무료 스탠포드 AI 학습 리소스 및 로드맵 가이드

### AI 학습을 위한 무료 리소스: 종합적인 로드맵
* 🎓 스탠퍼드 대학교의 많은 고급 과정이 온라인으로 무료로 제공되므로 재정 상황에 관계없이 **누구나 수준 높은 교육**을 받을 수 있습니다.
* 🗺 **15페이지 분량의 체계적인 로드맵**은 스탠퍼드대학교의 AI 대학원 자격증 프로그램의 검증된 커리큘럼을 기반으로 학습자가 AI를 스스로 학습할 수 있도록 안내하기 위해 만들어졌습니다.
* 💰 스탠퍼드 대학교의 학비는 **19,000달러에서 24,000달러**에 이르지만, 이 가이드는 비용 부담 없이 동일한 교육 과정을 따라갈 수 있는 방법을 제시합니다.
* 📚 미적분학, 선형 대수학, 확률에 대한 기초 지식은 매우 중요하며, 이러한 주제를 학습하는 데 도움이 되는 자료는 **Khan Academy 및 MIT OpenCourseWare**와 같은 플랫폼에서 이용할 수 있습니다.
* 🐍 Python 프로그래밍은 AI 개발에 필수적이며, **Google의 Python 클래스와 "지루한 일 자동화하기"라는 책**을 비롯한 무료 리소스를 추천합니다.
* ⚙ 프로그래밍에 익숙해지면 학습자는 **머신러닝 또는 더 광범위한 AI 주제 중에서 선택**하여 AI의 기초를 탐구하고 게시할 수 있는 수준의 프로젝트를 진행할 수 있습니다.
* 📖 기초를 마스터한 후에는 **딥러닝, 컴퓨터 비전, 자연어 처리 등의 분야에서 선택 과목**을 선택할 수 있으며, 이해를 심화할 수 있는 다양한 무료 강좌를 이용할 수 있습니다.
* 🔗 로드맵은 **무료 리소스에 대한 링크**뿐만 아니라 피해야 할 일반적인 함정과 개인적인 팁도 제공하여 AI에 대한 포괄적인 학습 경험을 보장합니다.

배경 지식에 관계없이 누구나 스탠퍼드에서 제공하는 무료 리소스로 구성된 체계적인 로드맵을 사용하여 AI를 마스터할 수 있습니다.

If you're looking to break into AI efficiently, you're in the right place. In this video, I'm going to show you how to self-study AI with a proven roadmap, all free of charge. When I first started learning AI on my own, it was really overwhelming to go through so many resources to figure out what is relevant or appropriate. There are just too many options out there. That's why today I'm really excited to share this roadmap with you, and it's not just a random plan that I put together. It's a structured step-by-step guide based on a proven curriculum from one of the best AI programs in the world, the Stanford AI graduate certificate program. Everyone knows that Stanford is top tier, but it's also one of the most expensive options out there. Tuition alone costs around $19,000 to $24,000, and that's a huge investment that not everyone can afford. But lucky for you, I'll show you how you can follow the same exact path without spending a dime. I've put together a 15-page road

map that contains all the free resources that you're going to need to master AI. Now, if you want just the road map, you can download it from the link in the description. But if you stick around for the full video, I'll share my personal tips on how to get the most out of the guide and highlight some of the common pitfalls that people often miss. And I'm not going to waste your time with the usual why you need to learn AI spiel. I'm sure you already know the value of AI. That's why you're watching this video. So let's skip the fluff and get straight to it. Let's first talk about what the journey is going to really look like. If you're hoping for a quick fix, I do need to set some expectations here. I'm not going to be offering any magic shortcut. The reality is that most students, even at Stanford, take about one to two years studying part-time. So for the self-study option, it could take anywhere from one to three years, depending on your schedule and

your background, and here are some things that will influence how quickly you progress. One is your experience level. If you already have a strong foundation in math and programming, you can breeze through some of the early content. Otherwise, you'll need to dedicate more time on those more foundational topics, which we'll cover in a bit. Two is time commitment. The more time you put in, the faster you'll get through the material. You can think of it sort of like building muscle in the gym; consistency is important. Number three is depth of learning. If you're looking to master the material, you'll need to go beyond the basics, which could take longer. But remember that this is not a race, so focus on understanding and really learning the material so you can set yourself up for success in the future. First, you need to lay a strong foundation in math. It is a crucial step in AI, especially in machine learning. So, if you're already comfortable with basic math and programming, you can jump ahead to phase three, where we dive deeper into AI fundamentals. So, first, you're going to

