this book taught me how to learn. It's how I learned SQL in order to pass my data science interviews and ultimately get my first data science job. It's how I learned how to make videos and start this YouTube channel and this entire business. It's how I learned AI, and most recently, it is how I passed my driving test after less than 2 hours of study. The Knowledge Test, not the road test. You should definitely study for longer than 2 hours on the road test. Anyways, in this video, I'm going to cover the book Ultra Learning by Scott Young, go through the concepts, and talk about how to apply these in your life and how I applied them to my life. This is the book that kickstarted my self-learning journey, and it just changed my life, and I hope it will for you as well. Okay, Homer, I'm ready to learn. What's the first lesson? This book is structured to cover the nine principles of ultra learning, but before we actually get into that, let's actually define ultra learning. Ultra learning is a strategy for acquiring

skills and knowledge that is both self-directed and intense. The three keywords here are strategy, self-directed, and intense. That is what describes an ultra-learning project. For example, one of the first ultra-learning projects that Scott Young, the author, did is to complete the entire MIT curriculum for a computer science undergraduate degree in only one year and without actually attending MIT or going to any of the classes. By the way, this was back in 2011, so there were not yet all of these free resources and courses that are posted on YouTube and all over the Internet. It was a lot harder back then. So, his strategy was to list out all the units and all the courses that he had to take and focus on passing their final exams and the projects. It's self-directed because he wasn't actually attending the school, and he was just learning everything himself. And it is intense. He did everything in one year. For example, for his multivariate calculus class, he literally studied everything in 5 days and then did the exam. And I finished single-variable.

calculus. I started the course on Monday, and I finished writing the final exam Friday afternoon. Just to be clear, Ultra learning is not confined to a certain topic or just for passing exams; it is for learning anything that you want to learn. Scott also used ultra learning to improve his art and be able to draw realistic faces in only 30 days. Eric Baron, I think his name is, used these principles in order to single-handedly learn game design and create a really, really popular game called Stardew Valley. I've gathered all the necessary strategy and tactics to finally defeat you. Principle number one is meta-learning, AKA designing a study plan. According to Scott, before you jump into learning, you should spend roughly 10% of the total learning time on figuring out what is the best way of learning the thing you want to learn and designing the study plan. For example, for his MIT challenge, it was 12 months in total, so around 10% of that is a little bit over 30 days, 36 days, which he devoted to

figuring out what's the best way to learn. So, actually, in a bit, I'll show you how to cut down on that time period a lot. But first, let's cover how to design a study plan based on the why, what, how principle. Why is why do you want to learn something? For example, I want to learn Japanese because I want to be able to watch anime without [Music] subtitles. What is what? What are you learning? I'm learning the structure of the Japanese language, the pronunciation of things, and the vocabularies. And the how is how do you learn these things? Doing research. Maybe by like watching YouTube videos, going on Reddit, and maybe looking at different programs and course curriculums. The book recommends spending a lot of time doing this, designing your study plan, because if you're on the wrong study plan, then ultimately, you know, you're not going to be learning the right things. He even goes as far as saying you should conduct expert interviews to check over your study plan. But I have good news. You can potentially

cut down this time by like 80-90%, even like 90-95%, by using something like this prompt for ChatGPT or whatever your favorite AI model is. Since AI is trained on the knowledge of the internet, and it can also search the internet, if you're able to explain properly what kind of study plan you're looking for, it is really good at making that custom study plan for you. If you want to be extra sure, maybe you can take that study plan and show it to somebody who, for example, already knows Japanese. But yeah, I find that for metal learning, AI is a tool that can just save you so much time. Here's also a prompt that I use in order to come up with a study plan to learn JavaScript. You can also switch that out for Python or any other language that you want. I'm not going to go into more detail about the prompt itself; you can just check it out. Link in the description. Okay, now that we have our study plan, we can get started studying and also move on to the next principle.

of ultra learning. Have you ever had to explain a process to someone like click this, click this, enter here, and then enter here, and they're like what? and then you have to explain it to them again? Well, I want to introduce you guys to a very nifty little AI tool called Guide. Guide is a GPT-powered tool that can explain the most complex tasks in seconds using AI-generated documentation. Let me actually show you. To get started on their website, just click on install free extension, and then make sure that it's pinned on Chrome. Now, here is the admin portal for Lonely Octopus, where we add courses to people's custom study plans. So, say here I want to add a custom study plan for Tina. I would click on Guide over here, click Capture. What are you capturing? It's knowledge and documentation, and it's how to add courses to a custom study plan, and then go through the process of adding a course. Say Chat B Technical Self Boot Camp Introduction to Python, maybe some Pandas and Introduction to AI Product Development. So, I got to rank these into order that

they appear on a study plan, and I click save. Then I go to guide, and the app will generate this step-by-step video guide. Select the course you want to add to your study plan. It has visuals, it has voiceover, and it has these call-to-action call-out buttons as well. So, we already do have documentation for things because we are not savages, but it's like in this text form, and I can like really long, and it takes a really long time to do. But now that we know about guide for the coming cohort, we're actually going to be using guide to update our documentation 'cause it's just so much easier, faster, and just clearer. You can easily edit the video, the visuals, the audio, the voiceover, and the motions and captions. And once you're ready and satisfied, you can just click on share or embed your guide anywhere for your team to see. The best part is that this browser extension, this app, is 100% completely free. You can check it out over here, also linked in description.

