Udacity AI Nanodegree vs AI Masters Degree? | Livestream Q&A

And we're live! What's happening, team? It's a beautiful, beautiful Saturday morning. I just finished a workout, did a little bit in the sauna, and I am a bit late. I had some technical difficulties getting this monitor together, but we are here, we're going live, and we're celebrating 1,500 subscribers. I'm here to answer your questions. I've been collecting a few that have trickled through, through the internet, through comments, through emails, through Twitter, Instagram. If you— oh, by the way, if you ever want to, want to message me somewhere, my handle everywhere is @DanielBourke. Twitter, wherever you want, you can hit me up there. If you want to ask a question, leave it in the chat. Listening or watching this at a later date, leave a comment. If you're on the podcast, because I am going to put this as a podcast as well, shoot me, shoot me something wherever you like. You can ask me, ask me whatever you want. You can contact me at mrdeburgh.com. But let's jump right into it. We're going to be here for the next half an hour, 45 minutes, who knows. We'll see, we'll see where we end up. And look at that. My hair's all over the place. I just had a shower. Been, been, been too rushed this morning. Anyway, we have a fun one here from, uh, Cora actually, which is Udacity AI Nanodegree versus an AI Master's Degree. Now, why did I— let's, let's, let's jump back a few steps. Why did I decide to study artificial intelligence online? Well, I went to a careers or a courses expo at my old university. I just graduated from a Bachelor of Science studying nutrition and food science. Really loved that. Finished up that in 2015. Went back at the— towards the end of 2015, actually, uh, to see, hold on, can I do a master's in something? I looked into computer science because I love technology, and It was something like $42,000 a year just to study a master's in computer science, at least 2 years. And then I was reading, I was reading through, they had a brochure there, right? The computer science brochure. I was reading through all the stuff and I was like, I want to get straight into this, this new cool stuff. I'll be honest, half the things I didn't understand on the page. At the time, I sort of had a little headspace about machine learning, AI and whatnot. But, um, I looked at the curriculum and then thought, hold on, why don't I just check online? Why don't I just see what, what courses are available online and go from there? Went online, found some awesome stuff, and then that's where we're here. That's, that's where we ended up. That's, that's how the YouTube channel started. That's how I got a job, uh, from creating my own AI master's degree. Now, that being said, What are the differences? Okay, first of all, you've got cost. That's it. When I checked it out, it's more expensive to do it at university versus online. You've got online versus university. You sort of, if you're going to university doing an actual master's, you might be in the track there. So you don't really have as much flexibility as if you were to go online and learn a whole bunch of different things. Like I had the beautiful option of going back and figuring out what I needed to learn. If I was weak in something, I could go learn that. If I wanted to learn something else, say for example, health and data science or machine learning and AI, like for example, I'm planning on doing a genomics course in AI. So that's the human genome and mixing that with data science and machine learning. So I'm really excited for that to be in the future. Then you've got environment. So fair enough, if you do an actual master's degree, you'll have other students usually around you. Which is a great thing to study in an environment versus online. You kind of— I kind of spend a lot of my time in this room. Like, this is where I've done the majority of my study, all through here. There's my brother. Sup, Josh? It's a lonely— it's a— well, yeah, actually, yeah, it can be lonely at times, but that's a choice you make for learning, right? If you're going to learn any any quality stuff, you do need to spend time, time alone, away from people, away from everything. So that's going to be wherever you are, no matter what you're trying to learn, is there will be a time where you have to figure something out for yourself. That way you can, you can really understand it. And then I went over, um, I'll put the link in the Quora question in the description or something like that because I wrote it in detail. And the quality of the courses, the courses I've done online are incredible, right? The Udacity Coursera, all of them, highly recommend everything that I've done. You can find everything that I've done in various, various links, uh, they're throughout all my, my YouTube videos. But anything by Coursera, Udacity, I highly recommend. Um, and plus, in terms of cost, yes, they are expensive. There is financial aid for Coursera. I've, over this course of the last year or so, I've spent $3,500 or thereabouts on my learning versus $42,000 a year through an actual master's degree. And then we go one more thing. What's, what's the future options after that? Right? So Udacity, for example, have an option for career services. I've never used that because I was lucky enough, fortunate enough to get a role before I had to do that. So I'm currently working as a machine learning engineer. Do some companies prefer you to have an actual master's degree? Yes, of