr. Starmer:
• Provides uniquely clear statistics and ML education via his StatQuest You Tube channel.
• Is Lead A.I. Educator at Grid.ai, a company founded by the creators of PyTorch Lightning that enables you to take an ML model you have on your laptop and train it seamlessly on the cloud.
• Was a researcher at the University of North Carolina at Chapel Hill for 13 years, first as a postdoc and then as an assistant professor, applying statistics to genetic data.
• Holds a PhD in Biomathematics and Computational Biology.
• Holds two bachelor degrees, one in Computer Science and another in Music.

In this episode filled with silliness and laughs from start to finish, Josh fills us in on:
• His learning and communication secrets.
• The single tool he uses to create YouTube videos with over a million views.
• The software languages he uses daily as a data scientist.
• His forthcoming book, "The StatQuest Illustrated Guide to Machine Learning".
• Why he left his academic career.
• A question you might want to ask yourself to check in on whether you’re following the right life path yourself.

Today’s epic episode is largely high level and so will appeal to anyone who likes to giggle while hearing from one of the most intelligent and creative minds in education on data science, machine learning, music, genetics, and the intersection of all of the above.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Thanks to Serg, Nikolay, Phil, Jonas, and Suddhasatwa for great audience questions!

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.

Deep Reinforcement Learning — with Wah Loon Keng

For an intro to Deep Reinforcement Learning or to hear about the latest research and applications in the field (which is responsible for the most cutting-edge "A.I."), today's episode with Wah Loon Keng is for you.

Keng:
• Co-authored the exceptional book "Foundations of Deep Reinforcement Learning" alongside Laura Graesser.
• Co-created SLM-Lab, an open-source deep reinforcement learning framework written with the Python PyTorch library.
• Is a Senior A.I. Engineer at AppLovin, a marketing solutions provider.

In this episode, Keng details:
• What reinforcement learning is.
• A timeline of major breakthroughs in the history of Reinforcement Learning, including when and how Deep RL evolved.
• Modern industrial applications of Deep RL across robotics, logistics, and climate change.
• Limitations of Deep RL and how future research may overcome these limitations.
• The industrial robotics and A.I. applications Deep RL could play a leading role in in the coming decades.
• What it means to be an A.I. engineer and the software tools he uses daily in that role.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

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.

Engineering Natural Language Models — with Lauren Zhu

Zero-shot multilingual neural machine translation, how to engineer natural language models, and why you should use PCA to choose your job are topics covered this week by the fun and brilliant Lauren Zhu.

Lauren:
• Is an ML Engineer at Glean, a Silicon Valley-based natural language understanding company that has raised $55m in venture capital.
• Prior to Glean, she worked as an ML Intern at both Apple and the autonomous vehicle subsidiary of Ford Motor Company; as a software engineering intern at Qualcomm; and as an A.I. Researcher at The University of Edinburgh.
• Holds BS and MS degrees in Computer Science from Stanford
• Served as a teaching assistant for some of Stanford University’s most renowned ML courses such as "Decision Making Under Uncertainty" and "Natural Language Processing with Deep Learning".

In this episode, Lauren details:
• Where to access free lectures from Stanford courses online.
• Her research on Zero-Shot Multilingual Neural Machine Translation.
• Why you should use Principal Component Analysis to choose your job.
• The software tools she uses day-to-day at Glean to engineer natural language processing ML models into massive-scale production systems.
• Her surprisingly pleasant secret to both productivity and success.

There are parts of this episode that will appeal especially to practicing data scientists but much of the conversation will be of interest to anyone who enjoys a laugh-filled conversation on A.I., especially if you’re keen to understand the state-of-the-art in applying ML to natural language problems.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

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.

How Genes Influence Behavior — with Prof. Jonathan Flint

How do genes influence behavior? This week's guest, Prof. Jonathan Flint, fills us in, with a particular focus on how machine learning is uncovering connections between genetics and psychiatric disorders like depression.

