기본 요건 및 사전 작업
컬렉션을 사용해 정리하기
내 환경설정을 기준으로 콘텐츠를 저장하고 분류하세요.
머신러닝 단기집중과정이 나에게 적합한가요?
머신러닝에 대한 지식이 거의 없거나 전혀 없습니다.
모든 자료를 순서대로 살펴보는 것이 좋습니다.
머신러닝에 대한 배경지식이 있지만 더 최신적이고 완전한 이해를 얻고 싶습니다.
머신러닝 단기집중과정은 복습에 도움이 됩니다. 모든 모듈을 순서대로 살펴보거나 관심 있는 모듈만 선택하세요.
머신러닝 개념을 적용하여 데이터를 사용하고 모델을 빌드한 실무 경험이 있습니다.
머신러닝 기초 과정은 기본적인 머신러닝 개념을 복습하는 데 유용하지만 다양한 도메인에서 머신러닝 문제를 해결하기 위한 도구와 기법을 다루는 고급 머신러닝 과정을 살펴보는 것도 좋습니다.
Keras와 같은 ML API를 사용하는 방법에 관한 튜토리얼을 찾고 있습니다.
머신러닝 기초 과정에는 numpy, pandas, Keras와 같은 ML 라이브러리를 사용하는 여러 프로그래밍 연습이 포함되어 있지만 주로 ML 개념을 가르치는 데 중점을 두고 있으며 ML API는 심층적으로 다루지 않습니다. 추가 Keras 리소스는
Keras 개발자 가이드를 참고하세요.
머신러닝 단기집중과정을 시작하기 전에 사전 작업 및 필수사항 섹션을 읽고 모든 모듈을 완료할 준비가 되었는지 확인하세요.
사전 작업
머신러닝 단기집중과정을 시작하기 전에 다음을 실행하세요.
-
머신러닝을 처음 접하는 경우 머신러닝 소개를 수강하세요.
이 짧은 셀프 스터디 과정에서는 기본적인 머신러닝 개념을 소개합니다.
-
NumPy를 처음 사용하는 경우 이 과정에 필요한 모든 NumPy 정보를 제공하는 NumPy 초고속 튜토리얼 Colab 연습을 진행하세요.
-
Pandas를 처음 사용하는 경우 이 교육 과정에 필요한 Pandas 정보가 모두 제공된 Pandas UltraQuick 튜토리얼 Colab 연습을 진행하세요.
기본 요건
머신러닝 단기집중과정은 머신러닝에 관한 사전 지식을 전제로 하지 않으며 사전 지식도 필요하지 않습니다. 하지만 제시된 개념을 이해하고 연습을 완료하려면 다음 기본 요건을 충족하는 것이 좋습니다.
변수, 선형 방정식, 함수의 그래프, 히스토그램, 통계적 평균을 알고 있어야 합니다.
프로그래밍에 능숙해야 합니다. 프로그래밍 연습이 Python으로 진행되므로 Python으로 프로그래밍한 경험이 있으면 좋습니다. 하지만 Python 경험이 없는 숙련된 프로그래머도 일반적으로 프로그래밍 연습을 완료할 수 있습니다.
다음 섹션에서는 유용한 추가 배경 자료 링크를 제공합니다.
대수학
선형대수학
삼각법
통계
미적분학 (선택사항, 고급 주제용)
Python 프로그래밍
다음 Python 기본사항은 Python 튜토리얼에서 다룹니다.
위치 및 키워드 매개변수를 사용한 함수 정의 및 호출
사전,
목록,
세트 (만들기, 액세스, 반복)
for 루프, 여러 iterator 변수가 있는 for 루프 (예: for a, b in [(1,2), (3,4)])
if/else 조건부 블록 및 조건식
문자열 형식 지정
(예: '%.2f' % 3.14)
변수, 할당, 기본 데이터 유형(int, float, bool, str)
일부 프로그래밍 연습에서는 다음과 같은 고급 Python 개념을 사용합니다.
Bash 터미널 및 Cloud 콘솔
로컬 머신이나 클라우드 콘솔에서 프로그래밍 연습을 실행하려면 명령줄에서 작업하는 데 익숙해야 합니다.
달리 명시되지 않는 한 이 페이지의 콘텐츠에는 Creative Commons Attribution 4.0 라이선스에 따라 라이선스가 부여되며, 코드 샘플에는 Apache 2.0 라이선스에 따라 라이선스가 부여됩니다. 자세한 내용은 Google Developers 사이트 정책을 참조하세요. 자바는 Oracle 및/또는 Oracle 계열사의 등록 상표입니다.
