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You probably know Santiago from his Twitter. On Twitter, daily, he shares a lot of useful aspects of machine discovering. Many thanks, Santiago, for joining us today. Welcome. (2:39) Santiago: Thanks for inviting me. (3:16) Alexey: Prior to we enter into our major subject of moving from software application engineering to artificial intelligence, possibly we can start with your background.

I began as a software program programmer. I mosted likely to college, got a computer technology degree, and I started developing software application. I assume it was 2015 when I determined to choose a Master's in computer system science. Back then, I had no idea concerning equipment knowing. I really did not have any type of rate of interest in it.

I understand you've been using the term "transitioning from software program engineering to equipment knowing". I like the term "contributing to my capability the artificial intelligence abilities" much more because I think if you're a software program designer, you are already giving a great deal of worth. By incorporating device understanding currently, you're increasing the impact that you can have on the industry.

Alexey: This comes back to one of your tweets or possibly it was from your training course when you contrast 2 approaches to discovering. In this situation, it was some issue from Kaggle regarding this Titanic dataset, and you just find out how to fix this trouble using a details device, like decision trees from SciKit Learn.

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You first find out mathematics, or linear algebra, calculus. When you know the math, you go to machine understanding theory and you learn the concept.

If I have an electric outlet here that I require replacing, I do not want to most likely to university, invest four years comprehending the math behind power and the physics and all of that, simply to change an outlet. I prefer to begin with the electrical outlet and find a YouTube video that assists me experience the trouble.

Santiago: I really like the concept of starting with a problem, trying to toss out what I know up to that trouble and understand why it does not work. Grab the tools that I need to solve that problem and start excavating much deeper and much deeper and deeper from that point on.

That's what I usually advise. Alexey: Maybe we can talk a little bit about learning resources. You stated in Kaggle there is an intro tutorial, where you can get and learn exactly how to choose trees. At the start, before we started this meeting, you pointed out a pair of publications 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 states "pinned tweet".

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Even if you're not a programmer, you can start with Python and function your way to more artificial intelligence. This roadmap is focused on Coursera, which is a system that I really, really like. You can audit every one of the programs free of charge or you can pay for the Coursera subscription to get certifications if you intend to.

Alexey: This comes back to one of your tweets or perhaps it was from your training course when you contrast two methods to knowing. In this situation, it was some trouble from Kaggle concerning this Titanic dataset, and you just find out how to fix this problem making use of a details device, like decision trees from SciKit Learn.

You first find out math, or linear algebra, calculus. After that when you know the mathematics, you most likely to artificial intelligence theory and you find out the theory. 4 years later on, you finally come to applications, "Okay, how do I utilize all these four years of mathematics to fix this Titanic issue?" Right? In the former, you kind of save yourself some time, I believe.

If I have an electric outlet right here that I require changing, I don't intend to most likely to university, spend 4 years understanding the math behind power and the physics and all of that, simply to change an outlet. I would certainly instead start with the electrical outlet and locate a YouTube video that aids me undergo the issue.

Santiago: I actually like the concept of starting with an issue, attempting to throw out what I know up to that trouble and understand why it does not work. Get the devices that I need to address that issue and start excavating deeper and deeper and much deeper from that factor on.

Alexey: Perhaps we can chat a little bit concerning discovering sources. You discussed in Kaggle there is an introduction tutorial, where you can obtain and discover just how to make choice trees.

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The only need 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 says "pinned tweet".

Also if you're not a programmer, you can begin with Python and function your way to more device knowing. This roadmap is concentrated on Coursera, which is a system that I really, actually like. You can examine every one of the training courses absolutely free or you can spend for the Coursera subscription to get certifications if you desire to.

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Alexey: This comes back to one of your tweets or perhaps it was from your course when you compare 2 techniques to knowing. In this situation, it was some problem from Kaggle concerning this Titanic dataset, and you simply discover exactly how to solve this problem making use of a certain device, like decision trees from SciKit Learn.

You initially learn math, or straight algebra, calculus. After that when you recognize the math, you go to device discovering theory and you learn the concept. After that 4 years later, you finally concern applications, "Okay, exactly how do I use all these 4 years of math to solve this Titanic issue?" Right? In the previous, you kind of conserve on your own some time, I believe.

If I have an electric outlet below that I require changing, I don't want to most likely to college, invest 4 years recognizing the mathematics behind electrical energy and the physics and all of that, simply to transform an electrical outlet. I prefer to start with the electrical outlet and find a YouTube video clip that helps me go via the trouble.

Santiago: I really like the concept of beginning with a trouble, trying to throw out what I know up to that problem and comprehend why it does not work. Get hold of the tools that I require to fix that trouble and start excavating deeper and deeper and deeper from that factor on.

Alexey: Perhaps we can speak a bit about learning resources. You pointed out in Kaggle there is an intro tutorial, where you can obtain and learn exactly how to make decision trees.

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The only need for that 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 states "pinned tweet".

Even if you're not a developer, you can begin with Python and work your means to even more maker discovering. This roadmap is concentrated on Coursera, which is a system that I really, actually like. You can audit all of the programs absolutely free or you can pay for the Coursera registration to obtain certificates if you wish to.

Alexey: This comes back to one of your tweets or perhaps it was from your program when you compare two techniques to learning. In this situation, it was some issue from Kaggle about this Titanic dataset, and you just learn just how to address this issue making use of a specific device, like choice trees from SciKit Learn.

You initially find out math, or linear algebra, calculus. When you understand the mathematics, you go to machine discovering theory and you discover the concept. Four years later, you lastly come to applications, "Okay, just how do I utilize all these four years of mathematics to resolve this Titanic problem?" Right? So in the previous, you type of save yourself some time, I believe.

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If I have an electrical outlet here that I need changing, I don't desire to most likely to university, spend 4 years understanding the mathematics behind electricity and the physics and all of that, just to alter an outlet. I would rather begin with the electrical outlet and locate a YouTube video that helps me experience the problem.

Bad example. But you understand, right? (27:22) Santiago: I actually like the concept of starting with an issue, attempting to toss out what I know as much as that trouble and comprehend why it doesn't function. Get the devices that I require to solve that issue and start excavating deeper and deeper and deeper from that factor on.

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

The only need 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 programmer, you can begin with Python and function your way to more artificial intelligence. This roadmap is concentrated on Coursera, which is a system that I actually, actually like. You can examine all of the programs totally free or you can spend for the Coursera registration to obtain certifications if you intend to.