In this episode, Prof. Flint details:
• How we know that genetics plays a role in complex human behaviors incl. psychiatric disorders like anxiety, depression, and schizophrenia.
• How data science and ML play a prominent role in modern genetics research and how that role will only increase in years to come.
• The open-source software libraries that he uses for data modeling.
• What it's like day-to-day for a world-class medical sciences researcher.
• A single question you can ask to prevent someone committing suicide.
• How the future of psychiatric treatments is likely to be shaped by massive-scale genetic sequencing and everyday consumer technologies.

Jonathan:
• Is Professor-in-Residence at the University of California, Los Angeles, specializing in Neuroscience and Genetics.
• Leads a gigantic half-billion dollar project to sequence the genomes of hundreds of thousands of people around the world in order to better understand the genetics of depression.
• Originally trained as a psychiatrist, he established himself as a pioneer in the genetics of behavior during a thirty-year stint as a medical sciences researcher at the University of Oxford.
• Has authored over 500 peer-reviewed journal articles and his papers have been cited an absurd 50,000 times.
• Wrote a university-level textbook called "How Genes Influence Behavior", which is now in its second edition.

Today’s episode mentions a few technical data science details here and there but the episode will largely be of interest to anyone who’s keen to understand how your genes influence your behavior, whether you happen to have a data science background or not.

Thanks to Mohamad, Hank, and Serg for excellent audience questions!

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

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.

Scaling Data-Intensive Real-Time Applications — with Matthew Russell

This week's guest is indefatigable Matthew Russell. An Air Force veteran and author of four data science books, Matthew is now Founder/CEO of Strongest AI, a leading tech platform for fitness.

In this episode, Matthew covers:
• The tech stack he uses to make it possible to provide data from fitness competitions to millions of spectators all over the world in real-time.
• How he rapidly tests machine learning models for deployment into portable devices like the iPhone and the Apple Watch.
• Multi-objective ML functions and why they’re so widely useful in real-world applications.
• The three critical traits he looks for in anyone he hires.
• The values instilled in him by pursuing a military education.
• The key skills he wishes he’d learned earlier in his career.

A bit more on Matthew... he's:
• Founder and CEO of Strongest, the leading technology platform for global fitness events, which is growing into an application that uses ML models to make you stronger, faster, and fitter than ever before.
• Author of four books published by O'Reilly Media, including the classic "Mining the Social Web", which is now in its third edition.
• Prior to founding Strongest, served as CTO at several firms.
• Holds a BS in Computer Science from the US Air Force Academy as well as an MS in Computer Science and Machine Learning from the US Air Force Institute of Technology.

Parts of today’s episode, particularly in the first half, do get fairly technical as we dig into the open-source software stack that enables the scalable deployment of data-intensive real-time applications. That said, much of the episode will appeal to anyone who’s excited about physical fitness or commercializing A.I.

Shout out to Austin Ogilvie for introducing me to Matthew 😀

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

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.

Sparking A.I. Innovation — with Nicole Büttner

Looking for ideas on how to spark A.I. innovation in your organization? Nicole Büttner, the eloquent and effervescent Founder/CEO of Merantix Labs, has concrete A.I. innovation frameworks for you in this week's guest episode.

Merantix Labs is a renowned Berlin-based consultancy that enables companies to unlock the value of A.I. across all industries.

In addition to being Founder and CEO of Merantix Labs, Nicole:
• Is a member of the Management Board of Merantix Labs’ parent company Merantix, an A.I. Venture Studio that has raised $30m in funding from the likes of SoftBank Group Corp. to serially originate successful ML start-ups.
• Holds a Masters in Quantitative Economics and Finance from the University of St.Gallen, the world’s leading German-language business school.
• Was a visiting researcher in Economics at Stanford University.

