Developing Your Expertise in Machine Learning: Podcasts for Breadth vs. Depth

From ad hoc to highly professional, there’s no shortage of resources when it comes to learning Machine Learning. Not only should podcasts be blatantly regarded as both viable and valuable resources, the two I cover in this post present opportunities for improving your breadth and/or depth in Machine Learning.

Machine Learning Guide

As a component of his own process for ramping up his knowledge and skills in the area of Machine Learning, OCDevel’s Tyler Renelle has developed an impressive resource of some 30 podcasts. Through this collection of episodes, Tyler’s is primarily a breadth play when it comes to the matter of learning Machine Learning, though he alludes to depth as well in how he positions his podcasts:

Where your other resources provide the machine learning trees, I provide the forest. Consider me your syllabus. At the end of every episode I provide high-quality curated resources for learning each episode’s details.

As I expect you’ll agree, with Tyler’s Guide, the purely audio medium of podcasting permits the breadth of Machine Learning to be communicated extremely effectively; in his own words, Tyler states:

Audio may seem inferior, but it’s a great supplement during exercise/commute/chores.

I couldn’t agree more. Even from the earliest of those episodes in this series, Tyler demonstrates the viability and value of this medium. In my opinion, he is particularly effective for at least three reasons:

  1. Repetition – Extremely important in any learning process, regardless of the medium, repetition is critical when podcasting is employed as a tool for learning.
  2. Analogies – Again, useful in learning regardless of the medium involved, yet extremely so in the case of podcasting. Imagine effective, simple, highly visual and sometimes fun analogies being introduced to explain, for example, a particular algorithm for Machine Learning.
  3. Enthusiasm – Perhaps a no-brainer, but enthusiasm serves to captivate interest and motivate action.

As someone who’s listened to each and every one of those 30 or so episodes, I can state with some assuredness that: We are truly fortunate that Tyler has expended the extra effort to share what he has learned in the hope that it’ll also help others. The quality of the Guide is excellent. If anything, I recall occasionally taking exception to some of the mathematical details related by Tyler. Because Tyler approaches this Guide from the perspective of an experienced developer, lapses mathematical in nature are extremely minor, and certainly do not detract from the overall value of the podcast.

After sharing his Guide, Tyler started up Machine Learning Applied:

an exclusive podcast series on practical/applied tech side of the same. Smaller, more frequent episodes.

Unfortunately, with only six episodes starting from May 2018, and none since mid-July, this more-applied series hasn’t yet achieved the stature of its predecessor. I share this more as a statement of fact than criticism, as sustaining the momentum to deliver such involved content on a regular cadence is not achieved without considerable effort – and, let’s be realistic, more than just a promise of monetization.

This Week in Machine Learning and AI

Whereas OCDevel’s Guide manifests itself as a one-person, breadth play, This Week in Machine Learning and AI (TWiML&AI) exploits the interview format in probing for depth. Built upon the seemingly tireless efforts of knowledgeable and skilled interviewer Sam Charrington, TWiML&AI podcasts allow those at the forefront of Machine Learning to share the details of their work – whether that translates to their R&D projects, business ventures or some combination thereof.

Like Tyler Renelle, Sam has a welcoming and nurturing style that allows him to ensure his guests are audience-centric in their responses – even if that means an episode is tagged with a ‘geek alert’ for those conversations that include mathematical details, for example. As someone who engages in original research in Machine Learning, I have learned a lot from TWiML&AI. Specifically, after listening to a number of episodes, I’ve followed up on show notes by delving a little deeper into something that sounded interesting; and on more than a few occasions, I’ve unearthed something of value for those projects I’m working on. Though Sam has interviewed some of the most well known in this rapidly evolving field, it is truly wonderful that TWiML&AI serves as an equal-opportunity platform – a platform that allows voices that might otherwise be marginalized to also be heard.

At this point, Sam and his team at TWIML&AI have developed a community around the podcast. The opportunity for deeper interaction exists through meetups, for example – meetups that have ranged from focused discussion on a particularly impactful research paper, to a facilitated study group in support of a course. In addition to all of this online activity, Sam and his team participate actively in a plethora of events, and have even been known to host events in person as well.

One last thought regarding TWiML&AI: The team here takes significant effort to ensure that each of the 185 episodes (and counting!) is well documented. While this is extremely useful, I urge you not to merely make your decision on what to listen to based upon teasers and notes alone. Stated differently, I can relate countless examples for which I perceived a very low level of interest prior to actually listening to an episode, only to be both surprised and delighted when I did. As I recall well my from my running days, run for that first kilometre or so (0.6214 of a mile 😉 ) before you make the decision as to how far you’ll run that day.

From the understandably predictable essentials of breadth, to the sometimes surprising and delightful details of depth, these two podcasts well illustrate the complementarity between the schools of breadth and depth. Based upon my experience, you’ll be well served by taking in both of these podcasts – whether you need to jumpstart or engage-in-continuous learning. Have a listen.

Data Scientist: Believe. Behave. Become.

