So, recently I decided to make a couple of videos on niche topics, namely the Business Aspect of A.I. in Data Science and  Extreme Learning Machines (ELMs). These vids are now available on Safari (here and here). Enjoy!

Note that in order to view these vids in their entirety you'll need a subscription to the platform. The latter enables you to view other materials, including a large variety of technical books as well as all my other videos. Cheers!

As a famous Chinese sage once said, "a car is more than the sum of its parts." It's intriguing how this applies not just to ancient vehicles in the Orient, but also to a special kind of data science models called ensembles. So, if you want to learn more about this fascinating topic and how it is useful in a data science setting, check out my latest video on the Safari platform.

Note that you will need a subscription to the Safari system in order to view this vid in its entirety. However, with such a subscription you'd be able to access a lot of other material on a variety of technical topics, including all my other videos. Cheers!

Recently I decided to do something a bit more experimental, which very few people have tried covering in a video. So, I tackled a more niche sub-topic of Natural Language Processing, related to custom-made features and their construction. Despite its seemingly simple nature, this skill is something that can differentiate you from a newcomer in NLP. This A.I. video assumes some knowledge of NLP but you don’t need to be a seasoned data scientist to follow. Also, I provide several examples, as well as an original taxonomy to help you organize all this information in your mind. So, check it out on Safari when you have a moment.

Note that a subscription to the Safari portal is required in order to view the video in its entirety. With the subscription you have access to a large number of books and videos, across various publishers and domains.

Over the past couple of weeks I've been thinking about this topic and gathering material about it. After all, unlike other more attractive aspects of A.I., this one still eludes the limelight, even though it's become quite popular as a research topic lately. Since I believe this is a matter that concerns everyone, not just those of us who are in the A.I. field, I created this video on the topic. It's a bit longer than the other ones on A.I. topics, but I made an effort to make it relate-able and avoid too many technical terms. So, if you have a Safari account, I invite you to check it out here.

This is a topic that I'm pretty confident hasn't been featured much anywhere in the pop data science literature. Although it is quite well-known in the research sphere, most non-PhDs (and some PhDs too!) may have never heard about it, or why it is useful in day-to-day data science work. So, if you are one of those people who are curious and interested in learning even the less popular topics of our field, feel free to check it out on Safari.

Note that although I made an effort to cover this subject from various angles, this is still an introduction video to its topics. Also, some experience in data science would be immensely useful, otherwise the video may appear a bit abstract. Whatever the case, I hope you find it useful and use it as a jumping board to new aspects of data science that you were not aware of. Cheers!

Blockchain has been making waves in the past 10 years or so, with many applications like BitCoin and other cryptocurrencies that have been developed on this platform. Yet, there is also alternative platforms like Hashgraph that promise to deliver the same services but in a more efficient manner. All these technologies are under the umbrella of Distributed Ledger Technologies and are particularly important in our era of pronounced cyber-security concerns. 

Recently I’ve put together a video on this topic that’s now available on Safari. It’s more high level but it covers all the key aspects of the technologies, making it ideal for someone new to the topic. What’s more, I’ve written a short article comparing the two technologies, on the DSP blog. Feel free to check them both out. Enjoy!

A/B testing is a crucial methodology / application in the data science field. Although it mainly relies on Statistics, it has a remained quite relevant in this machine learning and AI oriented era of our field.  It's no coincidence that in Thinkful that's one of the first things data science students learn, once they get comfortable with descriptive Stats and basic data manipulation. So, I decided to do a video on this topic to help those interested in learning about it get a good perspective of it and understand better its relationship with Hypothesis Testing.  It is my hope that this video can be a good supplement to one's learning on the subject. Enjoy!

A few years back, at a period I was both inspired to experiment with different Complex Systems and had enough time on my hands, I created this interesting variant of John Conway's Game of Life. As the beings in this model evolved, I named it the Game of Evolving Life. I ran a bunch of simulations on it and analyzed the results, a project that took the form of a whole ebook, which I never got around to publishing. Whatever the case, I thought this project would make a good example for the Complex Systems subtopic of the previous video's topic, so I made a video on it. This new video is now online on Safari. Enjoy!

Note that this video covers the main highlights of the model, with a very brief introduction to what complex systems are. Also, I focused on the more visual aspect of the analysis I'd done, otherwise it would be a much longer video that wouldn't be as interesting to most people. Finally, this whole thing was more of a programming exercise, so if you are looking at Data Science related videos that go into more depth on the methods of the craft, perhaps other videos would be better for you.

This past week I decided to do a vid on an experimental topic, involving different fields, an interdisciplinary topic if you will. I understand the risks of such a video, since randomness is not particularly easy as a subject, while complex systems are a bit niche as a field. However, I tried to bring about a more intuitive approach to all this and introduce a new feature for such videos: mini-quizzes so that you can test your understanding while you watch the video. Anyway, feel free to check out this introductory video to this topic by visiting the corresponding Safari page. Warning: some of the stuff covered in this video veers aways from conventional approaches to this topic. Also, the video is very light on the math aspect of the topic as otherwise it would be too long and it's already over 30 minutes in length...

Also, recently a viewer of this blog, S.M., contacted me with some suggestions on how to tackle certain typo-related issues he had found. Big thanks to S.M. for his contribution!

This past week I've had some time off work as my CEO was on vacation. As a result I did 2 videos, not just 1. Here they are:

The Bias-Variance Trade-Off: when you have a model that favors a certain class or a certain set of values, you have high bias, while you have a model whose predictions are all over the place, you have high variance. Could you find a compromise between the two? And how does all this relate to the model's fitness? This video includes a few examples too, for classification and regression problems, to cement the concepts introduced.

Backing Up and Wiping Out Sensitive Data: you probably have heard of this topic and perhaps even apply it to some extent, since taking care of sensitive data is a good cyber-security habit to have, plus it's not new either. However, there is much more to it than that, like which storage media are best for back-up, how you can handle sensitive data on your computer without leaving a trace, and what software is out there that helps make that happen.

Enjoy!