right now back to the video. I want 100% commitment and laser focus. Right, principle number two is focus. How to be laser-focused in your study sessions. The book says that there are three major problems that people have when it comes to focus. So, problem one and two are covered in a lot of other books already, so I will only briefly touch on them. But problem three, this is the only book that talks about this one, and it's what I think has helped me the most. Problem one is failing to start, AKA procrastination. Some types of combat for this would be like the 5-minute rule, in which you try to get yourself to study for 5 minutes, and you'll find that the rest of the time would be a lot easier, and Pomodoro studying for a specific period of time and then taking a break for a specific period of time. Problem two is failing to sustain focus, like after you get over your procrastination, but then you still cannot focus. This is mostly about environment control, like getting rid of distracting things like

your phone food maybe your friends go away while you're studying and problem of three is failing to recreate the right kind of focus. There is a relationship between arousal and task complexity. By arousal, I mean not that kind of arousal; it's kind of like alertness, um, how alert you are, how awake you feel, and task complexity is how difficult and complex the task is. For example, just having to memorize something will be considered low task complexity, while, say, like building an entire application would be considered high task complexity. The greater the arousal, the more narrow your focus is, and the more suitable that is for more simple tasks like memorizing something or practicing something repetitive like throwing a start or a layup, while for more complex tasks like doing math, writing an essay, you want your arousal to be lower so your focus is wider because complex tasks require you to use different parts of your brain. Okay, so how does this relate to creating the right kind of focus? Well, you can modify your arousal based on the task that you

have to do, for example, like you know today that you're going to be memorizing flashcards. You know that's a simple task, so it's better to have a higher level of arousal. You can increase your level of arousal by essentially increasing your stimulation, both external stimulation, like you can go somewhere where there's background music, there's background noise, like a coffee shop, or internal stimulation, um, by doing something like drinking coffee or working out. Isn't that really cool? Like if you understand that relationship between arousal and task complexity, you're able to modify your environment, your external and your internal environment, to create like optimal amounts of focus depending on what it is that you want to accomplish. Ballseye, the principle of directness means learning the task as close as possible to the situation where context that you're going to use it in. When I was preparing for my data science interviews, I realized that a lot of the interviews were going to be in SQL. The problem was I didn't know any SQL, so I had to learn

everything real quick. Uh, before taking the interview, which was in 11 days, this is when the principle of directness saved my ass. Because in order to prepare for it, there are a lot of things I could do. I could try reading through a SQL textbook. I could take like an online course about SQL. I could watch YouTube videos about SQL. But the most direct way of practicing for a SQL data science interview was by practicing SQL interview questions, and specifically SQL interview questions from that company, which was Meta. So I went on Glassdoor and filtered Meta data science interviews, and I literally picked out every single interview question that people have done. Then I reverse-engineered these questions to figure out exactly what it is that I need to learn. And I did that for those 11 days, practicing those questions in that interview style, and it paid off. Because when I went into that interview, two of the questions were almost exactly the same as questions I had already done. Some other examples of directness: say you want to be conversationally fluent in French. The

best way of learning is literally to go find some French people and try talking to them. The oppose just be YouTuber. If you want to learn to code, the best way is to find a project and code the entire project yourself. But you may be thinking at this point, like, isn't that a chicken or egg problem? Like, how are you supposed to converse with someone in French if you don't know any French? How are you supposed to code a project if you don't know how to code? Well, that is where the next principle comes in. You are the weakest link. The principle of drill. The principle of drilling is about attacking your weakest point. So, what you want to do is first apply the principle of directness and go do the task that you want to accomplish. And while you're doing it, especially in the very beginning, you realize that you don't know, like, pretty much everything, right? For example, if you were me and in the unfortunate situation where you had an interview in 11 days and you just saw your first metad data science interview.

