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You possibly know Santiago from his Twitter. On Twitter, on a daily basis, he shares a great deal of practical things concerning artificial intelligence. Thanks, Santiago, for joining us today. Welcome. (2:39) Santiago: Thank you for welcoming me. (3:16) Alexey: Before we go right into our primary subject of relocating from software program design to artificial intelligence, perhaps we can begin with your history.

I started as a software program designer. I went to university, got a computer technology level, and I began developing software program. I assume it was 2015 when I made a decision to go for a Master's in computer technology. Back then, I had no concept regarding artificial intelligence. I didn't have any type of interest in it.

I know you've been making use of the term "transitioning from software engineering to artificial intelligence". I like the term "contributing to my ability set the artificial intelligence abilities" more since I assume if you're a software application designer, you are currently giving a lot of value. By integrating machine learning now, you're increasing the influence that you can have on the sector.

Alexey: This comes back to one of your tweets or maybe it was from your training course when you contrast 2 methods to understanding. In this case, it was some trouble from Kaggle regarding this Titanic dataset, and you simply find out how to address this issue using a particular device, like choice trees from SciKit Learn.

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You initially discover mathematics, or straight algebra, calculus. Then when you know the mathematics, you go to artificial intelligence theory and you discover the concept. Then four years later on, you ultimately pertain to applications, "Okay, how do I make use of all these four years of math to resolve this Titanic trouble?" Right? In the former, you kind of conserve yourself some time, I believe.

If I have an electrical outlet here that I need replacing, I don't intend to most likely to university, invest 4 years recognizing the math behind electrical power and the physics and all of that, just to alter an electrical outlet. I prefer to begin with the outlet and discover a YouTube video that aids me go through the problem.

Santiago: I really like the idea of beginning with an issue, attempting to throw out what I recognize up to that problem and comprehend why it does not work. Get the devices that I require to resolve that problem and start digging much deeper and much deeper and deeper from that factor on.

That's what I generally recommend. Alexey: Perhaps we can talk a little bit regarding learning resources. You mentioned in Kaggle there is an introduction tutorial, where you can obtain and find out just how to make decision trees. At the start, before we started this meeting, you mentioned a couple of publications as well.

The only requirement for that training course is that you know a little of Python. If you're a designer, that's a terrific starting factor. (38:48) Santiago: If you're not a designer, after that I do have a pin on my Twitter account. If you go to my account, the tweet that's going to be on the top, the one that claims "pinned tweet".

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Also if you're not a developer, you can begin with Python and function your way to more device learning. This roadmap is concentrated on Coursera, which is a system that I really, actually like. You can audit all of the programs for totally free or you can pay for the Coursera subscription to get certificates if you intend to.

So that's what I would certainly do. Alexey: This comes back to among your tweets or possibly it was from your course when you contrast two approaches to understanding. One method is the issue based approach, which you simply spoke about. You find an issue. In this instance, it was some problem from Kaggle concerning this Titanic dataset, and you simply discover how to resolve this problem using a specific tool, like decision trees from SciKit Learn.

You first find out mathematics, or straight algebra, calculus. When you understand the mathematics, you go to machine knowing concept and you learn the concept.

If I have an electrical outlet below that I require replacing, I don't intend to most likely to college, invest four years recognizing the math behind electrical power and the physics and all of that, simply to change an electrical outlet. I would rather begin with the electrical outlet and discover a YouTube video clip that helps me go with the trouble.

Negative analogy. But you get the idea, right? (27:22) Santiago: I really like the concept of beginning with an issue, attempting to toss out what I recognize approximately that issue and comprehend why it doesn't work. Grab the tools that I need to solve that problem and begin digging much deeper and much deeper and much deeper from that factor on.

To ensure that's what I generally suggest. Alexey: Possibly we can talk a little bit about finding out sources. You mentioned in Kaggle there is an intro tutorial, where you can get and learn just how to make choice trees. At the start, prior to we began this meeting, you discussed a number of publications as well.

