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You possibly recognize Santiago from his Twitter. On Twitter, every day, he shares a whole lot of useful features of machine learning. Thanks, Santiago, for joining us today. Welcome. (2:39) Santiago: Thank you for inviting me. (3:16) Alexey: Before we enter into our main subject of relocating from software engineering to artificial intelligence, maybe we can start with your background.

I started as a software program designer. I mosted likely to college, got a computer technology degree, and I began developing software. I think it was 2015 when I decided to go with a Master's in computer science. Back after that, I had no idea concerning device understanding. I really did not have any passion in it.

I understand you have actually been utilizing the term "transitioning from software engineering to artificial intelligence". I like the term "adding to my capability the artificial intelligence skills" extra because I believe if you're a software engineer, you are already offering a great deal of value. By integrating device discovering currently, you're increasing the influence that you can carry the sector.

Alexey: This comes back to one of your tweets or maybe it was from your program when you compare two approaches to knowing. In this case, it was some problem from Kaggle about this Titanic dataset, and you simply discover exactly how to fix this trouble utilizing a particular tool, like decision trees from SciKit Learn.

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

If I have an electrical outlet below that I require replacing, I do not intend to go to university, invest four years comprehending the mathematics behind electrical power and the physics and all of that, just to alter an outlet. I prefer to begin with the outlet and discover a YouTube video clip that helps me go through the problem.

Santiago: I actually like the idea of starting with an issue, attempting to toss out what I understand up to that problem and comprehend why it doesn't function. Get hold of the tools that I need to solve that trouble and start excavating much deeper and deeper and much deeper from that factor on.

Alexey: Maybe we can talk a little bit about finding out resources. You pointed out in Kaggle there is an intro tutorial, where you can obtain and discover exactly how to make decision trees.

The only requirement for that course is that you understand a bit of Python. If you're a programmer, that's an excellent base. (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 mosting likely to be on the top, the one that says "pinned tweet".

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Also if you're not a programmer, you can begin with Python and work your method to more device learning. This roadmap is focused on Coursera, which is a platform that I truly, truly like. You can examine all of the training courses absolutely free or you can pay for the Coursera subscription to obtain certifications if you wish to.

That's what I would do. Alexey: This returns to one of your tweets or maybe it was from your training course when you compare two strategies to knowing. One technique is the trouble based strategy, which you just discussed. You find a trouble. In this situation, it was some trouble from Kaggle concerning this Titanic dataset, and you simply discover exactly how to address this trouble utilizing a specific device, like choice trees from SciKit Learn.

You first learn mathematics, or straight algebra, calculus. When you know the mathematics, you go to equipment discovering concept and you discover the concept.

If I have an electrical outlet here that I need replacing, I do not intend to go to college, invest four years comprehending the mathematics behind power and the physics and all of that, simply to alter an outlet. I prefer to start with the electrical outlet and find a YouTube video that aids me go through the problem.

Negative example. However you understand, right? (27:22) Santiago: I actually like the concept of beginning with a trouble, attempting to toss out what I recognize approximately that problem and understand why it doesn't work. After that grab the tools that I need to solve that problem and begin digging much deeper and deeper and much deeper from that factor on.

Alexey: Maybe we can chat a bit concerning finding out sources. You stated in Kaggle there is an intro tutorial, where you can get and find out how to make choice trees.

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The only requirement for that program is that you know a little bit of Python. If you're a designer, that's a great beginning factor. (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 account, the tweet that's mosting likely to be on the top, the one that claims "pinned tweet".

Also if you're not a designer, you can begin with Python and work your method to more artificial intelligence. This roadmap is concentrated on Coursera, which is a platform that I truly, actually like. You can audit all of the programs free of charge or you can pay for the Coursera registration to obtain certificates if you wish to.

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To ensure that's what I would do. Alexey: This returns to among your tweets or maybe it was from your training course when you compare two approaches to understanding. One technique is the problem based technique, which you simply spoke around. You locate an issue. In this case, it was some issue from Kaggle about this Titanic dataset, and you simply find out just how to address this trouble utilizing a particular device, like decision trees from SciKit Learn.

You first find out mathematics, or linear algebra, calculus. Then when you understand the math, you most likely to artificial intelligence concept and you discover the theory. Then four years later, you ultimately come to applications, "Okay, how do I utilize all these four years of mathematics to address this Titanic problem?" Right? In the former, you kind of conserve yourself some time, I think.

If I have an electrical outlet below that I require changing, I don't wish to most likely to college, spend 4 years recognizing the mathematics behind electrical power and the physics and all of that, just to change an outlet. I would certainly instead begin with the electrical outlet and locate a YouTube video that helps me undergo the issue.

Poor 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 up to that problem and understand why it doesn't function. Then order the tools that I need to address that problem and begin digging deeper and much deeper and much deeper from that point on.

To ensure that's what I typically advise. Alexey: Maybe we can speak a little bit about learning resources. You discussed in Kaggle there is an introduction tutorial, where you can get and find out just how to choose trees. At the beginning, prior to we began this meeting, you pointed out a number of publications too.

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The only need 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 states "pinned tweet".

Even if you're not a designer, you can begin with Python and function your means to more artificial intelligence. This roadmap is concentrated on Coursera, which is a platform that I actually, truly like. You can audit all of the programs completely free or you can pay for the Coursera registration to obtain certificates if you desire to.

So that's what I would certainly do. Alexey: This returns to among your tweets or possibly it was from your program when you compare 2 approaches to discovering. One technique is the issue based approach, which you simply discussed. You locate a trouble. In this instance, it was some trouble from Kaggle regarding this Titanic dataset, and you simply learn how to fix this issue utilizing a specific device, like choice trees from SciKit Learn.

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

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If I have an electric outlet below that I require replacing, I don't want to go to university, invest four years comprehending the mathematics behind electricity and the physics and all of that, just to alter an electrical outlet. I prefer to start with the outlet and locate a YouTube video that helps me experience the trouble.

Santiago: I really like the idea of starting with a problem, trying to toss out what I know up to that issue and comprehend why it does not work. Get hold of the tools that I require to resolve that trouble and begin excavating deeper and much deeper and much deeper from that factor on.

That's what I normally suggest. Alexey: Perhaps we can chat a little bit about learning resources. You mentioned in Kaggle there is an introduction tutorial, where you can obtain and discover just how to make choice trees. At the beginning, before we started this meeting, you discussed a couple of publications.

The only requirement 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".

Also if you're not a developer, you can begin with Python and work your method to even more device learning. This roadmap is focused on Coursera, which is a system that I really, truly like. You can investigate every one of the programs totally free or you can pay for the Coursera registration to get certifications if you desire to.