In this episode, Nicole details:
• What an A.I. Venture Studio is and how she founded a thriving A.I. consultancy within it
• How to spark A.I. innovation in a company of any size
• How to effectively use the unlabelled, unbalanced data sets that abound in business
• How to engineer reusable data and software components to tackle related projects efficiently
• The three distinct types of founders she looks for when she puts together the founding team of an A.I. start-up

Today’s episode touches on a few technical details here and there but the episode will largely be of interest to anyone who’s keen to make the most of A.I. innovation in a commercial organization, whether you happen to have a deep technical background today or not.

Special shout-out to the St. Gallen Symposium (Svenja, Rolf), which Nicole and I discuss our love for (as well as how you can get free flights, accommodation, and access — deadline to apply is Feb 1) starting at the 34-minute mark.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

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.

Data Observability — with Dr. Kevin Hu

This week's guest is the fun and wildly intelligent entrepreneur, Kevin Hu, PhD. Inspired by his doctoral research at MIT, he co-founded Metaplane, a Y-Combinator-backed data observability platform.

In a bit more detail, Kevin:
• Is Co-Founder/CEO of Metaplane, a platform that observes the quality of data flows, looks for abnormalities in the data, and reports issues
• Completed a PhD in machine learning and data science from the Massachusetts Institute of Technology

In this episode, Kevin covers:
• What data observability is and how it can help identify data quality issues immediately as well as more quickly resolve the source of the issue
• His PhD research on automating data science systems using ML
• How he identified the problem his start-up Metaplane would solve
• His experience in Y-Combinator accelerating Metaplane
• Pros and cons of an academic career relative to the start-up hustle
• The surprising complexity of the software tools he uses daily as a CEO
• What he looks for in the data engineers that he hires

This episode gets a little technical here and there but I think Kevin and I were pretty careful to define technical concepts when they came up, so today’s episode should largely be appealing to anyone who’s keen to learn a lot from a brilliant entrepreneur, especially if you’d like to found or grow a data science start-up yourself. Enjoy!

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

The Confusion Matrix

This video is a quick introduction to the Confusion Matrix, which thankfully really isn’t all that confusing! Understanding what the Confusion Matrix is is key to an Integral Calculus application coming up shortly in this video series.

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.

Interpretable Machine Learning — with Serg Masís

This week's guest is Serg Masís, an absolutely brilliant data scientist who's specialized in modeling crop yields and climate change. He's also a world-leading author and expert on Interpretable Machine Learning.

Serg:
• Is a Climate & Agronomic Data Scientist at Syngenta.
• Wrote "Interpretable Machine Learning with Python", an epic hands-on guide to techniques that enable us to interpret, improve, and remove biases from ML models that might otherwise be opaque black boxes.
• Holds a Data Science Masters from the Illinois Institute of Technology.

In this episode, Serg details:
• What Interpretable Machine Learning is.
• Key interpretable ML approaches we have today / when they're useful.
• Social and financial ramifications of getting model interpretation wrong.
• What agronomy is and how it’s increasingly integral to being able to feed the growing population on our warming planet.
• What it’s like to be a Climate & Agronomic Data Scientist day-to-day and why you might want to consider getting involved in this fascinating, high-impact field.
• His productivity tips for excelling when you have as many big commitments as he does.

Today’s episode does get technical in parts but Serg and I made an effort to explain many technical concepts at a high level where we could, so today’s episode should be equally appealing to both practicing data scientists and anyone who’s keen to understand the importance and impact of interpretable ML or agronomic data science. Enjoy!

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Deep Learning's Neurobiology Origins

Interested in understanding what Deep Learning is? Here's a ten-minute talk I gave last month that visually explains the approach, how it originated in neuroscience research, and how it's now leading the A.I. revolution.

Thanks to Kate Strachnyi and Ravit Jain for organizing the fun "Holiday Book Party" that I gave this talk at. And thanks to Aglae Bassens for the stunning illustrations I used throughout.