The End of Jobs

We’ve all heard the claim: “Robots will take our jobs.” But what actually happens when work is fundamentally changed by technology? Jeff Wald, author of The End of Jobs and co-founder of WorkMarket, joins me on this episode to discuss the future of our 9-to-5’s including how data science, automation, and other macroeconomic factors will reshape work around the globe in the coming decades.

Jeff’s work involves a careful examination of the data around the history of work, and builds on his own experience as co-founder of WorkMarket, a company focused on helping companies manage contractors. We discuss the pandemic, remote work, and the obligations of society towards the people caught up in the churn. 

Listening/viewing options, as well as full transcript, available here.

Communicating Data Effectively

Things can get silly when Kate and I get together 🙃 BUT we also covered a lot in this week’s guest episode of the SuperDataScience, including:

• The utter importance of communicating data effectively

• Concrete tips for doing so

• And how to build a giant social-media presence (like Kate's 150k following!)

Kate is the founder of Story by Data & DATAcated Academy, and author of four books including The Disruptors: Data Science Leaders and Journey to Data Scientist.

Listening/viewing options, as well as full transcript, available here.

ML Foundations: All Linear Algebra Videos Recorded, Calculus is Next

My Machine Learning Foundations tutorial series – as suggested by the diagram above – covers Linear Algebra, Calculus, Probability/Stats, and Computer Science (more detail on the series is available in GitHub).

Last month, with a big sigh of joy, I finished recording the last Linear Algebra video for the series, meaning that my filming journey for the first quarter of the ML Foundations series is now complete. I've recorded the footage three different ways:  

1. If you have a subscription to the O'Reilly learning platform, a studio-recorded version has been available since late December as my complete, standalone Linear Algebra for Machine Learning curriculum. 

2. If you have a subscription to the Ai+ training platform, live recordings of my entire linear algebra curriculum are available to view there on-demand as of January. 

3. The first half of my linear algebra content is available on my YouTube channel and via my Udemy course today. This is the version I finished filming most recently. My producer Sangbin is currently editing these videos and I'll post here as they’re released (I recommend signing up for my email newsletter on my homepage to be pushed notifications).

With Linear Algebra in my rear-view mirror, I’m currently tackling the Calculus content. The studio-recorded version of my calculus materials is currently available as a “sneak peak” in O'Reilly. I'm teaching my calculus materials live in the Ai+ platform from now through February 24th. And calculus videos should be published on YouTube and in my Udemy course in March and April. 

Deep Learning for Machine Vision

In the latest SuperDataScience episode, the brilliant A.I. researcher Deblina Bhattacharjee fills me in on the state of the art in machine vision. From predicting earthquakes to 3D VR experiences based on ordinary 2D art, she's seemingly in on it all!

We also covered:

• Automatic detection of biological cells in medical images

• The typical workday of, and software tools used by, A.I. researchers working at the cutting edge

• The critical math subjects necessary to be an outstanding machine learning practitioner (to my delight, she reeled off the subjects covered in my ML Foundations curriculum)

• The ever-increasing utility of unsupervised learning approaches

• Her top productivity tips (Deblina is unbelievably productive so I greatly value her two cents on this!)

• Prog rock music

• The intelligence of plants (yep, plants!)

This is an outstanding episode. I learned a ton while filming and had a lot of laughs too, so I think you'll enjoy it too.

Listening/viewing options, as well as full transcript, available here.

Data Science at a World-Leading Hedge Fund

This week's guest episode of SuperDataScience is wicked. Dr Claudia Perlich joined me to fill us in on data science at Two Sigma, how to win global data science competitions, and how to have an extraordinary career in any field.

We also talked about data science applied to financial markets in general and what the world's largest hedge funds (e.g., Two Sigma) look for in the data talent they hire.

The full episode is available here.

Scaling Up Machine Learning

Erica Greene, engineering manager at Etsy, joins me to discuss production machine learning applications. This is a great episode for practicing data scientists as well as ML engineer beginners! Topics include:

• Choosing the correct production ML software library

• Critical areas of expertise ML engineers should strive to master

• Setting up guardrails to avoid (frightening!) feature drift

• Whether a PhD is advantageous if you apply ML in industry

Loved having Erica on the episode because I learned a lot and had a great time. Full Transcript available here.

