Machine Learning INTERNSHIP Day 1 Takeaways | Learning Intelligence 29

On the way to the internship. Little bit scared, but you know the same feelings that scared cause, or the same emotions that, the same hormones that cause you to be scared are the same ones that cause you to be excited. So that's what I'm gonna do. I'm gonna flip that scaredness on its head and start to get excited and go in and learn some things. Just caught the train here with my little brother. So thanks for the chat, bro. And yeah, I'll catch up with you guys either after the day or through the day if I can share what I've learned. Oh! My goodness! Oh my goodness! Guys, I had an amazing time at the internship today. It's about 7 PM tonight, so I'm home a little late. I was up really early and now it's dark outside, but I'm pumped. You know why? Because when you're learning great things and you're around great people, it's hard not to be full of energy, and I think that's where I get most of my energy from, is from working on hard things, committing to it, doing it, seeing results, and meeting great people. But I wrote down a whole bunch of notes at the internship, but I took away 3 main key points that I want to share with you that I think are important at any stage of where you go. So, number 1 for me is whenever I go to a place, it's always— whenever I go to meet new people, it's actually the first thing is team. If you don't have a good team, the foundation isn't solid. Whatever you build, no matter what it is, how good the product is, it won't be that great. And the place I went to today, they have a great team. Everyone I met was excited about what they're working on. They're all incredibly smart in their domains and they all are just fascinated by technology, especially AI and machine learning, which is of course what fascinates you and I so much. So wherever you go, if you're starting a startup or if you're going for a job or you're doing an internship, please, please, please sort out the number one thing is team. If you don't, if you don't want to work with someone or if you can't work with someone else, it's going to be very hard going forward in the future. Make sure first and foremost you're going to work for a good team, or if you're building something, you build a good team. Number 2 was projects. And now I can't tell you the exact details of what the company I went to today, what they're working on, but I can give you some idea around it. So say for example you're a law firm and you want to work out how to use your data to make future settlements a more streamlined process. That's the sort of stuff that we were working on. So we were taking case histories, taking the data from that, manipulating a bit and trying to work out what relationships are there over 15 years worth of cases to make future cases more easily streamlined and easy to go from start to finish. And that's just one of the projects. If you go into a place, first of all is team, second of all is projects. You want to be— make sure that people are working on some amazing projects. And even in your own world, you have to make sure if you want to If you want to be driven, you have to be excited about what you're working on. So make sure the projects you're working on, first of all, excite you. And that's the main thing, right? If they excite you, somewhere down the line, they'll excite someone else. So number 2, projects. Number 3 is not really relatable to anything outside of the tech world except for AI, but brain-computer interfaces. But the segue of this is going back into team and talking about people is Be excited about the things that you're learning, right? And be excited about the possibilities of your field. I stayed back at the end of the day with one of the ML engineers, the machine learning engineers that I was partnered with through the day, and we spoke for 45 minutes about brain-computer interfaces. Everything from Neuralink to stuff OpenAI are working on, to stuff he's been thinking about in terms of neuroscience and marrying that with AI. I would say actually number 3 can be have an open mind, right? Go to these things, don't be streamlined in what you want to do. Of course, have your own opinion and be strong on your own interests and whatnot, but beware, there's heaps of other people around and they're going to have— they might not be interested in things you are. So a really good way to build some solid relationships is to be interested in what other people are interested. So there's my 3 major takeaways from the internship. Otherwise, what I was doing is actually just on the laptop most of the day, working in the JupyterLab, manipulating datasets, practicing using Pandas and Python. So if you're going into a data science role or an AI role, make sure your Python skills, your Pandas skills— Pandas in particular because that's the major data science language at the moment— make sure they're down pat. So what do you think? What are the most important things you look for when you go and work somewhere? For me, number one was team. Can you work with the team or can they work with you? Number 2 was projects. Are you excited to be working on what they're working on? Number 3 was— have an open mind. And of course the technical skills like Python and Pandas and whatnot are important, but what I think is more important is being able to take a problem and break it down into smaller steps and solving each step systematically. What do you think? Leave a comment below and, and we'll sort it out. We'll reply to each other and we'll figure out a good list of what people should look for when they're going into an internship. And speaking of comments, last week's comment of the week goes to Jeminiski. I love the way you applied Stoicism wisdom to, to going into a new situation. By assuming the worst-case scenario, you always come out on top. And I'd like to give a big thank you to Jared Gore, who's my latest Patreon. Thank you so much for your support, Jared. I really, really appreciate it. And guys, don't forget to leave a like, subscribe if you want to see more, but Last but not least, as always, keep learning. We'll see you next week.