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You most likely recognize Santiago from his Twitter. On Twitter, each day, he shares a whole lot of useful features of artificial intelligence. Many thanks, Santiago, for joining us today. Welcome. (2:39) Santiago: Thank you for welcoming me. (3:16) Alexey: Before we enter into our major topic of relocating from software application design to artificial intelligence, perhaps we can start with your background.

I started as a software program designer. I went to college, obtained a computer technology level, and I began constructing software application. I assume it was 2015 when I decided to opt for a Master's in computer technology. Back then, I had no concept about artificial intelligence. I really did not have any kind of interest in it.

I understand you've been making use of the term "transitioning from software application engineering to device understanding". I such as the term "including in my ability set the artificial intelligence abilities" extra since I believe if you're a software program designer, you are already offering a great deal of worth. By incorporating device understanding currently, you're boosting the influence that you can carry the market.

That's what I would do. Alexey: This returns to one of your tweets or maybe it was from your program when you compare 2 methods to learning. One technique is the problem based strategy, which you simply spoke about. You find an issue. In this instance, it was some problem from Kaggle concerning this Titanic dataset, and you simply find out how to solve this trouble using a particular tool, like choice trees from SciKit Learn.

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

If I have an electric outlet right here that I need changing, I don't wish to most likely to university, spend 4 years comprehending the mathematics behind electricity and the physics and all of that, just to alter an electrical outlet. I would certainly rather begin with the electrical outlet and discover a YouTube video clip that assists me undergo the problem.

Santiago: I really like the idea of starting with a trouble, attempting to toss out what I know up to that problem and understand why it does not function. Get the devices that I require to resolve that trouble and begin excavating deeper and deeper and much deeper from that point on.

Alexey: Possibly we can speak a bit regarding learning sources. You pointed out in Kaggle there is an introduction tutorial, where you can get and learn just 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 profile, the tweet that's going to be on the top, the one that states "pinned tweet".

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Also if you're not a programmer, you can begin with Python and function your way to more artificial intelligence. This roadmap is concentrated on Coursera, which is a platform that I really, actually like. You can investigate all of the training courses free of cost or you can spend for the Coursera subscription to obtain certifications if you wish to.

Alexey: This comes back to one of your tweets or possibly it was from your course when you compare 2 methods to learning. In this instance, it was some trouble from Kaggle regarding this Titanic dataset, and you simply find out exactly how to solve this trouble making use of a certain tool, like decision trees from SciKit Learn.

You initially learn math, or direct algebra, calculus. When you recognize the mathematics, you go to equipment learning concept and you discover the theory. After that four years later on, you ultimately involve applications, "Okay, how do I make use of all these 4 years of mathematics to address this Titanic trouble?" Right? In the former, you kind of save yourself some time, I assume.

If I have an electrical outlet here that I require changing, I don't want to go to university, invest 4 years recognizing the mathematics behind electricity and the physics and all of that, just to change an outlet. I prefer to begin with the electrical outlet and locate a YouTube video that aids me undergo the issue.

Poor analogy. Yet you understand, right? (27:22) Santiago: I actually like the concept of starting with an issue, trying to throw out what I know up to that trouble and understand why it doesn't work. Then get the tools that I require to fix that trouble and begin excavating deeper and deeper and deeper from that factor on.

To make sure that's what I typically suggest. Alexey: Possibly we can chat a bit about finding out resources. You discussed in Kaggle there is an intro tutorial, where you can get and discover exactly how to choose trees. At the start, before we began this interview, you discussed a number of books also.

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The only need for that training 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".

Even if you're not a developer, you can start with Python and function your way to even more equipment knowing. 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 cost or you can pay 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 training course when you compare two techniques to learning. In this case, it was some trouble from Kaggle concerning this Titanic dataset, and you just find out how to solve this problem making use of a specific device, like choice trees from SciKit Learn.

You first find out mathematics, or direct algebra, calculus. When you understand the mathematics, you go to machine learning concept and you learn the theory. After that 4 years later on, you finally involve applications, "Okay, just how do I utilize all these four years of mathematics to resolve this Titanic trouble?" ? In the previous, you kind of save yourself some time, I believe.

If I have an electric outlet right here that I need replacing, I don't desire to most likely to college, invest four years recognizing the math behind electrical energy and the physics and all of that, simply to transform an outlet. I would certainly rather start with the electrical outlet and find a YouTube video clip that helps me experience the trouble.

Bad example. You obtain the concept? (27:22) Santiago: I really like the concept of starting with a trouble, trying to toss out what I recognize up to that trouble and comprehend why it does not function. Get hold of the tools that I need to solve that trouble and start digging deeper and deeper and deeper from that point on.

Alexey: Possibly we can talk a little bit regarding learning resources. You stated in Kaggle there is an intro tutorial, where you can obtain and discover just how to make decision trees.

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The only demand 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 claims "pinned tweet".

Also if you're not a programmer, you can begin with Python and work your means to more artificial intelligence. This roadmap is focused on Coursera, which is a platform that I really, truly like. You can examine all of the training courses completely free or you can pay for the Coursera membership to obtain certificates if you intend to.

Alexey: This comes back to one of your tweets or possibly it was from your course when you compare two methods to discovering. In this case, it was some issue from Kaggle concerning this Titanic dataset, and you just find out just how to fix this problem using a certain device, like decision trees from SciKit Learn.

You initially find out math, or linear algebra, calculus. After that when you recognize the mathematics, you most likely to artificial intelligence concept and you find out the concept. After that four years later, you ultimately pertain to applications, "Okay, just how do I make use of all these four years of math to fix this Titanic trouble?" Right? So in the former, you type of conserve on your own a long time, I assume.

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If I have an electric outlet right here that I need replacing, I do not wish to most likely to college, invest 4 years understanding the mathematics behind electrical energy and the physics and all of that, simply to alter an outlet. I would certainly rather start with the electrical outlet and discover a YouTube video clip that assists me experience the issue.

Santiago: I really like the concept of starting with a trouble, trying to toss out what I understand up to that issue and recognize why it does not function. Order the devices that I require to address that issue and begin excavating deeper and deeper and much deeper from that factor on.

To make sure that's what I generally suggest. Alexey: Maybe we can talk a bit about finding out resources. You mentioned in Kaggle there is an intro tutorial, where you can obtain and learn exactly how to make choice trees. At the start, before we started this meeting, you mentioned a number of books too.

The only need for that course is that you know 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".

Also if you're not a programmer, you can begin with Python and function your way to even more artificial intelligence. This roadmap is focused on Coursera, which is a platform that I really, truly like. You can audit all of the training courses free of cost or you can pay for the Coursera registration to get certifications if you desire to.