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You most likely know Santiago from his Twitter. On Twitter, on a daily basis, he shares a great deal of functional things about artificial intelligence. Many thanks, Santiago, for joining us today. Welcome. (2:39) Santiago: Thanks for inviting me. (3:16) Alexey: Prior to we enter into our main subject of moving from software engineering to artificial intelligence, possibly we can start with your history.

I started as a software application designer. I went to college, obtained a computer science level, and I started building software application. I believe it was 2015 when I determined to go with a Master's in computer technology. At that time, I had no idea about artificial intelligence. I really did not have any passion in it.

I know you have actually been making use of the term "transitioning from software engineering to equipment discovering". I like the term "adding to my skill established the maker understanding abilities" a lot more because I believe if you're a software program engineer, you are currently providing a great deal of worth. By including artificial intelligence now, you're increasing the effect that you can have on the sector.

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 compare 2 strategies to discovering. One method is the problem based approach, which you just discussed. You find a trouble. In this instance, it was some trouble from Kaggle about this Titanic dataset, and you simply find out exactly how to solve this problem utilizing a details device, like decision trees from SciKit Learn.

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You initially find out mathematics, or linear algebra, calculus. Then when you understand the math, you go to machine learning theory and you discover the theory. Four years later, you lastly come to applications, "Okay, just how do I utilize all these four years of mathematics to fix this Titanic problem?" ? In the former, you kind of save yourself some time, I think.

If I have an electric outlet here that I need changing, I do not intend to go to university, spend four years comprehending the math behind electricity and the physics and all of that, just to alter an electrical outlet. I would rather begin with the electrical outlet and discover a YouTube video that assists me undergo the problem.

Bad analogy. You get the concept? (27:22) Santiago: I truly like the concept of starting with an issue, attempting to throw away what I understand as much as that issue and understand why it does not function. Get hold of the devices that I need to resolve that trouble and start excavating deeper and deeper and much deeper from that factor on.

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

The only need for that training course is that you understand a bit of Python. If you're a designer, that's a fantastic 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 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 programmer, you can start with Python and function your means to more machine knowing. This roadmap is concentrated on Coursera, which is a platform that I truly, really like. You can investigate every one of the courses completely free or you can pay for the Coursera membership to get certifications if you intend to.

To make sure that's what I would do. Alexey: This returns to among your tweets or maybe it was from your training course when you contrast 2 approaches to discovering. One strategy is the trouble based strategy, which you simply spoke about. You find a trouble. In this instance, it was some problem from Kaggle about this Titanic dataset, and you just find out exactly how to resolve this problem utilizing a certain device, like choice trees from SciKit Learn.

You first find out math, or direct algebra, calculus. When you know the mathematics, you go to equipment understanding theory and you learn the theory. Four years later, you ultimately come to applications, "Okay, just how do I use all these four years of mathematics to solve this Titanic trouble?" Right? In the former, you kind of save on your own some time, I assume.

If I have an electrical outlet here that I need replacing, I do not wish to go to university, spend four years recognizing the math behind power and the physics and all of that, simply to transform an outlet. I prefer to start with the electrical outlet and discover a YouTube video that helps me undergo the problem.

Santiago: I actually like the idea of starting with a problem, trying to throw out what I understand up to that trouble and recognize why it doesn't work. Grab the devices that I need to address that trouble and start digging much deeper and deeper and deeper from that factor on.

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

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The only need for that training course is that you understand a little bit of Python. If you're a developer, that's a great base. (38:48) Santiago: If you're not a programmer, 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 claims "pinned tweet".

Even if you're not a designer, you can start with Python and work your means to even more artificial intelligence. This roadmap is focused on Coursera, which is a system that I really, actually like. You can investigate every one of the training courses free of charge or you can spend for the Coursera membership to obtain certifications if you wish to.

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Alexey: This comes back to one of your tweets or maybe it was from your program when you contrast 2 approaches to knowing. In this case, it was some problem from Kaggle regarding this Titanic dataset, and you simply find out just how to resolve this issue using a particular device, like decision trees from SciKit Learn.

You first discover math, or direct algebra, calculus. When you understand the mathematics, you go to machine knowing theory and you discover the concept. Four years later on, you lastly come to applications, "Okay, just how do I use all these 4 years of mathematics to resolve this Titanic trouble?" Right? In the former, you kind of save yourself some time, I believe.

If I have an electric outlet right here that I require replacing, I do not want to most likely to college, invest 4 years understanding the mathematics behind power and the physics and all of that, just to change an electrical outlet. I would rather start with the electrical outlet and discover a YouTube video that aids me go through the problem.

Poor example. You obtain the concept? (27:22) Santiago: I truly like the idea of beginning with a trouble, trying to throw out what I know up to that issue and understand why it doesn't function. Then get 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 chat a little bit concerning learning sources. You discussed in Kaggle there is an intro tutorial, where you can get and learn how to make choice 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 claims "pinned tweet".

Also if you're not a programmer, you can begin with Python and function your means to more artificial intelligence. This roadmap is focused on Coursera, which is a system that I truly, truly like. You can audit every one of the training courses free of charge or you can pay for the Coursera registration to obtain certificates if you intend to.

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

You first learn mathematics, or linear algebra, calculus. When you recognize the math, you go to device discovering theory and you discover the theory.

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If I have an electric outlet below that I require replacing, I don't desire to most likely to university, spend four years understanding the mathematics behind electrical power and the physics and all of that, simply to change an outlet. I prefer to begin with the electrical outlet and discover a YouTube video that helps me undergo the issue.

Bad example. But you understand, right? (27:22) Santiago: I truly like the idea of starting with a problem, trying to throw out what I recognize approximately that trouble and recognize why it does not function. After that order the tools that I require to address that trouble and begin digging deeper and much deeper and much deeper from that factor on.

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

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 says "pinned tweet".

Even if you're not a programmer, you can begin with Python and function your means to even more equipment knowing. This roadmap is concentrated on Coursera, which is a system that I really, truly like. You can audit every one of the programs totally free or you can pay for the Coursera registration to get certificates if you intend to.