Lately I worked on a new series of videos, this time on Optimization. This A.I. methodology is a very popular one these days, one that adds a lot of value to both data science and other processes where resources are handled. Specifically, I talk about:
* Optimization in general (including its key applications)
* Particle Swarm Optimization
* Genetic Algorithms
* Simulated Annealing
* Optimization ensembles
* some auxiliary material that supplements these topics
You can find this video series on Safari, along with my other A.I. videos. Cheers!
Interestingly, the video throughput on Safari has increased lately so we don't have to wait too long before a video gets approved and published. This little guy, for example, I just finished on Thursday and it's already online at the Safari platform. It's by no means an exhaustive survey of the ML field, which is much larger than many people think and it doesn't include A.I. methods only. This video is more of an overview of ML and how it relates to other aspects of Data Science, such as Statistics, A.I., and various applications. So, if you are new to Data Science or want to get a comprehensive overview of the topic to supplement your studies of the subject, feel free to check it out!
With all the plethora of material out there for data science education, it is easy to get overwhelmed and even confused about what to study and how much time, money, and effort to put into it. Enter evaluation of data science material, a concise strategy for tackling this issue. In this 24 minute video, I talk about the various aspects of data science material, criteria for evaluating it, the matter of resources required to delve into this material, and some useful things to have in mind in your data science education efforts. Whether you are a newcomer to the field or a more seasoned data scientist, you have something to learn about data science (I know I do!) and this video can hopefully aid you in that. You can find it on Safari.
Note that in order to be able to view this video in its entirety, you'll need a subscription for the Safari platform. Also, it's important to remember that this video can offer you a framework for evaluating the data science material; you'll still need to find that material though and put the effort to study it, in order to make the most of it. The video can only help you organize your efforts more efficiently. Enjoy!
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!
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.