course. But the way I see the future going is, I just think from my perspective, right, if I was hiring someone, I rather than looking at your certifications and your degrees, I mean, they're great. My dogs are having a good time. They're great, of course. But I would rather look at your body of work. What do I mean by that? It's like, what have you done? What projects have you worked on? Okay, so you can have all the certificates in the world, but have you got anything to show from that? Like, as in, I worked on this and I did this. That's what I take as being very valuable, hence why I create so much YouTube videos, create so many articles and whatnot. Is because I figured if I'm going the non-traditional route, which is studying on my own, I'm going to need to have a body of work to back me up, which is essentially all of this, these YouTube videos and, and, uh, and articles and, and projects on my GitHub and whatnot, uh, are my resume. They're, they're the equivalent of me having an AI master's degree, and That being said, even though you— if you were to complete a traditional master's degree or I was to finish off my, my own self-created one, the learning never stops. And that's something we'll jump into in the next question, right? So I— even though I'm doing— I'm up to the last course, right? I'm up to fast.ai. I've worked through it. Actually, big shout out to fast.ai. If you want to do a quality AI course and you've got some experience in, in Python, let's say 6 months to a year if you know a little bit of Python. Go to fast.ai. Literally type that into your browser. There you go, fast.ai. And it's free. I'm doing that now up to Lecture 5. And it is, it is incredible. So as I was saying, I'm up to the last course. Once I finish fast.ai, I'll be officially finished. Curriculum, the original curriculum I set myself for the AI Masters. But there's going to be plenty more to go. There's a genomics course I mentioned. I'm planning on getting more into statistics. I want to get really good at Python. So I'll, I'll keep practicing that. There's some math courses I want to do on Khan Academy. There's a lot to do. There's no shortage of work. Let's just say that. But yeah, that's in a nutshell, AI Masters versus, uh, Creating your own, or Udacity's AI Nanodegree. I mean, it's, it's figure yourself out. What, what do you— how do you learn best? Do you learn best in an environment with other people, or can you learn on your own? For me, I can learn on my own. I, I'm motivated, I'm driven, so I chose the online route. Uh, if you, if you prefer having people around you, that's, that's a great— learning at a university with a traditional master's degree is is an awesome option and probably at the moment still definitely, definitely a great thing to have if you're going into further research or if you want to go into further research as in doing a PhD or something, or if you want, or if some company requires you to have a master's degree. But even if they do say they require you to have a master's degree, I'd apply anyway. If you've got the skills, if you know how to apply them, apply anyway, ignore the requirements. If you think you're good, you're good, but don't get too big-headed as if you're the best in the world, right? 'Cause we can all still learn more stuff. So that's question 1. And then we got a fun one actually. It's from my boy Merlin. And this chair I'm in is really, anyway, Merlin, my boy. If I was graduating high school, what would I do? Hey Paul, how you doing brother? Nice to meet you, mate. If you have a question, Paul's just joined the live stream for everyone listening and watching at a later date. If you have a question, just leave it in the comments. Hey, my boy Merlin, I'm about to answer your question. I'm about to unpack, unpack this question. So if I was graduating high school or if I was at the end of high school, Um, what would I, what would I do? That is a massive, massive question. And you know, I thought about this and it's like so many different options went through my head. I mean, like, it's always that, that hindsight, that hindsight. If, if only I could go back and, and know what I, what I knew now, right? So I'm— when did I graduate? I graduated high school in 2010. I turned 25 this year in about 2 months or so. You better say happy birthday to me. No, I'm kidding. Um, 1st of September, really though. And it was, it was, it was a strange period, right? Because in high school we had it, there was a lot of pressure. I remember being really stressed in year 11 and 12, and it was like, um, which is the final 2 years of school. And it was like there was this one test called QCS, Queensland centralized standardization or some, some bullshit, right? And it was like all this effort, like you need to, to get a good grade, you need to get good, good whatever, you need to get this, that, that. And it was all these, these, these subjects I didn't really enjoy. I enjoyed some of them, but there was things you had to do which is just not that fun. And I mean, I had an Xbox. Will dictate your future. And I'm like, man, this is— I'm trying to do well, but I really don't like what I'm studying. And if I get bad grades, I'm going to be stuck in something for 40 years. And yeah, so it was a really, really tough time. It's like you have to decide what you And so what would I do if I went back? I'd probably— it's really hard to say, right? Because now all my answers are going to be influenced by, by what I know now. I'd be