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You probably know Santiago from his Twitter. On Twitter, every day, he shares a great deal of useful aspects of equipment understanding. Thanks, Santiago, for joining us today. Welcome. (2:39) Santiago: Thanks for welcoming me. (3:16) Alexey: Before we go into our main topic of moving from software application engineering to artificial intelligence, possibly we can start with your background.

I began as a software program designer. I went to college, obtained a computer technology level, and I began building software application. I believe it was 2015 when I chose to opt for a Master's in computer technology. At that time, I had no concept concerning device understanding. I really did not have any rate of interest in it.

I understand you've been making use of the term "transitioning from software design to artificial intelligence". I like the term "including in my ability the equipment knowing skills" more because I assume if you're a software application engineer, you are already offering a lot of worth. By incorporating artificial intelligence currently, you're augmenting the effect that you can carry the industry.

That's what I would do. Alexey: This returns to among your tweets or perhaps it was from your training course when you compare 2 methods to discovering. One technique is the issue based method, which you simply chatted about. You find an issue. In this situation, it was some trouble from Kaggle regarding this Titanic dataset, and you just find out just how to address this problem using a specific device, like decision trees from SciKit Learn.

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You first discover mathematics, or straight algebra, calculus. When you understand the mathematics, you go to device learning theory and you learn the theory.

If I have an electrical outlet below that I require changing, I do not intend to most likely to university, spend four years understanding the math behind electrical energy and the physics and all of that, just to alter an electrical outlet. I would certainly instead begin with the electrical outlet and locate a YouTube video that assists me experience the trouble.

Bad analogy. You obtain the idea? (27:22) Santiago: I really like the idea of beginning with an issue, trying to toss out what I recognize up to that trouble and recognize why it does not function. Get the tools that I need to address that issue and begin excavating much deeper and much deeper and deeper from that point on.

Alexey: Possibly we can speak a little bit about discovering sources. You stated in Kaggle there is an intro tutorial, where you can get and discover how to make decision trees.

The only demand for that program is that you recognize a little bit of Python. If you're a designer, that's an excellent base. (38:48) Santiago: If you're not a designer, then I do have a pin on my Twitter account. If you most likely to my profile, the tweet that's mosting likely to get on the top, the one that says "pinned tweet".

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Also if you're not a designer, you can begin with Python and function your method to more machine understanding. This roadmap is focused on Coursera, which is a platform that I actually, truly like. You can examine all of the courses free of cost or you can spend for the Coursera membership to obtain certifications if you wish to.

Alexey: This comes back to one of your tweets or maybe it was from your course when you contrast 2 strategies to discovering. In this instance, it was some issue from Kaggle about this Titanic dataset, and you simply discover exactly how to resolve this trouble utilizing a details tool, like decision trees from SciKit Learn.

You first find out mathematics, or linear algebra, calculus. When you recognize the mathematics, you go to device knowing concept and you discover the concept. After that four years later on, you lastly pertain to applications, "Okay, how do I make use of all these four years of mathematics to fix this Titanic problem?" Right? So in the former, you type of save on your own a long time, I assume.

If I have an electric outlet here that I require changing, I do not desire to most likely to college, invest four years comprehending the mathematics behind electricity and the physics and all of that, simply to change an electrical outlet. I prefer to begin with the electrical outlet and locate a YouTube video clip that helps me undergo the trouble.

Poor example. However you understand, right? (27:22) Santiago: I actually like the idea of beginning with a trouble, attempting to throw away what I recognize as much as that trouble and recognize why it does not work. Grab the tools that I need to resolve that problem and begin excavating much deeper and much deeper and much deeper from that factor on.

Alexey: Maybe we can chat a bit concerning learning resources. You stated in Kaggle there is an introduction tutorial, where you can get and discover exactly how to make choice trees.

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The only requirement 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 says "pinned tweet".

Also if you're not a developer, you can start with Python and work your way to more artificial intelligence. This roadmap is concentrated on Coursera, which is a system that I truly, really like. You can examine every one of the training courses completely free or you can spend for the Coursera subscription to obtain certifications if you desire to.

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Alexey: This comes back to one of your tweets or possibly it was from your training course when you contrast 2 approaches to discovering. In this case, it was some problem from Kaggle about this Titanic dataset, and you simply learn exactly how to resolve this issue utilizing a particular device, like decision trees from SciKit Learn.

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

If I have an electrical outlet here that I need changing, I do not intend to most likely to university, invest four years recognizing the mathematics behind electricity and the physics and all of that, simply to transform an electrical outlet. I prefer to start with the electrical outlet and find a YouTube video that assists me undergo the problem.

Bad analogy. But you obtain the concept, right? (27:22) Santiago: I really like the idea of starting with an issue, attempting to toss out what I understand as much as that trouble and recognize why it does not function. Then get the tools that I need to address that problem and begin excavating much deeper and deeper and deeper from that factor on.

Alexey: Possibly we can speak a little bit concerning finding out resources. You stated in Kaggle there is an introduction tutorial, where you can get and discover how to make choice trees.

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The only need for that course is that you understand a bit of Python. If you're a programmer, that's a great 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 states "pinned tweet".

Even if you're not a designer, you can start with Python and function your means to even more equipment discovering. This roadmap is focused on Coursera, which is a system that I actually, really like. You can examine every one of the courses totally free or you can spend for the Coursera membership to get certificates if you desire to.

Alexey: This comes back to one of your tweets or maybe it was from your program when you compare two approaches to learning. In this situation, it was some issue from Kaggle regarding this Titanic dataset, and you just learn how to resolve this issue making use of a particular device, like decision trees from SciKit Learn.

You first discover math, or direct algebra, calculus. When you know the mathematics, you go to machine knowing theory and you discover the theory.

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If I have an electric outlet right here that I need changing, I do not want to go to college, spend four years understanding the math behind electricity and the physics and all of that, simply to alter an electrical outlet. I prefer to begin with the electrical outlet and discover a YouTube video clip that helps me experience the issue.

Santiago: I really like the idea of starting with a problem, attempting to toss out what I recognize up to that issue and comprehend why it does not function. Get the devices that I require to address that issue and start digging deeper and much deeper and deeper from that point on.

That's what I typically advise. Alexey: Possibly we can chat a little bit concerning discovering sources. You mentioned in Kaggle there is an intro tutorial, where you can get and learn how to make decision trees. At the beginning, before we started this interview, you discussed a number of books too.

The only requirement for that program is that you recognize a little of Python. If you're a developer, that's a wonderful beginning factor. (38:48) Santiago: If you're not a designer, then I do have a pin on my Twitter account. If you go to my account, the tweet that's mosting likely to get on the top, the one that states "pinned tweet".

Also if you're not a programmer, you can begin with Python and function your means to more machine discovering. This roadmap is concentrated on Coursera, which is a platform that I really, really like. You can audit every one of the training courses free of charge or you can spend for the Coursera registration to obtain certificates if you intend to.