Data Science Trends for 2021

The brilliant Ben Taylor joined us for a record fourth time on the SuperDataScience Podcast. We covered 2021's big trends, including:

• Race and gender biases in AI

• Measuring a return on data science investment

• Understanding black-box algorithms

Full episode here.

Now Hosting the SuperDataScience Podcast

Happy new year! I'm delighted to announce that I've taken over the reigns as host of the SuperDataScience podcast as of the January 1st episode (#432).

In November, Kirill Eremenko blew me away by asking me if I'd be interested in hosting the program and of course I immediately said, "yes!"

By releasing two riveting episodes per week since 2016, Kirill has amassed an extraordinary global audience of 10,000 listeners per episode. I'm over the moon to have the opportunity to share cutting-edge content from the fields of data science, machine learning, and A.I. with so many engaged professionals and students.

Kirill left behind super-sized shoes to fill, but I'm committed to maintaining the lofty standard that he set. I'll also be maintaining the structure that preceded me:

• Odd-numbered episodes feature guests and are released every Wednesday. I've already recorded fun episodes packed with practical insights from Ben Taylor is..., Erica Greene, and Claudia Perlich, with many more "household" data science names lined up.

• Even-numbered episodes, like #432, are "Five-Minute Fridays". These are short, come out every Friday (duh), and focused on a specific item of data science or career advice.

With a challenging 2020 behind us, I hope you're as excited as I am to be starting 2021 off with something new.

Co-Hosted SuperDataScience Podcast on 2020's Biggest Breakthroughs

Alongside Kirill Eremenko, Founder and CEO of SuperDataScience, I co-hosted this podcast episode on 2020’s biggest machine learning breakthroughs including:

• AlphaFold 2

• GPT-3

• The latest GPUs

We also announced an exciting upcoming podcast-host transition for 2021! Further details to come on air during the next podcast episode.

Co-Hosted SuperDataScience Podcast Episode

I had the honor of co-hosting SuperDataScience podcast episode #427 with the podcast's founder and host, Kirill Eremenko. The guest on the episode was Syafri Bahar, who is the director of data science at Gojek, a decacorn ($10+ billion-dollar valuation) "super app". The discussion was wide-ranging but we returned time and again to the concept of using data science and technology to make a positive social impact.

"Foundations for Machine Learning" Live Online Bootcamp

On Thursday, I kick off a live, 14-lecture bootcamp on the four foundational subjects underlying machine learning theory:

1. Linear Algebra
2. Calculus
3. Probability and Statistics
4. Computer Science

All of the bootcamp details -- including lecture dates, a detailed topic-by-topic syllabus, and an introductory video -- are available here.

The premise of the bootcamp is that to be an outstanding data scientist or ML engineer, it doesn't suffice to only know how to use ML algorithms via the abstract interfaces that the most popular libraries (e.g., scikit-learn, Keras) provide. To train innovative models or deploy them efficiently in production, an in-depth appreciation of machine learning theory (pictured as the central, purple floor in my metaphorical house diagram; see below) is required. And, to cultivate such in-depth appreciation of ML, one must possess a working understanding of the four foundational subjects.

When the foundations of the "Machine Learning House" are firm, it also makes it much easier to make the jump from general ML principles (purple floor) to specialized ML domains (the top floor, shown in gray) such as deep learning, natural language processing, machine vision, and reinforcement learning. This is because, the more specialized or cutting-edge the application, the more likely its details for implementation are available only in academic papers or graduate-level textbooks, either of which typically assume an understanding of the four foundational subjects.

In any event, all 14 lectures of the bootcamp are included as part of a subscription to the AI+ Training platform that was launched earlier this year by the Open Data Science Conference (ODSC). Through the platform, you also get unlimited access to recordings of the lectures so you can brush up anytime or attend lectures that you miss.

I love offering online lectures because I get to meet intelligent, ambitious people from all over the world. They're also great for students because of the interactivity. Speaking of which, the course will be filled with paper-and-pencil exercises and we'll work through the solutions together. On top, I've included hundreds of hands-on code demos in Python, with a particular focus on low-level operations in the PyTorch and TensorFlow libraries.