need calculus and understand key concepts like derivatives, integrals, and fundamental theorems. Stanford usually covers these in Maass 19 and Maass 20, but you can find free resources on platforms like Khan Academy or MIT OpenCourseWare. Where I personally like to learn from books, so if you're like me, the book that Stanford recommends is called Calculus by New Horizons. They recommend at least the sixth edition or later, and this is a paid resource, so you can get this on Amazon for each topic in my guide. Whenever I could find free written materials or courses, I've linked it in the roadmap; otherwise, I marked it as a paid resource, so you can look out for that mark. Next is linear algebra. You're going to need to master matrix operations, vector spaces, and linear transformations. Free courses include Conmy and Sheldon Axel's Linear Algebra Done Right. Another four to six weeks is a reasonable estimate for the section again. Next is probability and statistics. Introduction to Probability and Statistics by MIT OpenCourseWare is a great free resource if you look at the

syllabus for probability. You will learn things like counting, random variables, distributions, conditional probability, joint distributions, and Central Limit Theorem. For statistics, you'll learn the inference with known and unknown priors, as well as linear regression. If you're just starting out with these topics, take it slow and practice with examples until you get comfortable with it. You can also start exploring basic machine models and AI projects while building up your math skills at the same time. Okay, once you have your math foundations, it's time to build your programming skills, which are crucial for AI development. Again, if you're already familiar with this step, you can go ahead and skip to phase three. First, the first thing you're going to need is the Linux command line, since many AI tools run on Linux-based systems. There are free resources like Linux Academy or Ubuntu's beginners guide, and I would spend about 1 to 2 weeks here. Next is object-oriented programming. Stanford recommends this as a prerequisite. The OOP principles are essential for AI, so get familiar with the basic concepts like

data types, control flow, functions, and check out free resources on Codecademy. Next is data structure and algorithms. This is another essential for optimizing your AI models. You will want to understand lists, stacks, sorting algorithms, and search algorithms. Plan for about 4 to 6 weeks here. Next is probably the most important part, which is Python. Python is the primary language for AI, so start with free resources like Google's Python class. There's also a free book called Automate the Boring Stuff for additional resources. Okay, once you're comfortable with Python, you can dive into Python libraries like NumPy, TensorFlow, PyTorch, or Pandas. Expect at least 4 to 6 weeks for Python basic courses, plus another 4 to 8 weeks for the deeper dive into the machine learning libraries. Now comes the exciting part, the AI fundamentals. Here, you're going to choose one of the two paths: machine learning or broader AI topics. While Stanford's AI program asks you to specialize in one or the other, you can always explore both if you're learning independently. And if you're not sure what is the difference

between the two, I do have another video explaining it, so you can watch it later. But for now, if you're not sure what to do, or if you want to do both, I would recommend you start with a broader AI class. This will give you an overall understanding of AI, covering topics like constraint satisfaction, game markup, decision process, graphical models, and logic. There are lots of great courses, and I recommend courses like MIT's intro to AI or Stanford's logic series on YouTube. If you want to choose machine learning, this is really the part of AI focusing on algorithms that help machines learn from data, and topics for this include things like supervised learning, unsupervised learning, and reinforcement learning. I've linked lots of free resources from Stanford CS 229 courses, and you can also check out Andrew Ng's machine learning specialization on Coursera or Fast AI's introduction to machine learning for coders. They also have the whole syllabus available with lots of written materials, slides, and notes, including the main notes by Andrew Ng, all available for free. This main

notes is a comprehensive 216-page PDF, so I highly recommend you check it out. With either the AI or the machine learning classes, you will work on projects for the later part of the course, and Stanford has a lot of guidelines on how to come up with your projects and how to work on your projects. So when you're choosing your project topic, you can focus on the application of machine learning, developing a new algorithm, or exploring the theoretical properties of algorithms. A top project, as Stanford considers it, can be of publishable quality for submission to conferences or journals. For inspiration, review recent machine learning research papers from major conferences like ICML or NeurIPS, or you can also look at Stanford past class projects for ideas, and of course, the links are all in the document. Now once you have mastered the AI fundamentals, it's time for you to pick electives at Stanford. You can choose up to three electives from deep learning, computer vision, robotics, natural language processing, reinforcement learning. In the PDF roadmap, you'll find more details on each topic along with