like, yeah, I'd get straight into technology. I'd learn math. I'd learn how to code, which, I mean, all, all amazing skills which you can still do now. But I think you've got a few options, right? You've got— you graduate from high school, you could go to university. That's, that's, that's what I did. I went straight from high school to university, uh, failed the first 2 years because I was just following, following my friends and, um, didn't, didn't really have, have much meaning. That's, that's a big thing actually. That's a big thing. Uh, we'll get onto that later. Meaning versus whatever. If you, if you do anything, search for meaning in something. And what I mean by that is, is, is if I find meaning in creation, right? And so I create a lot of things. I enjoy it. So that's, that's a big thing, but you won't find that until you practice over and over again. So going back to what we said, you're finishing high school, you've got a couple of options. We got actually fucking world's your oyster. You can choose whatever you want. You can go straight to university, you could go traveling around the world, have an amazing experience. You could go get a job, you could start a business, you could do absolutely nothing at all for 6 months if you still live with your parents and they're happy for you to sit around and chill out all day. I mean, that's a great option. Really, why? Because there's no right option, right? We all have beautiful and amazing and diverse lives. But what would I go back and do? I would probably, instead of going straight into university, this is me personally, instead of going straight into university, I would probably spend some time saving some money and just reading a bunch of books, like just literally going to the library, studying whatever I want, doing some courses online. Now, 8 years ago, the online courses weren't nowhere near as good as what was available, right? I'd study a few things, I'd practice writing, like, so And this is just stuff I do now, right? But it's— this is what I'd practice back then. Reason being is so I would be exposed to so many different things. Because when you come out of high school, unless you've been started or you've started already, like through younger years, you're not that good at anything. I certainly— I was okay at a few things, but you're not really really good. And so why I would, I would just absorb this knowledge and not necessarily go straight into a set path. I'd be interested. I get a book, I don't like it, I'd put it away. I'd read another book, I'd go on to something else. I'd study philosophy, I'd study, study math, I'd study biology. Why does, why does a bird fly? How can a bird fly? And the reason why I just, just go over all these eclectic things is to find something that, that I was interested in. And then get real good at it, right? What it is, if you get really damn good at something, you'll, you'll have no trouble finding, finding success, whatever you define success as, later in life. Uh, whether it be a skill like, like programming, whether it be writing, whether it be videography, whether it be whatever, insert, insert the blank, right? There's no right or wrong one. Of course, some are more in demand than others at the moment. But if you get really good at something, you'll be miles away. So the one thing I've done for the last 8 years is work out. And now I'm seeing the transformation of what I graduated high school as to what I am now. And I use that principle for anything now. If I was to practice one thing for an extended period of time, just continually getting better and better and better, then it's compound interest over time, right? So if I was graduating high school, I would get really good at talking to myself, understanding what I was interested in, understanding what I wasn't interested in. I'd re-read a bunch of books, watch a bunch of YouTube videos. YouTube is absolutely amazing now in 2018 versus 2010. Just, just learn a whole bunch of things. I'd probably get, get a part-time job somewhere, wherever. But this is, this is me, right? This is me in my country with the facilities I have. I'd probably, probably try and start a business of some sort because look at, look at the technologies we have. You could start a media company and do media for other businesses. For example, dentists, right? They just have a lot of cash, and you're a young 17-year-old, you know how social media works. You could run their Facebook ads, their Instagram ads, do, do their, their content creating, and charge them $1,000 a month or something like that when you're fresh out of high school, because you're the one who knows about these technologies. And they don't, they don't really know it. And once you start to get them customers from the work you've been doing, you'll, you'll get more customers because it'll just, it'll just be an onflow. So a whole bunch of, whole bunch of things. But at the same time, I'd be taking note of the things I learned and thinking about, oh, I get really good at this. And if I get really good at this, it will take me a while, But I can use that, I can use that as a, as a market myself in the future as being really good at that skill, right? And so, yeah, there's no right or wrong answer. Just, just try a bunch of things out. If you can go explore somewhere, I learned more about myself in 3 weeks in Japan, there by myself, than 6 years out of high school. Why? Because I was just by myself all the time. I was, I was traveling around. If