The Constant Multiple Rule for Derivatives

Continuing my short series on Differentiation Rules, today’s video covers the Constant Multiple Rule. This rule is often used in conjunction with the Power Rule, which was covered in the preceding video, released on Monday.

New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.

More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.

Data Community Content Creator Awards

I am surprised and utterly delighted to be recognized yesterday with the Data Community Content Creator Award for the "Machine Learning and AI" YouTube category. 🥳

From my perspective, my YouTube channel is still in its early days so while I did not anticipate formal recognition like this perhaps ever, I *certainly* did not so soon after launching the channel. This is a massive, galvanizing signal that I should continue pressing on with this nascent video-creation effort — I absolutely will!

First off, thank you to everyone who voted. This category was apparently one of the tightest races in this "Peoples' Choice"-style awards show, so truly your individual vote may have tipped the award in my favor.

Many thanks are due to Sangbin Lee and Maria Lee, who have edited, produced, branded, and marketed every single video on my channel since day one. My freely-available YouTube content would not exist without them. Thanks as well to Guillaume Rousseau, who recently joined us and dramatically accelerated how quickly we can publish perfectly-edited videos.

Finally, thanks to Harpreet Sahota and Kate Strachnyi who conceived of the DCCCA show and delivered it with the flair, fun, and precision that we'd expect from them!

The entire ceremony is on YouTube here. And a short recap post is here.

Performance Marketing Analytics

My guest this week is Kris Tait, who fills us in on how data and machine learning have transformed — and will continue to transform — marketing, enabling even small firms to effectively target customers and grow their revenue.

In this episode of the SuperDataScience show, we cover:
• What performance marketing is
• The rapidly shifting digital marketing ecosystem, as well as how data and ML can mitigate the risks associated with these changes
• The sweet spot for augmenting human marketers' skills with machines
• How any firm should define metrics to maximize return on marketing investment, thereby ensuring broader commercial success
• The most useful modern data science tools for global digital marketing

Kris is the managing director for the US at Croud - Performance Marketing Agency of the Year, an innovative marketing agency that is driven by data analytics and machine learning algorithms.

Listen or watch here.

The Power Rule for Derivatives

On Thursday, I published a video on the Constant Rule, the first video in a series on Differentiation Rules. Today, we continue the series with the Power Rule, arguably the most common and most important of all the rules.

New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.

More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.

Top Resume Tips

In recent weeks, I've received several messages from folks struggling to get callbacks for Data Scientist interviews. In reviewing their résumés, I realized there are five specific tips that I highly recommend adhering to.

You can listen or watch here.

The Derivative of a Constant

This and the next several videos will provide you with clear and colorful examples of all of the most important differentiation rules. We kick these rules off with the Constant Rule.

The derivative rules are critical to machine learning as they allow us to find the derivatives of cost functions. These cost-function derivatives are concatenated into the "gradient" that we descend to allow ML models to learn.

New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.

More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.

Announcing Nebula, Our New Machine Learning Company

Biggest milestone of my career so far: Delighted to announce a major capital raise that enables us to launch Nebula, a new machine learning company that reimagines talent management and recruitment.

In April, we announced the acquisition of untapt by Wynden Stark (blog post here). Now, Wynden Stark has completed a massive raise of growth capital from Corbel Capital Partners that makes the launch of Nebula possible. I couldn't be more excited about how this broadens and accelerates our ability to transform human capital industries.

As of today, I'm taking up the position of Chief Data Scientist at Nebula. I feel terrifically fortunate to be making this transition alongside:

• untapt's founder, Ed Donner, now Nebula CTO

• untapt's CEO, Gareth Moody, now Nebula's head of enterprise sales

• Long-standing untapt data scientists Andrew Vlahutin and Grant Beyleveld as well as the many outstanding recent hires to our quickly-growing team

• Every single one of untapt's software engineers as well as, again, many brilliant new hires across the engineering and product teams

• The world-leading recruiters and commercial leaders from GQR Global Markets, Wynden Stark's service arm, who serve in formal and informal advisory roles on Nebula's products

Congratulations to Wynden Stark's founders Steven Talbot and Hugo Sugden, as well as the rest of the firm, on realizing this significant stepping stone toward the even larger successes to come.

Press release here.

Knowledge Graphs

In this week's guest episode, wildly intelligent and meticulously communicative Maureen Teyssier, Ph.D. explains what Knowledge Graphs are, why they're so powerful, and how to grow a flourishing data science team.

In more detail, in today’s episode we cover:
• The theory and applications of Knowledge Graphs, a cool and powerful data type at the heart of much of Maureen’s work at Reonomy
• The data science techniques that Reonomy use to flow data through extremely high-volume pipelines, enabling them to efficiently apply models to their massive data sets
• What Maureen looks for in the data scientists that she hires and the tools and approaches she leverages in order to grow a highly effective data science team
• The differences between data scientists, data analysts, data engineers, and machine learning engineers.
• Maureen’s fascinating academic work in which she used gigantic supercomputers to simulate solar systems and galaxies

Maureen is Chief Data Scientist at Reonomy, a very well-funded New York start-up — they’ve raised over 100 million dollars — that is transforming the world of commercial real estate with data and data science. Prior to working in industry, Maureen was an academic working in the field of computational astrophysics; she obtained her PhD from Columbia University in the City of New York and then carried out research at Rutgers University in New Jersey.

