Studying Data Science at the Library | Learning Intelligence 39

What's going on, team? Welcome back. We're at, I think we're at number 39 now, Learning Intelligence 39, but I'm excited. Why? We've got a big day on tour. This morning started out, it's been a good day already. I went for an early morning walk, listened to an audiobook, got some sunlight, got some exercise, came home, had some coffee, and now me and my brother Josh, say what's up, Josh. What up, y'all? We're heading off to the library. We're dressed in gym gear. I don't know if you can see, but I've got my gym singlet on and whatnot. Why? Because we're gonna do a couple hours at the library and then we're gonna go to gym straight after. And why are we going to the library? Why can't I just study at home? Well, I like being in an environment where other people are doing the same thing because it's motivating, right? I can study on my own in my room, but it's— I find it when I go to the library, it creates that cultivation of knowledge. Today I'll be getting back into the Applied Data Science with Python course, the one me and my brother are doing. My other brother Will, who's not here, he's at work. Gonna do a couple hours on that, and then later tonight I'm actually really excited. We got a, got like a Roman murder mystery party at Max Kelson, and I gotta, I think I gotta dress up with a toga. I think the last time I wore a toga was like 2014 or something. I gotta act like a character too. I think mine is Felix. I'm not sure what the personality is, but maybe, maybe some kind of intellectual slash upper class. But everything's happening today. Got an article coming out, how I'm learning deep learning part 5, I believe. And it's, it's back to basics. It's, it's sort of, it goes, I started learning Python with deep learning and machine learning, right? But the article dives into why I'm doing the applied data science with Python, which doesn't have too much deep learning. It has one, one little course on machine learning, but I feel like when I first started, I went to the tip of the iceberg with AI, and I sort of skipped all the, all the entry-level stuff, like preparing a dataset, getting it ready and whatnot. And I found most of the stuff we've been working on, Max Kelsen, has been getting data ready. And so what does that mean? Well, well, often with clients, we look at their data and it hasn't been in the form that we can use machine learning and deep deep learning techniques. So I bought the Python, the book, and now I'm learning through this course on how to, how to get data ready in a way to use machine learning and deep learning techniques, which I think is, it's very valuable because you're not always going to find a dataset which you can start using deep learning on straight away. So that's one thing. I'll link the article below, but if, if, as a recommendation to save you, save you some time reading it, if you've learned, if you're learning deep learning and machine learning, make sure you complement with the ability to manipulate a dataset. What does that mean? So use Python. I'm not sure about R because I've only learned Python. So be able to use Python and Pandas and NumPy to transform a DataFrame in a way that you can, you can sort of look at it in a different way. Practice that. That was pretty good. Got about 50 cards done in 10 minutes. That's what I try to do about every day is 10 minutes or so of the flashcards, so I'm not overly learning it. I've got to put them into practice now. Just finished up at the library. Josh, what were you studying? I was studying CRUD in Swift, how to create, read, update, and delete data from a database using Swift. Josh is an iOS developer. He's got an app on the App Store. You might have seen it, you might not. It's called SCT. I'll leave a link in the description. But I was continuing with the Applied Data Science with Python course. I started out with my 10 minutes of Python Anki flashcards. I'm trying to do at least about 10 minutes, only about 10 minutes every day to just get my, my Python syntax knowledge up to scratch. But what's really important is putting it into practice, right? So now we're heading off to gym and actually the Applied Data Science With Python course is amazing. The parallels it has to what we've been doing day to day at Max Kelsen is unreal. Like all the functions we've just gone over, I can start to visualize how I can start to use them with the projects we're working on. Got dressed in our gym clothes so we had no excuse but to come to the gym after finishing at the library. We've done the mind workout, now it's time for the body workout. Yay! Been really loving the gymnastic rings lately. They're like a whole upper body exercise in one piece of equipment. If you want 3 exercises to get ripped, pull-ups, push-ups with gymnastic rings, and hill sprints. And that's all you really need. I gotta go get ready for this Toga Party. Do like a sick little transition, just— Woo! How do I look? You diggin' this necklace? How about my belt? Let's get some light in here. So my name for the event tonight, I think there's a murder mystery and we've got to solve it. I'm Felix Festus, and I'm excited. Who's my enemy, Sam? Uh, some dude on a chariot? Yeah, Speedius is my enemy. So I gotta make sure Speedius doesn't get his chariot racing into the Senate as a form of entertainment. Gladiators! I'm the owner of a gladiator tribe, by the way. Gladiators is where the real entertainment is. My champion Titus will take us to the top. What a night! My toga kind of got disassembled. I just caught the bus home in a toga. It was good fun. But it was a really fun night. It was a murder mystery. My wife, Fabiola— Felix and Fabiola. We were the hosts of the extravaganza of the Gladiator event that we had just in the office, and everyone had their own character. We had to work out— one of my Gladiator, one of my best Gladiators died. And we had to work out who the murderer was by different clues and whatnot. I ended up blaming Fabiola, my wife, and she ended up blaming me. So, some couple we were. But that was— it was a really fun night. Good to mix it up. Good to have a little bit of fun here and there. I think today was a really good day. Brain exercise, studying, body exercise, workout, and a little bit of playtime with the rest of the crew. Now it's time for me to eat some dinner and then go to sleep. And wake up, continue the learning journey. But as always, keep learning. Oh, and check this out, I'm really excited for dinner. Oh yeah!