I wanted to go down that street, I wanted to go down that street, I just go down that street, right? So I just said a whole bunch of things, but realistically there's no right or wrong answer. Spend a lot of time doing nothing and it will, it will, it will, it might not appear. It might take you another 15 years to work it out, might take you 45 years. Whatever it is you want to do. But just beware, everyone's on the same, same journey, but, but different at the same time. So don't put too much pressure on yourself. Enjoy it. This is what everyone's going to say. And don't worry about following or finding your passion, which is, which is a load of crap, because if you're out of high school, you probably won't be that good at anything. And you only start to develop a passion after you continually get good at something. When I started working out, hated it. It was shit. Why didn't I get results faster? Now I'm pretty good at it. It's, it's a passion of mine, right? Because I've gotten better and better and better and better at it. When I started coding Python, it was crap. Why isn't this function working? Now I start to understand a little bit more. I'm starting to get into it and I'm really, really starting to enjoy it and it makes me want to learn more. So thanks for the question, Merlin. Um, keep hitting me up wherever you want, uh, message me. I know I just sort of blurb on for about 10 minutes or so and tried to unpack it, but, but I know you're in a good place, so just keep doing what you're doing. All right, well, let's go, let's go to the chat. Paul, thank you for the question. So my question: when studying AI, how did you know what you were learning was going to get you a job versus going to a college degree? Um, good question, Paul. How did I know what I was going to study was going to get me a job? Um, well, what I was— that I just looked at, looked at trends, right? Looked at the world. And Jeff Bezos says— oh, well, that's in one of the Amazon shareholder letters. Which is riding the, the momentum, the wave of society, right? Um, I've probably messed that up. Oh, the tailwinds, the tailwinds of society. And what he means by that is, where's, where's society as a whole? Where, where's the technology landscape? Because technology fundamentally is what drives the human race forward, right? All of our advancements in, in terms of standard of living and whatnot. Imagine doing this 100 years ago. I would be— I'd be probably killed, right? Because you can't do this 100 years ago. So technology is what pushes, pushes us forward. And so I knew, uh, I had to get involved in some kind of, of technology. I'd always been interested into it. Um, well, that's just me personally, right? And I was working at Apple and I was listening to a bunch of podcasts and this thing kept coming up. It's like machine machine learning, machine learning does this, artificial intelligence is going to take over the world. And I'm like, whoa, this is really exciting. A computer that could learn on its own. I remember being as a kid and like playing against these, these bots. Oh, there's one of my puppies. Hey, Belle. I remember being as a kid and playing these computer games and these AIs, they're making the decisions on their own. They're pretty crap, right? But And then I started to hear about they're getting better because we've just got heaps more computing power. And so I figured, well, actually, I started to hear about all these companies starting to use machine learning, artificial intelligence, Google, YouTube, Facebook, wherever, name, insert blank, whatever. I'm like, I need to get on this. And so I just looked up online, how can I learn these technologies? I even went to university to see if I could learn them and they didn't have any of it. I read the curriculum of computer science masters and I just figured Hey, if I learn these skills and I'm confident in my ability and I get good at them and I keep practicing over time, eventually I will either get a role somewhere or I will start my own thing. And I still plan to do both. Well, I've done one. I've got a role somewhere. And in the future, I do want to start my own thing in the health space. So I want to use machine learning and artificial intelligence and data science and all the keywords, all the fun terminologies to help to improve health, right? Because I think health is a slow, like don't worry too much about an asteroid or nuclear bombs and all that. I think we're gonna slowly kill ourselves with bad health. That's another topic we can go over later. But did I know I was gonna get a job? No, I was sort of— I knew I would eventually somehow. I knew the end goal, but it's like I didn't know the steps. I was kind of figuring it out on the way, right? I knew where I wanted to head, but I had to— I was climbing this wall, but I had to build the ladder on the way up, right? And I sort of stumbled into this job I've got now, and I got really lucky. I'm really, really happy for that. All happened because I just was sharing my work online and continually getting better. And I had, I'll tell you a story. I had a review where I'm working now. It was a half-yearly review at Max Kelson and I'd only been there for 2 months and they asked me, what were 2 of your biggest achievements for the year? And one of them was getting a role, right? It was, I was, 'cause I was in 2 minds, right? You always had that voice in your head. 