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You possibly understand Santiago from his Twitter. On Twitter, every day, he shares a great deal of functional things about artificial intelligence. Many thanks, Santiago, for joining us today. Welcome. (2:39) Santiago: Thank you for inviting me. (3:16) Alexey: Before we go right into our primary topic of relocating from software application engineering to artificial intelligence, perhaps we can start with your background.

I began as a software application programmer. I went to university, got a computer technology degree, and I began building software. I believe it was 2015 when I chose to go for a Master's in computer system science. At that time, I had no idea regarding equipment understanding. I didn't have any type of rate of interest in it.

I know you have actually been using the term "transitioning from software program engineering to device understanding". I like the term "contributing to my skill established the artificial intelligence skills" more since I believe if you're a software application designer, you are already giving a great deal of worth. By including device learning currently, you're augmenting the effect that you can carry the market.

That's what I would do. Alexey: This comes back to one of your tweets or possibly it was from your course when you compare 2 approaches to understanding. One strategy is the trouble based strategy, which you simply talked around. You locate an issue. In this instance, it was some issue from Kaggle about this Titanic dataset, and you just learn how to fix this problem making use of a certain tool, like decision trees from SciKit Learn.

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You first discover math, or direct algebra, calculus. After that when you know the math, you go to artificial intelligence theory and you learn the theory. 4 years later, you lastly come to applications, "Okay, just how do I use all these four years of math to address this Titanic issue?" ? So in the former, you kind of save yourself time, I assume.

If I have an electric outlet below that I require changing, I don't intend to most likely to university, invest 4 years recognizing the mathematics behind electricity and the physics and all of that, simply to transform an electrical outlet. I would certainly instead start with the outlet and locate a YouTube video that aids me go with the issue.

Santiago: I really like the concept of beginning with a problem, attempting to toss out what I understand up to that issue and understand why it does not function. Grab the devices that I need to fix that problem and begin digging much deeper and deeper and deeper from that factor on.

To ensure that's what I normally advise. Alexey: Maybe we can speak a bit concerning learning sources. You stated in Kaggle there is an introduction tutorial, where you can obtain and learn just how to choose trees. At the beginning, before we began this meeting, you mentioned a number of books too.

The only demand for that training course is that you recognize a little bit of Python. If you go to my profile, the tweet that's going to be on the top, the one that says "pinned tweet".

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Also if you're not a designer, you can start with Python and work your means to more artificial intelligence. This roadmap is concentrated on Coursera, which is a system that I truly, really like. You can investigate all of the courses completely free or you can pay for the Coursera subscription to obtain certificates if you wish to.

That's what I would certainly do. Alexey: This comes back to among your tweets or possibly it was from your training course when you contrast 2 methods to discovering. One method is the trouble based technique, which you simply discussed. You find a problem. In this instance, it was some issue from Kaggle regarding this Titanic dataset, and you just find out how to address this problem making use of a details device, like choice trees from SciKit Learn.

You initially find out math, or direct algebra, calculus. When you understand the math, you go to machine learning concept and you discover the concept. After that 4 years later on, you ultimately concern applications, "Okay, just how do I utilize all these 4 years of math to resolve this Titanic issue?" ? So in the previous, you type of save on your own a long time, I think.

If I have an electric outlet below that I require changing, I do not want to most likely to university, invest 4 years comprehending the mathematics behind power and the physics and all of that, simply to change an electrical outlet. I prefer to begin with the outlet and locate a YouTube video that assists me experience the problem.

Poor analogy. However you get the idea, right? (27:22) Santiago: I truly like the concept of beginning with a trouble, attempting to toss out what I know approximately that trouble and recognize why it doesn't function. Get hold of the devices that I require to solve that issue and begin excavating much deeper and much deeper and deeper from that factor on.

Alexey: Possibly we can speak a little bit concerning learning sources. You discussed in Kaggle there is an intro tutorial, where you can obtain and learn just how to make decision trees.

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

Even if you're not a designer, you can begin with Python and work your means to more artificial intelligence. This roadmap is concentrated on Coursera, which is a system that I really, truly like. You can examine every one of the training courses absolutely free or you can pay for the Coursera subscription to obtain certifications if you want to.

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To make sure that's what I would do. Alexey: This returns to among your tweets or possibly it was from your course when you compare two approaches to learning. One method is the problem based method, which you just discussed. You discover an issue. In this case, it was some issue from Kaggle concerning this Titanic dataset, and you simply find out just how to fix this trouble making use of a details tool, like decision trees from SciKit Learn.

You first learn math, or straight algebra, calculus. When you recognize the math, you go to maker learning concept and you discover the theory. Then four years later, you lastly involve applications, "Okay, just how do I utilize all these 4 years of mathematics to address this Titanic trouble?" Right? So in the former, you type of conserve on your own a long time, I think.

If I have an electric outlet here that I need replacing, I don't intend to go to university, invest 4 years recognizing the math behind power and the physics and all of that, just to change an outlet. I prefer to start with the electrical outlet and find a YouTube video that helps me undergo the trouble.

Poor example. You get the concept? (27:22) Santiago: I actually like the idea of starting with a problem, attempting to throw away what I understand up to that trouble and recognize why it does not function. After that order the tools that I need to solve that problem and start excavating much deeper and deeper and deeper from that point on.

So that's what I typically suggest. Alexey: Maybe we can speak a little bit regarding learning resources. You discussed in Kaggle there is an intro tutorial, where you can obtain and discover just how to choose trees. At the start, before we started this meeting, you pointed out a number of publications also.

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

Even if you're not a programmer, you can start with Python and function your method to more device learning. This roadmap is concentrated on Coursera, which is a system that I truly, actually like. You can investigate all of the programs completely free or you can pay for the Coursera membership to get certifications if you intend to.

Alexey: This comes back to one of your tweets or possibly it was from your training course when you contrast two methods to discovering. In this instance, it was some problem from Kaggle regarding this Titanic dataset, and you simply learn how to address this issue using a specific tool, like choice trees from SciKit Learn.

You initially learn mathematics, or direct algebra, calculus. When you understand the math, you go to machine discovering theory and you find out the concept.

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If I have an electrical outlet right here that I require replacing, I do not want to go to university, spend 4 years understanding the math behind electricity and the physics and all of that, simply to change an electrical outlet. I would instead start with the outlet and locate a YouTube video that aids me undergo the problem.

Santiago: I actually like the concept of beginning with a problem, trying to throw out what I know up to that issue and understand why it doesn't function. Get the tools that I need to solve that trouble and begin digging deeper and deeper and deeper from that point on.

Alexey: Perhaps we can chat a little bit concerning discovering resources. You stated in Kaggle there is an introduction tutorial, where you can obtain and learn just how to make decision trees.

The only requirement for that training course is that you recognize 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".

Even if you're not a programmer, you can start with Python and work your means to even more device knowing. This roadmap is concentrated on Coursera, which is a platform that I really, truly like. You can investigate every one of the programs free of cost or you can pay for the Coursera membership to obtain certifications if you want to.