free resources for learning. The good news is that many of Stanford's advanced courses are available entirely for free online, either through YouTube or the Stanford website itself. It did take me some time to Google and put everything together in one document, but now you have it all. So, with the right motivation and passion, anyone can really access this knowledge and learn these skills, regardless of their background or financial circumstances. But even if you master all the topics that I just mentioned, there is still one big key missing. If you want to find the final key, you can watch this video. Otherwise, YouTube thinks you should watch this one next. I'll see you there.

AI Machine Learning Roadmap: Self Study AI!

Hey there, it's Jean, founder of Exaltitude! As a former software engineer and engineering manager at WhatsApp & Facebook, I know firsthand how tough it can be to navigate the tech industry and advance your career. That's why I created Exaltitude to share the advice I wish I had earlier in my career.

Think of Exaltitude as your personal guide to career success. We'll cover everything from crafting the perfect resume and acing job interviews to negotiating salaries and building your personal brand. But we won't just give you a bunch of dry advice - we'll spice things up with real-world examples and candid conversations. 

Checkout Exaltitude - https://www.exaltitude.io
Follow Jean on LinkedIn - https://www.linkedin.com/in/jeanklee/
Follow EXA on LinkedIn - https://www.linkedin.com/company/73799118/



Exaltitude

AI 마스터하기: 종합적인 로드맵

이 동영상은 AI를 마스터하기 위한 무료 리소스가 포함된 로드맵을 제공하며, 그 여정에는 1~3년의 독학이 필요하다는 점을 강조합니다.

주요 과목으로는 미적분, 선형 대수, 확률과 통계가 있으며, 가이드에 링크된 추천 무료 리소스를 제공합니다.

이 강연자는 시청자가 AI 학습 여정을 최대한 활용할 수 있도록 개인적인 팁을 공유하고 일반적인 함정을 강조합니다.

강의 계획서에서는 통계 및 선형 회귀와 함께 수 세기, 확률 변수, 분포, 중심 극한 정리와 같은 확률 주제를 다룹니다.

예제를 연습하고 기본 머신 모델과 AI 프로젝트를 탐구하면서 수학 능력을 키우는 것이 중요합니다.

특히 Linux 명령줄 및 객체 지향 프로그래밍과 같은 프로그래밍 기술은 AI 개발에 매우 중요하며, 경험 수준, 시간 투자, 학습의 깊이에 따라 진도가 달라집니다.

AI 및 머신러닝을 위한 학습 경로

코드 아카데미의 무료 리소스를 이용해 데이터 유형, 제어 흐름, 함수부터 시작한 다음 4~6주 동안 데이터 구조와 알고리즘으로 넘어가세요.

그런 다음, 4~6주 동안은 기본에, 4~8주 동안은 머신러닝 라이브러리에 집중하여 Google의 무료 강의와 "지루한 작업 자동화"라는 책을 통해 Python에 집중하세요.

마지막으로 AI 기초를 살펴보고, 머신러닝 또는 더 광범위한 AI 주제 중에서 선택하여 프로젝트를 진행하며, 스탠포드에서 전문 주제에 대한 선택 과목을 선택할 수 있습니다.

AI 주제에 대한 전반적인 이해를 위해 광범위한 AI 강의부터 시작하는 것이 좋습니다.

좋은 강좌로는 MIT의 AI 입문과 YouTube의 스탠포드 로직 시리즈가 있습니다.

머신 러닝의 경우 스탠포드 CS 229의 무료 리소스, Coursera의 Andrew Ng의 전문 강의, 코더를 위한 Fast AI의 소개를 무료 서면 자료 및 슬라이드와 함께 이용할 수 있습니다.

스탠퍼드의 고급 과정은 온라인에서 무료로 제공되어 누구나 쉽게 접근할 수 있는 지식을 제공합니다.

스탠퍼드의 대학원 자격증 프로그램을 기반으로 독학으로 AI를 학습할 수 있는 체계적인 단계별 로드맵을 제공합니다.

이 플랜은 개인이 높은 학비 부담 없이 AI에 입문할 수 있도록 동일한 커리큘럼을 무료로 제공하는 것을 목표로 합니다.