'You can't do this, Daniel. You can't do this.' But then it's like, 'No, I can do this. I'm not going to listen to you.' You just got to shut that voice out or use it as motivation. It's like, 'I'm going to show you what's what. I'm going to do what I set out to do, and I'm gonna— I'm gonna do it. It's gonna— even if it's gonna take me over a year, I'm gonna keep going. 5 years, whatever. If I said it, set it out, I'm gonna keep going.' Having that belief, having that belief is 50% of it, right? And so I told them, yeah, getting a role is one of my, one of my biggest achievements because I really, a year ago I didn't know anything about this. And it's, it's, it's been a, it's been a fast ride. It's been, it's been good fun. And it's, it's all come, uh, that's why I make these videos, right? And that's why I write these articles. It's all come from, from just showing up every day and putting in the work. And it's, it's I'm not special. That's another reason I make these videos, because I'm not special. I'm just a guy who decided to start learning it and sharing online. So that was one of them. And the other one was Spartan Race, of course, right, which is what I mentioned before. Before I ran 50 kilometers in a day, I never would have thought I could. And then I did. And so now the barrier's higher. Now I have to run 100Ks or jump out of a plane or something like that. Um, so yeah, that's, that's, that's essentially— did I know I was going to get a job? Yes. Did I know how? No. I figured that on the way. Um, because there's a saying, I'm not sure, I love, I love quotes, right? A man who has a why will figure out anyhow. So I knew what my why was. I knew I, I was studying this stuff because it fascinated me and because that's where that's where society is going, right? It's the tailwind of society. It's the Amazon. They're implementing, uh, the latest and greatest technologies, uh, to, to drive their products forward. And, and that's what I figured to myself. I'm going to learn the great latest and greatest technologies to get into this field and, and use them, uh, with, with what I, what's what I like to, to make the world a better place. He or I will find anyhow. Now we have one from Fasan Rashid. How many hours do you put in study each day? Great, great question. Uh, I do, I do change it up day to day, but I'm, I'm a big fan of doing, doing a little bit each day, right? So, so, so I was into talking with my brother the other night actually about our workouts is instead of going balls to the wall every, every session and being sore the next day and you can't really move. Uh, we go 60 to 70% each session but do it every day. So instead of missing out 2 days because you're sore, you're doing a little bit every single day. And that's the same philosophy I take into study, right? Is I, I've worked it out. I've got about If I'm on a really, really like the top of the top good day, I might have 6 hours of brain work, 6 hours of undistracted brain work. And that's at the very top, right? And once I get to that point, I'm gone. Like, it's like anything I learn is just— And so I mentioned before in a previous video that I forget— well, I remember 1% of everything that I learn. 1%, that's it. You learn something new and it builds a connection, but it's only, it's only 1%. A course is, is 100 hours long and I'll remember an hour worth of content just because there's a lot going on in these, in this 3 inches of our skull, right? So what I aim to do is just a little bit every single day. What's, what's been interesting me, I'll, I'll read something about it, I'll do some writing about it. Where do I need to improve? I'll, I'll write about that as well. Is I'll be like, okay, I need to do these steps. Maybe I'll take on this next. What skills have I been learning lately that I could be improving on? Or what's something that's been holding me back? For example, lately, and I'll get into this in a next question, actually I've got one here, is I've been really diving into Pandas, right? Because that's a Python library that you can use to manipulate data frames. And so I've been, I've been pretty, pretty weak in Pandas. I'm really trying to get better at that so I can manipulate a DataFrame. And so I'll try to do a little bit each day. And so what will it end up averaging? I would say 3 hours maximum average. So each day, so some days I'll do more, some days I'll do less, right? Some days I might even do nothing, but on those days I don't feel so good. All right. I like doing a little bit every day. So every morning I get up, I write a checklist of like 6 things I want to do today. So number 1, well, one of them is workout. One of them is livestream. Here we are. How you going? The other one is thefast.ai, the next lecture, and then write down a list of topics that I need to go over for that lecture. So that's what I'm going to get to after this. So just a little bit each day. And that being said, if you don't do it, it's important not to bash yourself. I mean, be firm on yourself, but don't be mean, right? Treat yourself as though you were someone you have to care for. So if you have to care for your sick parent, your sick mother, you're not going to be mean to her. Or not even sick. Like if you care for your mother, right, you know, it's not like you're going to be mean to her. So treat yourself like that, right? And if you do a good job, tell yourself that. Give yourself a little pat on the back. No, don't, don't take too much of a break from— because you're patting yourself on the back and just get back into it. So a little bit