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You possibly recognize Santiago from his Twitter. On Twitter, on a daily basis, he shares a lot of sensible features of maker learning. Many thanks, Santiago, for joining us today. Welcome. (2:39) Santiago: Thanks for welcoming me. (3:16) Alexey: Before we go right into our main subject of moving from software design to artificial intelligence, perhaps we can begin with your history.

I began as a software application developer. I mosted likely to university, got a computer system scientific research level, and I began constructing software application. I believe it was 2015 when I chose to go with a Master's in computer technology. Back then, I had no concept about artificial intelligence. I didn't have any kind of interest in it.

I recognize you have actually been utilizing the term "transitioning from software design to artificial intelligence". I such as the term "including in my ability the device discovering abilities" extra since I believe if you're a software program designer, you are currently providing a great deal of worth. By incorporating artificial intelligence now, you're boosting the influence that you can carry the market.

So that's what I would certainly do. Alexey: This comes back to among your tweets or perhaps it was from your training course when you contrast two strategies to knowing. One strategy is the trouble based method, which you simply talked around. You find a trouble. In this case, it was some trouble from Kaggle regarding this Titanic dataset, and you simply learn exactly how to fix this trouble utilizing a details device, like decision trees from SciKit Learn.

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You first discover mathematics, or linear algebra, calculus. Then when you recognize the mathematics, you most likely to artificial intelligence concept and you discover the theory. Then 4 years later, you ultimately pertain to applications, "Okay, how do I use all these four years of math to resolve this Titanic problem?" Right? In the former, you kind of conserve yourself some time, I believe.

If I have an electric outlet right here that I need changing, I don't wish to go to college, spend 4 years comprehending the mathematics behind power and the physics and all of that, simply to alter an outlet. I would certainly instead begin with the electrical outlet and locate a YouTube video that helps me undergo the trouble.

Santiago: I truly like the concept of beginning with a problem, trying to toss out what I understand up to that issue and recognize why it does not work. Get hold of the devices that I require to address that trouble and start excavating deeper and much deeper and deeper from that point on.

Alexey: Maybe we can speak a bit regarding discovering sources. You pointed out in Kaggle there is an intro tutorial, where you can obtain and discover exactly how to make decision trees.

The only need for that course is that you recognize a little of Python. If you're a developer, that's a wonderful beginning point. (38:48) Santiago: If you're not a programmer, then I do have a pin on my Twitter account. If you go to my account, the tweet that's mosting likely to be on the top, the one that claims "pinned tweet".

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Even if you're not a programmer, you can begin with Python and work your way to more equipment discovering. This roadmap is focused on Coursera, which is a platform that I really, truly like. You can audit every one of the programs completely free or you can spend for the Coursera registration to get certifications if you intend to.

So that's what I would do. Alexey: This comes back to among your tweets or maybe it was from your program when you compare 2 strategies to knowing. One technique is the trouble based method, which you just chatted about. You find a problem. In this situation, it was some trouble from Kaggle regarding this Titanic dataset, and you just find out just how to solve this trouble making use of a certain tool, like choice trees from SciKit Learn.

You first find out mathematics, or direct algebra, calculus. When you recognize the math, you go to maker learning concept and you discover the concept.

If I have an electric outlet right here that I need replacing, I do not wish to most likely to university, spend 4 years understanding the math behind electrical energy and the physics and all of that, just to alter an electrical outlet. I prefer to start with the electrical outlet and discover a YouTube video that aids me go with the problem.

Santiago: I really like the idea of starting with a problem, attempting to throw out what I recognize up to that issue and understand why it does not function. Get hold of the devices that I need to resolve that issue and begin digging deeper and deeper and much deeper from that factor on.

That's what I normally suggest. Alexey: Maybe we can speak a bit about discovering sources. You discussed in Kaggle there is an intro tutorial, where you can get and learn just how to make decision trees. At the beginning, prior to we began this meeting, you discussed a couple of publications too.

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The only demand for that 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 claims "pinned tweet".

Even if you're not a programmer, you can begin with Python and work your way to more artificial intelligence. This roadmap is concentrated on Coursera, which is a platform that I actually, really like. You can audit all of the training courses free of charge or you can pay for the Coursera membership to obtain certificates if you wish to.

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Alexey: This comes back to one of your tweets or maybe it was from your training course when you contrast 2 approaches to learning. In this case, it was some trouble from Kaggle concerning this Titanic dataset, and you simply find out just how to solve this trouble making use of a specific tool, like choice trees from SciKit Learn.

You initially learn math, or direct algebra, calculus. When you know the math, you go to machine discovering concept and you discover the theory.

If I have an electric outlet below that I need changing, I do not wish to most likely to university, invest 4 years comprehending the math behind electrical power and the physics and all of that, simply to transform an outlet. I prefer to begin with the electrical outlet and find a YouTube video clip that helps me undergo the trouble.

Bad example. However you obtain the concept, right? (27:22) Santiago: I really like the idea of starting with an issue, trying to toss out what I know up to that issue and recognize why it does not work. Then get hold of the tools that I require to resolve that problem and start excavating deeper and much deeper and much deeper from that point on.

Alexey: Possibly we can talk a little bit about finding out resources. You discussed 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 demand 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 says "pinned tweet".

Even if you're not a programmer, you can start with Python and function your method to more artificial intelligence. This roadmap is focused on Coursera, which is a system that I truly, truly like. You can audit all of the training courses free of cost or you can spend for the Coursera registration to get certificates if you intend to.

To make sure that's what I would do. Alexey: This comes back to one of your tweets or possibly it was from your training course when you contrast 2 approaches to learning. One strategy is the problem based technique, which you just spoke about. You discover a trouble. In this case, it was some trouble from Kaggle regarding this Titanic dataset, and you just learn just how to address this trouble using a particular device, like choice trees from SciKit Learn.

You initially find out mathematics, or direct algebra, calculus. When you recognize the math, you go to device discovering concept and you find out the concept.

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If I have an electric outlet below that I need replacing, I do not intend to go to university, invest four years recognizing the math behind power and the physics and all of that, simply to change an electrical outlet. I would certainly instead start with the outlet and discover a YouTube video that assists me go via the issue.

Poor analogy. You get the concept? (27:22) Santiago: I really like the idea of starting with a trouble, trying to toss out what I know approximately that issue and comprehend why it does not work. Get the tools that I need to solve that issue and begin excavating much deeper and deeper and deeper from that point on.

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

The only need for that program 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 says "pinned tweet".

Also if you're not a programmer, you can start with Python and work your means to even more maker knowing. This roadmap is focused on Coursera, which is a system that I actually, actually like. You can investigate every one of the courses completely free or you can spend for the Coursera subscription to get certifications if you desire to.