question in SQL. I don't even know what that means, so I started making notes like, Okay, I got to figure out what's the actual structure of a SQL query. What does the 'FROM' mean? What does 'SELECT' mean? Like, this conditional part, oh, you're able to use something called 'WHERE,' which is you're able to filter certain parts of the table. So, yeah, I kept on doing that, writing out what I didn't understand, going and learning that thing, and then coming back and doing more of the questions. And every time I ran into something that I didn't understand, I would go learn that part specifically. Window functions, for example. I ran into window functions in one of the questions, and I didn't really understand how it worked. So then I just really focused on window functions and did a bunch of questions related to the window functions until I figured out how to use it. A very common drooling tactic for musicians or for athletes is something called time-sharing. I think my handwriting is so bad. You can tell I can't read. I will put this on. I think

it's time time slicing time slicing oh my God yes so time slicing. This is when you take a task and you isolate and practice the most difficult part of it. Say as part of a song that you're rehearsing, you will just keep practicing the part that is the most difficult. Or say you're a basketball player and you're focusing specifically on layups. Layouts, you get what I mean. This combination of directness and then drilling and then just over and over again until you master a craft. This is such a powerful combination of techniques. I can testify that it is a game-changer. Okay, next principle. Pop Quiz, everybody. The principle of retrieval is about testing yourself to learn. Scott says that the best way to learn something is to actually test yourself on it. This may sound very counterintuitive until you understand the phrase neurons that wire together, fire together. You see, anything that you're learning from a neuroscience perspective is simply encoding something into the neurons of your brain, your brain cells specifically. It's about certain networks of neurons that depict

the information that you're trying to learn now. The better you get at something, the stronger the network of connections of your neurons is going to be for that specific task. Like the better you are at coding, the stronger your coding networks of brain cells are going to be. So what does that have to do with testing? Well, testing yourself, forcing your brain to activate and reactivate that network of neuronal connections associated with the task, is the single best way in order to strengthen that network. When you're learning to code, you could just like watch some tutorials of like other people code, or just like I don't know, like do some games about coding, but that wouldn't really like activate that neuronal network that much. So you don't really like deepen your understanding of it that much. But if you do something like force yourself to write an entire application, you're testing your own understanding by forcing that neuronal network of coding stuff by reactivating and keep activating. So by the time you're done building that application, that skill of coding is so deeply

encoded into your brain cells, like literally into your brain cells. Some other retrieval tactics that the book talks about include flashcards, which are really good for vocabulary, free recall, which is just saying the thing that you learned but in your own words, and self-generated challenges or the question book method, where you rephrase the thing that you're trying to learn as a question. For example, when I was studying for the driving test, I would ask myself, What does it mean when there's a flashing green light? By the way, if you know the answer to that, put it in the comments. For some reason, a lot of people don't know the answer to that. Very concerning. I thought maybe you can give me your feedback, feedback, feedback, feedback, feedback, feedback. I'm sure that you already know that everybody's told you you should always get feedback. But did you know that there's actually three types of feedback? The first type of feedback is called outcome feedback. For example, there's this very famous comedian called Chris Rock. He would go to like these smaller shows, like usually

he has like big sold-out shows, right? But he would go to like these smaller kind of like stand-up places and tell some of his new jokes in order to gauge the audience's reaction, like do they laugh or do they not laugh. That's how he knows that it's a good joke or it's not a good joke. This is outcome feedback, where you only know if the joke is funny or not funny. When I first released a video, I can tell that it's a good video or a bad video based upon how many people want to click on the video and watch it. If you get this type of feedback, it's already very, very helpful, but you can upgrade this feedback to informational feedback. This would be like Chris Rock asking his audience, Why do you think that was funny? or Why do you think it wasn't funny? or me looking at the comments to see what it is that people liked or didn't like about a video. It tells you what it is that you're doing wrong or you're doing

right, but it doesn't tell you why or how to fix the things to improve the third type of feedback and the best type of feedback. It's called corrective feedback. This not only tells you whether the thing you did was good or bad, why it's good or bad, but also how to fix the bad parts. How do you improve to the next level? This type of feedback usually involves having to hire a mentor or coach where they can exactly look at whatever it is you're doing and point out, like your golf swing, for example. Maybe you should, I don't know, like adjust your grip exactly like this in order to improve it. Or if it's your code, doing a code review and pointing out, like, here is exactly what you can do in order to make your code better. This can be really, really expensive, but just by the fact that how many people are willing to hire a personal coach just shows how useful this kind of feedback could be. By the way, this is also an area where AI is amazing as a

learning tool because it's able to give you that individualized informational feedback when you feed it something like your code where you're writing and you ask it to like give you feedback as to how it is that you can correct it and make it better. So, I recommend playing on with Chach BT to get this kind of feedback at a fraction of the cost to ensure maximum retention. Principle number seven is retention. Imagine a bucket with some holes in it; that is your brain. Now imagine that you're pouring water or knowledge or things that you're learning into the bucket. So, you're learning things, but at the same time, it is flowing out. You're also forgetting things at the same time. Unfortunately, our brains are leaky buckets, and they can be seen by what is called the forgetting curve. After you learn something, you immediately start forgetting about it. The most amount of forgetting happens right after you learn it, and over time, the rate of forgetting decreases, but you're still like slowly forgetting stuff in the book, Scott.