each day, that's what I believe in. Um, Sandeep has a question. Uh, what are the topics to study in series Get Be Zero to Hero in Data Science? Oh, Zero to hero. I don't know what hero level is because I'm not there yet. But if I was from zero, it'd definitely be Python, Pandas, NumPy, all three of those. I think if you can manipulate a data frame and derive some insights from a Pandas data frame, if you're not sure what that is, it's just like a table of numbers. And the beautiful thing about Pandas and Python is that you can manipulate them. So that is, that is the place to start is get around Python spend a few weeks on Python, spend a few weeks on Pandas, spend a few weeks on NumPy, and then start to merge them together to make some, some beautiful data frames and whatnot. So they're the three foundational things you have to do. How much of each? It's really hard to say. DataCamp's a great resource to go to to start learning. There's plenty of stuff on YouTube. I just ordered a book on Pandas. I'm not sure entirely what it's called. Let me get back to that. I'll put it in the description at some point, but I think it's, it's just Pandas by, by Wes McKinney. If you Google Pandas Wes McKinney, that's the book I ordered. So, but what's a hero level? I mean, that's— I don't think you can, you can define that because there'll always be something, something new to learn. I think hero level, maybe you can, you know, the top 10 data science algorithms, you can input them whenever you want, but realistically it's going to be problem by problem. The top 10 algorithms might not work for a problem you come up, you face in the future, right? As we start to gather more data, we might need some new algorithms. So that may be a hero level if you invent something new to analyze stuff. But to start off, Python, Pandas, NumPy. If you're an R person, I know nothing about R, go for R, but Python is— you probably can't go wrong with Python. Um, Fossan, do I have a university CS/ML degree? Uh, I answered that at the start of the stream, but, but thank you for the question. Um, no, I don't, my friend. Um, I don't have a CS or ML degree. I created my own artificial intelligence master's degree. Is it backed by anyone? It's backed by me. I don't get an official certificate at the end, but you can copy that link and put it in your browser. That will go— I don't think you can share actual clickable links in the comments, but you put that link in your browser and it will go to the courses I've been doing. I don't have an official CS or ML degree from a university. Victor, do I think that animators and the musicians can be replaced by AI for tasks like game development? These are— that's a good question, but these replacement questions, I don't know, it's— I think AI replacing things is really— it's a really tough one. Reason being, I think no doubt probably in the future there'll be AI that can create graphics and, and, and games, whatnot, as, as good or indifferential— in— yeah, that word, uh, from people. But not anytime soon, right? Well, maybe actually, who knows, right? Because this stuff moves so fast. Um, Um, but that being said, I don't know anything about animation. I'd love to get into it, actually. I'm planning on learning some animation so I can really level up my videos because I'm a visual learner. I'd love to create some animations. Um, if an AI could do that for me, that'd be awesome. Burping again, sorry. Um, but I don't know, I think a lot of the fun comes, comes from, from creating it. But like game development, definitely, like I, I don't see it a bad thing in the future. Well, not a bad thing, um, uh, let's say something that, that, let's say in the future, uh, you, you could go on to your Xbox and have an AI generate with— generate a completely new game, like completely to you, and it'll be like 'Xbox, make me a game with dragons, dinosaurs, and volcanoes,' and it will make you a game like that. I think that that's, that's definitely coming. When? Hard to say. In the future? Yes. I can't answer that. I'm not entirely sure. But great question. Um, Paul, when did you start building your presence on your journey learning about AI? I started— when did I start? About a year ago. Let's say a year ago. Let's say this time last year, I was really, really ramping it up. That will probably be different if you go to my YouTube channel. I probably got some uploads before then. But definitely, definitely a year ago, I was well and truly into the deep learning Nanodegree. And I started by just documenting doing 100 days of code. That's literally how I started, was coding every day for 100 days. Writing about what I was doing, sharing that online. I started with a series called How I'm Learning Deep Learning. That's probably— I probably got to wrap that up soon because I've finished, finished a lot of the courses. But again, it's always a continual learning journey. So I'll write another article about that in the near future, actually, on what I've been doing lately, because I love writing, I love making videos. But at least about a year ago, I started with 100 Days of Code. You can see that I've got a series a playlist on my YouTube of, of I think 25 maybe. I don't know how many videos of 100 Days of Code. And Learning Intelligence is the other series, which of course, which is documenting learning artificial intelligence, which has been really— question, Paul. Um, the book, yes, that's it. That's the book, Python for Data Analysis, Data Wrangling, and