A Litmus Test

When do you legitimately get to call yourself a Data Scientist?

How about a litmus test? You’re at a gathering of some type, and someone asks you:

So, what do you do?

At which point can you (or me, or anyone) respond with confidence:

I’m a Data Scientist.

I think the responding-with-confidence part is key here for any of us with a modicum of humility, education, experience, etc. I don’t know about you, but I’m certainly not interested in this declaration being greeted by judgmental guffaws, coughing spasms, involuntary eye motion, etc. Instead of all this overt ‘body language’, I’m sure we’d all prefer to receive an inquiring response along the lines of:

Oh, just what the [expletive deleted] is that?

Or, at least:

Dude, seriously, did you like, just make that up?

Responses to this very-legitimate, potentially disarming question, will need to be saved for another time – though I’m sure a quick Google search will reveal a just-what-the-[expletive deleted]-is-Data-Scientist elevator pitch.

To return to the question intended for this post however, let’s focus for a moment on how a best-selling author ‘became’ a writer.

“I’m a Writer”

I was recently listening to best-selling author Jeff Goins being interviewed by podcast host Srini Rao on an episode of the Unmistakable Creative. Although the entire episode (and the podcast in general, frankly) is well worth the listen, my purpose here is to extract the discussion relating to Goins’ own process of becoming a writer. In this episode of the podcast, Goins recalls the moment when he believed he was a writer. He then set about behaving as a writer – essentially, the hard work of showing up every single day just to write. Goins continues by explaining how based upon his belief (“I am writer”) and his behavior (i.e., the practice of writing on a daily basis), he ultimately realized his belief through his actions (behavior) and became a writer. With five, best selling books to his credit, plus a high-traffic-blog property, and I’m sure much more, it’s difficult now to dispute Goins’ claim of being a writer.

Believe. Behave. Become. Sounds like a simple enough algorithm, so in the final section of this post, I’ll apply it to the question posed at the outset – namely:

When do you legitimately get to call yourself a Data Scientist?

I’m a Data Scientist?

I suppose, then, that by direct application of Goins’ algorithm, you can start the process merely by believing you’re a Data Scientist. Of course, I think we all know that that’ll only get you so far, and probably not even to a first interview. More likely, I think that most would agree that we need to have some Data Science chops before we would even entertain such an affirmation – especially in public.

And this is where my Data Science Portfolio enters the picture – in part, allowing me to self-validate, to legitimize whether or not I can call myself a Data Scientist in public without the laughing, choking or winking. What’s interesting though is that in order to work through Goins’ algorithm, engaging in active curation of a Data Science portfolio is causing me to work backwards – making use of hindsight to validate that I have ‘arrived’ as a Data Scientist:

  • Become – Whereas I don’t have best sellers or even a high-traffic blog site to draw upon, I have been able to assemble a variety of relevant artifacts into a Portfolio. Included in the Portfolio are peer-reviewed articles that have appeared in published journals with respectable impact factors. This, for a Data Scientist, is arguably a most-stringent validation of an original contribution to the field. However, chapters in books, presentations at academic and industry events, and so on, also serve as valuable demonstrations of having become a Data Scientist. Though it doesn’t apply to me (yet?), the contribution of code would also serve as a resounding example – with frameworks such as Apache Hadoop, Apache Spark, PyTorch, and TensorFlow serving as canonical and compelling examples.
  • Behave – Not since the time I was a graduate student have I been able to show up every day. However, recognizing the importance of deliberate practice, there have been extended periods during which I have shown up every day (even if only for 15 minutes) to advance some Data Science project. In my own case, this was most often the consequence of holding down a full-time job at the same time – though in some cases, as is evident in the Portfolio, I have been able to work on such projects as a part of my job. Such win-win propositions can be especially advantageous for the aspiring Data Scientist and the organization s/he represents.
  • Believe – Perhaps the most important outcome of engaging in the deliberate act of putting together my Data Science Portfolio, is that I’m already in a much more informed position, and able to make a serious ‘gut check’ on whether or not I can legitimately declare myself a Data Scientist right here and right now.

The seemingly self-indulgent pursuit of developing my own Data Science Portfolio, an engagement of active self-curation, has (quite honestly) both surprised and delighted me; I clearly have been directly involved in the production of a number of artifacts that can be used to legitimately represent myself as ‘active’ in the area of Data Science. The part-time nature of this pursuit, especially since the completion of grad school (though with a few notable exceptions), has produced a number of outcomes that can be diplomatically described as works (still) in progress … and in some cases, that is unfortunate.

Net-net, there is some evidence to support a self-declaration as a Data Scientist – based upon artifacts produced, and implied (though inconsistent) behaviors. However, when asked the question “What do you do?”, I am more likely to respond that:

I am a demonstrably engaged and passionate student of Data Science – an aspiring Data Scientist, per se … one who’s actively working on becoming, behaving and ultimately believing he’s a Data Scientist.

Based on my biases, that’s what I currently feel owing to the very nature of Data Science itself.