explains that Ultra Learners not only focus on getting information into the brain; they also address the fact that they need to prevent information from leaking out of the brain as much as possible. The book talks about a few different tactics that you can use in order to combat the forgetting curve. The first one is spacing out your learning. So, this is unfortunate news, maybe for some of us like me who have trouble with procrastinating and not cramming things, but apparently, if you're spacing out your study sessions—like instead of cramming 10 hours one day before the exam, but you know, study 1 hour 10 days for the exam—that is a lot better in remembering things. There's also something called a spaced repetition system where if you're especially trying to remember something and have some flashcards, there's a way of spacing the learning so that it's the most optimal for your brain. There is a free app called Anki that does this, so definitely check that out if you need to memorize a lot of things. Another one is

called proceduralization. You might have heard the phrase that you never forget how to bike or like you never forget how to swim, and that's because once you automate how to do something like biking or swimming, it gets encoded into your brain differently, so it's very difficult for you to forget. In the beginning, when you're trying to learn how to ride a bike, it might be quite difficult because you're trying to remember like, Oh, I have to hold the handle like this, I have to remember to balance, I have to remember to pedal, and all these things. But after it becomes automated in your head, it gets like encoded into your brain, and even like 10 years later, you get on a bike, you still know how to bike. So if you manage to learn something enough so that it becomes automatic and a procedure in your brain, then the likelihood of you forgetting it is much lower. Okay, at this point, we're nearing mastery, and the last two principles are for people who really want to become experts. My spidey sense is tingling. The

The principle of intuition is about really digging deep into a concept, into a subject, until you gain like almost an uncanny ability of solving problems or just like understanding things. A really good example of this is Richard Feynman. He's said to have almost like a magical sense of understanding when it comes to like numbers and it comes to physics, and that's because he has a very solid grasp of fundamentals. If you just tell him f is equal to ma, force is equal to mass times acceleration, just memorize that, he'll be like no, why? Why is force equals m * a? Why is there a linear relationship? Why is force measured the way it is? Why is mass the way it is? Why is acceleration the way it is? Like why, why, why, why, why, why? And he'll just keep digging and digging until he like truly understands all of the components. That is the mindset of someone who becomes an expert. I'll put on screen out the Feynman technique. This is what he asks himself every time that he's learning something.

so he's able to dig really, really deep and truly grasp that information. Hey, look, experiment 78, the principle of experimentation. This is a principle that is crucial to becoming a true master in your field. You see, whenever you learn something, there's always the most amount of resources for beginners. Like, if you just type introduction to Python onto Google, there's like literally so many courses that are here. And if you go on YouTube, there's like countless videos covering introduction to Python. However, once you start getting to the intermediate and to the mastery level, the amount of resources becomes fewer and fewer. And this is normal because there's just like less people who are at like the mastery level, right? And those who are at the mastery level, they're probably focused on like mastering their craft as opposed to like making courses, teaching other people about the craft. So, that is where experimentation comes in. Scott gives the example of Enko, the amazing painter that painted Star KN and a lot of other masterpieces. He is someone that embodies experimentation throughout his career. He's experimented

with so many different things, different mediums, different usages of color, different styles of paints, how thick his paint is, different techniques, the subjects of his painting, his internal states. He pushes the boundaries and tries things that are just outside of what anybody even imagined, and that is how he became a master of his craft. Unlike artists like Leonardo da Vinci, who was just kind of known as a genius, Van Gogh, a painter like Van Gogh, became a master. He became a genius because of his experimentation and pushing the boundaries. Maybe in some ways, having a lot of talent and being known as a genius may actually prevent you from pushing the boundaries of what is known, but that is the topic of another video. All right, so thank you so much for watching until the end of this video. I try my best to cover this book comprehensively and in a more action-based way. Let me know in the comments if you like this kind of video, 'cause I've never actually done a book review video before. Um, you like it, we might make it.

into a series. All right, thank you all so much for watching, and I will see you guys in the next video or live stream.

How To Learn Anything

Hi! My name is Tina and I'm a ex-Meta data scientist. Now I create content and other internet things! 

This channel is about coding, tech, career, and self-study. I love learning new things and my goal is to always minimize effort and maximize outcome! 

I strive to do things in the most efficient way possible and with the highest reward. I basically refuse to do extra work to achieve my goals c: 