최종 업데이트: 2025-01-28(UTC)
[null,null,["최종 업데이트: 2025-01-28(UTC)"],[[["\u003cp\u003eGoogle's Machine Learning Crash Course offers a flexible learning experience for users with varying levels of machine learning expertise, including beginners, those seeking a refresher, and experienced practitioners.\u003c/p\u003e\n"],["\u003cp\u003eThe course requires prework, such as familiarity with Python, NumPy, and pandas, and has prerequisites in algebra, linear algebra, statistics, and optionally, calculus, to fully grasp the concepts.\u003c/p\u003e\n"],["\u003cp\u003eWhile focusing on core ML concepts, the course incorporates practical programming exercises using libraries like NumPy, pandas, and Keras but doesn't delve deep into specific ML APIs.\u003c/p\u003e\n"],["\u003cp\u003eLearners are encouraged to complete the prework, including an introductory machine learning course and tutorials for NumPy and pandas, to ensure preparedness.\u003c/p\u003e\n"],["\u003cp\u003eThe course leverages the Colaboratory platform, offering browser-based programming exercises that require no setup and are best experienced on Chrome or Firefox desktops.\u003c/p\u003e\n"]]],[],null,["# Prerequisites and prework\n\n\u003cbr /\u003e\n\n### Is Machine Learning Crash Course right for you?\n\nI have little or no machine learning background. \nWe recommend going through all the material in order. \n[START LEARNING](/machine-learning/crash-course/linear-regression) \nI have some background in machine learning, but I'd like a more current and complete understanding. \nMachine Learning Crash Course will be a great refresher. Go through all the modules in order, or select only those modules that interest you. \n[START LEARNING](/machine-learning/crash-course/linear-regression) \nI have practical experience applying machine learning concepts to work with data and build models. \nWhile Machine Learning Crash Course may be useful to you as a refresher of fundamental machine learning concepts, you may also want to explore some of our advanced machine learning courses, which cover tools and techniques for solving machine learning problems in a variety of domains. \n[START LEARNING](/machine-learning/advanced-courses) \nI am looking for tutorials on how to use ML APIs like Keras. \nWhile Machine Learning Crash Course includes several programming exercises that use ML libraries such as numpy, pandas, and Keras, it is primarily focused on teaching ML concepts, and does not teach ML APIs in depth. For additional Keras resources, see the [Keras Developer guides](https://keras.io/guides/).\n\nPlease read through the following [Prework](#prework) and\n[Prerequisites](#prerequisites) sections before beginning Machine Learning\nCrash Course, to ensure you are prepared to complete all the modules.\n\nPrework\n-------\n\nBefore beginning Machine Learning Crash Course, do the following:\n\n1. If you're new to machine learning, take [Introduction to Machine\n Learning](/machine-learning/intro-to-ml). This short self-study course introduces fundamental machine learning concepts.\n2. If you are new to [NumPy](https://numpy.org), do the [NumPy\n Ultraquick Tutorial](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/numpy_ultraquick_tutorial.ipynb?utm_source=mlcc&utm_campaign=colab-external&utm_medium=referral&utm_content=mlcc-prework&hl=en) Colab exercise, which provides all the NumPy information you need for this course.\n3. If you are new to [pandas](https://pandas.pydata.org/), do the [pandas\n UltraQuick Tutorial](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/pandas_dataframe_ultraquick_tutorial.ipynb?utm_source=mlcc&utm_campaign=colab-external&utm_medium=referral&utm_content=mlcc-prework&hl=en) Colab exercise, which provides all the pandas information you need for this course.\n\nProgramming exercises run directly in your browser (no setup\nrequired!) using the [Colaboratory](https://colab.research.google.com)\nplatform. Colaboratory is supported on most major browsers, and is most\nthoroughly tested on desktop versions of Chrome and Firefox.\n\nPrerequisites\n-------------\n\nMachine Learning Crash Course does not presume or require any prior knowledge in\nmachine learning. However, to understand the concepts presented\nand complete the exercises, we recommend that students meet the\nfollowing prerequisites:\n\n- You must be comfortable with variables, linear equations,\n graphs of functions, histograms, and statistical means.