Listen here.

Derivative Notation

In today's YouTube video, we detail all of the most common notation for derivatives. This lays the foundation for a fun, immediately forthcoming series of videos covering all of the major differentiation rules. Enjoy!

New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.

More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.

Five Keys to Success

I've recently been able to achieve markedly better results than ever before across my personal and professional lives. For Five-Minute Friday, I reflect on five keys to success that may allow achievement of many complex, long-term goals.

You can listen or watch here.

How Derivatives Arise from Limits

In today's video, we use hands-on code demos in Python to find the slopes of curves with the Delta Method. While finding these slopes, we derive together — from first principles — the most important Differential Calculus formula.

This video is part of a thematic segment of videos on Differentiation. In the forthcoming videos, we’ll cover derivative notation and a series of useful rules for differentiation.

New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.

More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.

How to Thrive as an Early-Career Data Scientist

Getting started in data science? Today's episode is for you! Sidney Arcidiacono is absolutely crushing her first year in the field; we discuss the options for getting started in the field and top tips for early-career success.

Trained as a phlebotomist (blood-sample collection), Sidney was inspired by the potential for machine learning to revolutionize healthcare, so she jumped feet first into a full-time computer science degree at Make School, specializing in the data science track. From no familiarity with code or models just a year ago, Sidney's immersion has paid off: She's now fluent in the modern data science software stack and landed a summer data science internship at GreenLight Biosciences, Inc., an RNA-molecule therapeutics firm (like the Pfizer/BioNTech/Moderna vaccines).

Sidney is terrifically sharp and engaging; I think you'll enjoy hearing from her as much as I did during filming.

Watch or listen here.

The Delta Method

In today's video, we use a Python code demo to develop a working understanding of the Delta Method, a centuries-old technique that enables us to determine the slope of a curve.

This video is the first from a thematic segment of videos on Differentiation. In Thursday's video, we'll build on what we covered today to derive — and deeply understand — the most common, most important equation in differential calculus.

New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.

More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.

Derivatives and Differentiation — Segment 2 of Subject 3, "Limits & Derivative

Today marks the beginning of a new thematic segment of videos in my ML Foundations series. This segment builds on the Limits content already covered to clearly illustrate how Differentiation works and how we find Derivatives.

Through a combination of color-coded equations, paper-and-pencil exercises, and hands-on Python code demos, the videos in this segment instill a deep understanding of how differentiation allows us to find derivatives.

More specifically, the videos cover:
• The Delta Method
• The Differentiation Equation
• Differentiation Notation
• Rules that enable us to quickly calculate the derivatives of a wide range of functions, including those found throughout machine learning

New videos are published every Monday and Thursday. The playlist for my Calculus for ML course is here.

More detail about my broader ML Foundations series and all of the associated open-source code is available in GitHub here.

Learn my ML Foundations Curriculum LIVE on O'Reilly

From July through December, I'll be offering my entire Machine Learning Foundations curriculum as a series of 14 live, interactive workshops via O'Reilly online. It'll be a lot of fun — would love to see ya there!

Each training is 3 hours long with an extra 30 minutes afterward for answering your questions and working through exercises together. The curriculum is filled with hands-on code demos in Python, particularly the NumPy, TensorFlow, and PyTorch libraries.

There are four subject areas:
• Linear Algebra (3 classes)
• Calculus (4 classes)
• Probability and Statistics (4 classes)
• Computer Science (3 classes)

All 14 training dates are provided at jonkrohn.com/talks.

Sign up opens about two months prior to each class; the first three classes are open for registration now at oreilly.com.

The 20% of Analytics Driving 80% of ROI

Today’s episode is with freakin' David Langer, people!! (So obviously it's brilliant, witty, and full of laughs.) He fills us in on the most powerful 20% of analytics — the analytics that drive 80% of companies’ return on investment.

Publishing under his Dave on Data brand, Dave's YouTube channel is top-notch, with several videos that have over a million views (and the thumbnails are hilarious; check 'em out). He is an exceptionally accomplished data scientist and software engineer, including spending nearly a decade at Microsoft's Global HQ, where his titles included principal software architect, principal data scientist, and director of analytics.

Topics in the episode include:

• Surprisingly powerful modeling approaches in spreadsheet tools like Excel

• The SQL databases we'll need if the data sets we're working with are too big for spreadsheets

• Why R programming is easy and should be our default language choice for moderate to advanced statistical analysis

• How companies can maximize value from machine learning

Listen or watch here.

Exercises on Limits

Final YouTube video from my thematic segment on Limits out today! It's a handful of comprehension exercises. Starting Thursday, we'll begin releasing videos from a new Calculus segment, on derivatives and differentiation.

We release new videos from my "Calculus for Machine Learning" course on YouTube every Monday and Thursday. The playlist is here.

The Machine Learning House

In last week’s Five-Minute Friday, I discussed how, in the data science field, the learning never stops. But there’s one big counterpoint: The foundational subjects that underlie the field barely change at all, decade after decade.

These subjects — linear algebra, calculus, probability, statistics, data structures, and algorithms — build a strong foundation for your “Machine Learning House”. Today's Five-Minute Friday articulates my perspective that investing time in studying these foundational subjects will reap great dividends throughout your data science career.

You can listen or watch here.