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The only requirement for that program is that you know a bit of Python. If you're a designer, that's a terrific base. (38:48) Santiago: If you're not a programmer, then I do have a pin on my Twitter account. If you most likely to my account, the tweet that's mosting likely to be on the top, the one that says "pinned tweet".

Even if you're not a programmer, you can start with Python and function your method to more equipment discovering. This roadmap is concentrated on Coursera, which is a platform that I truly, really like. You can audit every one of the training courses free of charge or you can pay for the Coursera subscription to obtain certifications if you intend to.

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To make sure that's what I would do. Alexey: This returns to one of your tweets or perhaps it was from your course when you compare 2 methods to discovering. One method is the trouble based technique, which you simply spoke about. You locate a trouble. In this instance, it was some issue from Kaggle concerning this Titanic dataset, and you simply discover exactly how to address this problem using a details device, like decision trees from SciKit Learn.

You first discover mathematics, or direct algebra, calculus. When you understand the math, you go to equipment learning theory and you learn the concept.

If I have an electric outlet right here that I need replacing, I don't desire to most likely to university, spend four years understanding the mathematics behind power and the physics and all of that, just to change an outlet. I would certainly rather begin with the electrical outlet and find a YouTube video that assists me experience the problem.

Negative analogy. You get the concept? (27:22) Santiago: I truly like the concept of beginning with a trouble, trying to toss out what I recognize approximately that trouble and comprehend why it does not function. Get hold of the devices that I require to address that issue and start digging much deeper and much deeper and deeper from that point on.

Alexey: Possibly we can talk a little bit concerning discovering resources. You pointed out in Kaggle there is an introduction tutorial, where you can obtain and find out just how to make choice trees.

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The only need for that course is that you understand a little bit of Python. If you go to my account, the tweet that's going to be on the top, the one that states "pinned tweet".

Also if you're not a developer, you can start with Python and work your method to even more artificial intelligence. This roadmap is concentrated on Coursera, which is a system that I actually, really like. You can audit all of the courses free of charge or you can spend for the Coursera registration to get certifications if you intend to.

To make sure that's what I would certainly do. Alexey: This returns to one of your tweets or perhaps it was from your training course when you compare two techniques to discovering. One technique is the issue based strategy, which you simply discussed. You find a trouble. In this case, it was some trouble from Kaggle concerning this Titanic dataset, and you just find out just how to address this problem utilizing a certain tool, like decision trees from SciKit Learn.

You initially find out math, or direct algebra, calculus. Then when you understand the mathematics, you most likely to artificial intelligence theory and you find out the theory. 4 years later, you lastly come to applications, "Okay, just how do I make use of all these four years of math to solve this Titanic trouble?" ? So in the former, you type of conserve on your own time, I believe.

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If I have an electrical outlet right here that I require changing, I do not desire to go to university, spend 4 years understanding the mathematics behind power and the physics and all of that, simply to alter an outlet. I would certainly rather start with the outlet and discover a YouTube video that assists me go with the trouble.

Bad analogy. You obtain the idea? (27:22) Santiago: I actually like the concept of beginning with a trouble, trying to throw away what I recognize up to that issue and comprehend why it doesn't work. After that get the devices that I require to address that problem and begin excavating deeper and deeper and much deeper from that factor on.

That's what I normally advise. Alexey: Maybe we can speak a little bit concerning finding out sources. You mentioned in Kaggle there is an intro tutorial, where you can get and learn exactly how to choose trees. At the beginning, before we started this meeting, you discussed a couple of publications.

The only need for that training course is that you understand a bit of Python. If you're a designer, that's an excellent base. (38:48) Santiago: If you're not a developer, after that I do have a pin on my Twitter account. If you most likely to my profile, the tweet that's mosting likely to be on the top, the one that claims "pinned tweet".

Also if you're not a programmer, you can begin with Python and work your method to even more equipment understanding. This roadmap is concentrated on Coursera, which is a platform that I actually, truly like. You can investigate all of the training courses free of cost or you can pay for the Coursera membership to get certifications if you want to.