Pandas. Do I plan— oh, good question. Do I plan on moving to the US or somewhere else? I'll, I'll, I'll let you know, right? So I had— I actually had a flight booked to the US on July 6th, and so what is it now? July 15th, 12th, something, 14th, July 14th. So it should have been 8 days ago. Why am I still in Australia? Well, because I booked the flight before I got a role at Max Kelson. and I, I booked it because I'm like, I'm going to go to where the technology hub of the world is and just try my luck at getting a role somewhere. That was my idea, right? And so I'm like, I'm going to back myself. I'm going to fly there. I booked it one way, right? So I had a one-way ticket. I had US Brisbane to LA, actually the return ticket because you have to book a return ticket to get into the US unless you have a visa. Long story short, anyway, I was going there indefinitely. That was my plan. And then all of a sudden I got a role at Max Kelsen, and I figured I, I got into it, and I'm like, this is everything that I wanted to be doing right here. And I said, well, I can always go to the US, but I'm learning some great stuff right, right here. So I just figured I'm gonna push it back, and I'm going in September. Actually, this is— I can do a sort of announcement. I'll make a proper video when I, when I actually start to go. September, I'll be there 12th. September 12th to October 6th, I'm flying into LA, probably do some stuff in California, go to Austin, go to New York. Will I be looking for a role? Most likely not, because I'm loving what I'm doing back here. But who knows, right? The universe works in beautiful ways if, if you just let things happen. So, so we'll see. But yeah, I'll be there for 3 weeks. If you want to, if you want to meet up with me and we're in the same place, I'll be pumping where I'm at, at the socials, making videos, whatever, or while I'm over there, hit me up @MrDBurke on Instagram, Telegram, Twitter, wherever. But yeah, I'd love to, love to come hang out with anyone who's, who's in the US in that time. So yeah, um, that was— that's to answer your question, did I plan on moving to the US? I actually did plan on it, but I've changed it now, so I'm only going for 3 weeks. Would I go to Europe or something like that? Maybe, but I love the heat. So, California is pretty much exactly like Queensland, like in terms of weather. So, I'd probably go there because, yeah, I'm a heat-loving creature. Europe's probably a bit too cold for me, and that's a bit weak. But I will probably go to Europe at some point and explore because I love exploring. Who doesn't love exploring? So we have Fasan. Are you more into applied data science or theoretical data science? How you covered math prerequisites? Great question, Fasan. I'm well and truly more into applied data science because I'm not smart enough at the moment for theoretical data science. So I— where I know that I'm going to be heading in the next 5 years is using the techniques I've applied I've learned, sorry, and applying them to the health field. How does that look like? I'm not entirely sure. I'm planning on— it definitely has to be something to do with genome data and working out personalized medicine. As for theoretical data science, I'll leave that to people who are much smarter than me to figure that out, and then once they figured it out, I'll happily use their techniques to further my endeavors in the health field. And how did I learn the prerequisites for math? All through Khan Academy, uh, stats, calculus, linear algebra. Khanacademy.org, I believe. Amazing courses on there. Um, if I need to learn something, I go there and upskill myself there. Is it the best resource? I'm not entirely sure. It works for me. That's, that's the most in Khan Academy, Coursera, Udacity. If you're a written learner, like if you, if you, you learn better by reading books. I'm definitely a visual learner, but I still, I still like to have books. Paul, how old am I? I'm 24, 25 on September 1st. So I'm excited. I love birthdays. I really do love birthdays. All right, now we go, we go a question from, from the— I've got some, some written down here. So we have real life data science versus online courses. And I'm sorry, I forgot who— I didn't write down whose name this is from, but thank you for the question. I believe it was in in the, in the YouTube comments of, of one of my other videos. So what is real life versus online? Well, first of all, a lot of online stuff is, is, is clean. And what does that mean? Well, it's the datasets, they're ready to be used, they're ready to be, to be put into, into some machine learning algorithms or some deep learning neural nets or something like that. And all you'll have to do is push, push start or code up the neural net and just push the beautiful data straight through, let it compute, and it will, it will come out as an output. But is that the same as real life? No. So that's why I've been so interested in Pandas lately, is because there's, there's no shortage of data in the world at the moment. Well, well, there is, but there is also a lot. But the stuff that does exist is generally not in the best condition. And what do I mean by that? Well, you'll get it, and then you'll try to— you want to use some, some algorithms on it, but you've got to, you've got to play around with it, make— turn it into a form that you can use. And so The data that we've been getting lately, and I can't share the specific examples because of contracts and privacy, a lot of the data