\n\n- You should be a good programmer. Ideally, you should have some\n experience programming in [Python](https://www.python.org/) because\n the programming exercises are in Python. However, experienced\n programmers without Python experience can usually complete the programming\n exercises anyway.\n\nThe following sections provide links to additional background material\nthat is helpful.\n\n### Algebra\n\n- [variables](https://www.khanacademy.org/math/algebra/x2f8bb11595b61c86:foundation-algebra/x2f8bb11595b61c86:intro-variables/v/what-is-a-variable), [coefficients](https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-equivalent-exp/cc-6th-parts-of-expressions/v/expression-terms-factors-and-coefficients), and [functions](https://www.khanacademy.org/math/algebra-home/alg-functions)\n- [linear equations](https://wikipedia.org/wiki/Linear_equation) such as \\\\(y = b + w_1x_1 + w_2x_2\\\\)\n- [logarithms](https://wikipedia.org/wiki/Logarithm), and logarithmic equations such as \\\\(y = ln(1+ e\\^z)\\\\)\n- [sigmoid function](https://wikipedia.org/wiki/Sigmoid_function)\n\n### Linear algebra\n\n- [tensor and tensor rank](https://www.tensorflow.org/guide/tensor)\n- [matrix multiplication](https://wikipedia.org/wiki/Matrix_multiplication)\n\n### Trigonometry\n\n- [tanh](https://reference.wolfram.com/language/ref/Tanh.html) (discussed as an [activation function](https://developers.google.com/machine-learning/glossary#activation_function); no prior knowledge needed)\n\n### Statistics\n\n- [mean, median, outliers](https://www.khanacademy.org/math/probability/data-distributions-a1/summarizing-center-distributions/v/mean-median-and-mode), and [standard deviation](https://wikipedia.org/wiki/Standard_deviation)\n- ability to read a [histogram](https://wikipedia.org/wiki/Histogram)\n\n### Calculus (*optional, for advanced topics*)\n\n- concept of a [derivative](https://wikipedia.org/wiki/Derivative) (you won't have to actually calculate derivatives)\n- [gradient](https://www.khanacademy.org/math/multivariable-calculus/multivariable-derivatives/gradient-and-directional-derivatives/v/gradient) or slope\n- [partial derivatives](https://wikipedia.org/wiki/Partial_derivative) (which are closely related to gradients)\n- [chain rule](https://wikipedia.org/wiki/Chain_rule) (for a full understanding of the [backpropagation algorithm](https://developers.google.com/machine-learning/crash-course/backprop-scroll/) for training neural networks)\n\n### Python Programming\n\nThe following Python basics are covered in [The Python Tutorial](https://docs.python.org/3/tutorial/):\n\n- [defining and calling functions](https://docs.python.org/3/tutorial/controlflow.html#defining-functions),\n using positional and [keyword](https://docs.python.org/3/tutorial/controlflow.html#keyword-arguments) parameters\n\n- [dictionaries](https://docs.python.org/3/tutorial/datastructures.html#dictionaries),\n [lists](https://docs.python.org/3/tutorial/introduction.html#lists),\n [sets](https://docs.python.org/3/tutorial/datastructures.html#sets) (creating, accessing, and iterating)\n\n- [`for` loops](https://docs.python.org/3/tutorial/controlflow.html#for-statements),\n `for` loops with multiple iterator variables (e.g., `for a, b in [(1,2), (3,4)]`)\n\n- [`if/else` conditional blocks](https://docs.python.org/3/tutorial/controlflow.html#if-statements) and\n [conditional expressions](https://docs.python.org/2.5/whatsnew/pep-308.html)\n\n- [string formatting](https://docs.python.org/3/tutorial/inputoutput.html#old-string-formatting)\n (e.g., `'%.2f' % 3.14`)\n\n- variables, assignment, [basic data types](https://docs.python.org/3/tutorial/introduction.html#using-python-as-a-calculator)\n (`int`, `float`, `bool`, `str`)\n\nA few of the programming exercises use the following more advanced\nPython concept:\n\n- [list comprehensions](https://docs.python.org/3/tutorial/datastructures.html#list-comprehensions)\n\n### Bash Terminal and Cloud Console\n\nTo run the programming exercises on your local machine or in a cloud console,\nyou should be comfortable working on the command line:\n\n- [Bash Reference Manual](https://tiswww.case.edu/php/chet/bash/bashref.html)\n- [Bash Cheatsheet](https://github.com/LeCoupa/awesome-cheatsheets/blob/master/languages/bash.sh)\n- [Learn Shell](http://www.learnshell.org/)\n\n[Help Center](https://support.google.com/machinelearningeducation)"]]