All of the code is available open-source in GitHub now.

Filming "Calculus for Machine Learning"

Same shirt, different day with my "weekend crew". We burnt the midnight oil Friday through Sunday last weekend filming what will be eight hours of interactive videos on Calculus for Machine Learning.

In the above photo, pictured from left to right at New York's Production Central Studios: myself, technician Guillaume Rousseau, and producer Erina Sanders.

All of the code (featuring the Python libraries NumPy, TensorFlow, and PyTorch) is available open-source in GitHub today.

The videos themselves will appear in the O'Reilly learning platform later this year, with thanks to Pearson's Debra Williams Cauley for bringing another project concept of mine to life.

Deep Q-Learning Networks tutorial on YouTube

Here's a free 72-minute intro to Deep Reinforcement Learning with Deep Q-Learning Networks. It's my most popular video, with 44k views. I'd never have predicted it to do so well, which goes to show how important it is to produce on a schedule.

Don't wait for inspiration; it's unlikely to ever come. Simply commit to regular deadlines and produce, produce, produce because some content you release that you think is rushed, that won't be interesting to your audience, will end up surprising you by being right on the money.

As an added benefit, studies show that focusing on quantity over quality paradoxically tends to result in significantly higher quality products.

Anyway, enjoy the video, which I've never shared on my website before. It uses hands-on code demos in Python to bring the mathematical theory of Deep Q-Learning to life (Jupyter notebook is here).

Free Launch of Udemy Course

My first Udemy course, Machine Learning and Data Science Foundations Masterclass, went live on Monday and is free (for now)! An astounding 20,000 students signed up in the first 24 hours and they've given it a five-star average rating.

The course is currently two hours long because Udemy has a two-hour cap on free courses. In about a month, we'll add four more hours of content, which will put us well over the two-hour cap. From that point on, the course will be paid in Udemy, but anyone who enrolled while the course was free will automatically get unlimited access to all of the paid-tier content.

Eventually the course will grow to be ~30 hours long and provide a comprehensive overview of all the subjects -- linear algebra, calculus, probability, and computer science -- that underlie modern machine learning. Again, by signing up now, all of that content will be available to you for free when it comes out; no strings attached!

Many thanks to the super-duper SuperDataScience Team (especially Kirill Eremenko, Leonid Golub and Roberto Lambertini) for partnering with me on this course. And thanks as ever to Sangbin Lee, who flawlessly produced and edited all of the video content.

Filming "Linear Algebra for Machine Learning" Videos

Back in studio this past weekend with magical producer Erina Sanders, creating seven hours of interactive Linear Algebra for Machine Learning videos.

All of the code (featuring the Python libraries NumPy, TensorFlow, and PyTorch) is available open-source in GitHub today.

The videos themselves will appear in the O'Reilly learning platform later this year, with thanks to Pearson's Debra Williams Cauley for bringing another project concept of mine to life.

(December 2020 update: These videos are now live in O'Reilly here.)

"Intro to Linear Algebra": The First Subject of my ML Foundations Series is Live!

Intro to Linear Algebra, the first subject of my Machine Learning Foundations tutorial series, is now available in its entirety! It's three content-rich hours, featuring code demos and exercises, split over a total of 24 YouTube videos.

I released the final Intro to Linear Algebra videos over the past week, all of which are from the Matrix Properties segment of the subject (see the segment intro video below):

• Topic 18: The Frobenius Norm
• Topic 19: Matrix Multiplication
• Topic 20: Symmetric and Identity Matrices
• Topic 21: Matrix Multiplication Exercises
• Topic 22: Matrix Inversion
• Topic 23: Diagonal Matrices
• Topic 24: Orthogonal Matrices

The YouTube playlist for the entire Machine Learning Foundations series is here.

The series is full of hands-on code demos in Python (particularly the NumPy, TensorFlow, and PyTorch libraries) and all of the code is available open-source in GitHub.

Please let me know what you think of the series so far as it will shape my creation of the remaining seven subjects. Up next in the series will be the second subject, Linear Algebra II: Matrix Operations. Stay tuned!