is very, very private. So, but it's— there's been a lot of it. Well, in some cases a lot, but let's just say there's been a lot of it, and it hasn't been in the form that we'd like it to be in to get our insights from it. And so that's why I've been preaching lately is Pandas, because ultimately, like, to feed an input into a, into a deep learning neural net, you've got a big table of numbers and you want to feed it into, in there so you get an output of, of what the relationships are between those numbers. But if you don't have that, that big table of numbers that looks good, or if, or if it looks bad and you put it through the network, at the other end you're going to get bad outputs, right? That's the, that's the golden rule in machine learning and deep learning is, is bad inputs, bad outputs. Good inputs may be good inputs, it's not guaranteed, but you're definitely guaranteed bad, bad outputs if you, if you have bad inputs. So it's all about getting the data ready, cleaning it. That's— if, if you're not sure what data cleaning is or data manipulation, I would, I would probably, probably Google that. I might actually write an article article or do a, do a video on that in the future. I've just lost all the chat. Oh no, it's still here. So yeah, that's a, that's a very valuable skill to have in real life versus online courses. I think online courses touch on it a little bit in terms of cleaning the dataset, but if you want to practice on your own, go to Kaggle or something like that, get a dataset, play around with it in Pandas. Or get the Pandas book. I believe I haven't used it yet. I think there's some examples in there of how you would clean it. Then again, everything is going to be different. You might learn a beautiful way to prepare this, this lovely text data that you've got from one place, and then you go to the next problem and you've got all these images and everything you've learned on that text data. Doesn't work for the images. So it's a continual— as I said, it's always a continual learning process. So we have here, uh, for San, apart from Udacity, have you ever tried platforms? Coursera? Yep, definitely. edX, also good. Um, not so much on edX, just, just because I haven't. Uh, that doesn't say— mean that they're bad, but Coursera, Udacity, highly Highly recommend both of those. Paul, would you still follow the same courses, programs you used to learn, um, you know, now, or would you switch things around? Uh, great question, Paul. I mean, again, it's like that, that hindsight thing is, is would I, would I change it around? Um, you know what, if I was starting again from fresh, I'd probably spend a little more time upskilling myself on Python. Because that's— the math is important, but I'm from an applied sense. I want to apply these techniques. I don't necessarily want to, to be thinking about new ways to do backpropagation. That may be something in the future. But what's backpropagation? It's essentially just applying the chain rule across all of your functions in a neural network. It's, it's, it's beautiful. But there are better ways to— well, anyway, it has its limitations. Let's not get too deep into that. What would I change? Yeah, I'd probably spend a little bit more time getting into Python. When I started doing deep learning, the Deep Learning Nanodegree, I'd done Python for 3 weeks. And so I struggled a lot. Well, not a lot, but at times I struggled because the syntax and the functions and the layout. I mean, I'm all— I'm going to continually learn Python regardless, but if I was to start again, I'd probably at least do 1 or 2 months of, of, of hardcore Python, like just, just get around it. But otherwise, I'd follow everything places, um, or be— I'm gonna keep as I go over time anyway. Whatever you're doing, if you're a Python dev at 15, I wish I was Python dev at 15. But if you're older than 15, even if you're 45, never too late to start whatever you want. If you have a belief, you can do it. So just, just keep going. There's no secret to it. Just keep improving yourself, uh, build something awesome and share it with the world. Send dip. Math, Python first, really depends on what your goals are. For me, I wanted to apply these techniques, so it's Python first. If you want to go into machine learning PhD or deep learning podcast, so the audio version of this— and my connection keeps going unstable, so what I'll do is wrap this up. But yes, Sandeep applied Python first. I went Python first. Because to then, but if you want to do further study, PhD, program, etc., do both because you'll need math and programming skills. Hope you all had fun. I really had fun. I love doing these. If you have any more questions for me, again, hit me up @MrDBurke. Thank you all so much. 1,500 subscribers. Woo! In a previous livestream, I think it was 1,000. I don't really have a number goal. I just like making videos. I like hearing from you guys. I like, yeah, I like being here, right? I love these technologies. I'm excited about them. I think they're powerful. I think they can, they can help us in, in, in many different ways. And is there a right way to do it? Who knows? But as long as you're, you're having fun, you're continually challenging yourself, That's what matters, right? But thank you, Paul. Thank you, Dogukan. Thank you, Fassan. Thank you, Sandeep. Thank you, Moon, because I'm hungry after this morning. This is a little trial, um, I can't— when I want to see it come to my YouTube. But peace out, homies, and as always