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Anna Koop
이 강좌는 머신 러닝에 관심이 있으며 데이터 분석 및 자동화에 머신 러닝을 적용하길 원하는 전문가를 위한 강좌입니다. 이 강좌는 금융, 의약품, 공학, 비즈니스 등 분야와 상관없이 머신 러닝 프로젝트에서 문제를 정의하고 데이터를 준비하는 방법을 소개합니다.이 강좌를 수료하고 나면 머신 러닝 문제를 두 가지 접근 방법으로 정의할 수 있을 것입니다. 또한 이용 가능한 데이터 자료를 조사하고 잠재적 ML 적용을 알아보는 방법을 알게 될 것입니다. 비즈니스 니즈를 파악하고 실용 머신 러닝에 적용하는 방법을 알게 될 것입니다. 그리고 머신 러닝을 효과적으로 적용하기 위해 데이터를 준비할 수 있을 것입니다.이 강좌는 Coursera와 Alberta Machine Intelligence Institute에서 준비한 첫 번째 실용 머신 러닝 전문 과정입니다.
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What's inside
Syllabus
실용 머신 러닝 소개
이번 주는 머신 러닝(ML)이 무엇인지 배우고, 다양한 문제 상황을 비교해보고, ML에 대해 흔히 무엇을 잘못 알고 있는지 알아볼 것입니다. 이 내용을 배우고 나면 머신 러닝 비즈니스 솔루션에 필요한 요소를 파악할 수 있습니다.
현실 세계에서의 머신 러닝
Read more
Syllabus
실용 머신 러닝 소개
이번 주는 머신 러닝(ML)이 무엇인지 배우고, 다양한 문제 상황을 비교해보고, ML에 대해 흔히 무엇을 잘못 알고 있는지 알아볼 것입니다. 이 내용을 배우고 나면 머신 러닝 비즈니스 솔루션에 필요한 요소를 파악할 수 있습니다.
현실 세계에서의 머신 러닝
Read more
이번 주에는 비즈니스 니즈를 머신 러닝 문제로 변환하는 방법을 배울 것입니다. 잘 정의된 QuAM 질문을 만드는 방법을 알 수 있도록 몇 가지 적용 사례를 알아볼 겁니다. ML에 성공하려면 질문의 범위를 좁히고 학습에 필요한 데이터를 확보했는지 확인하는 것이 중요합니다!
학습 데이터
이번 주는 전부 데이터에 관한 내용입니다. 데이터 수집과 학습 데이터의 다양한 출처에 대해 알아볼 것입니다. 데이터가 얼마나 필요한지, 윤리적 문제 등 어떤 실수를 하게 될 수 있는지 알아볼 것입니다.
머신 러닝 프로젝트
이번 주는 머신 러닝 프로세스 라이프사이클(MLPL)에 대해 배울 것입니다. MLPL의 정의와 구성 요소를 이해하고 사례 연구를 통해 MLPL의 적용 사례를 분석합니다.
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강좌는 문제를 정의하고 데이터 준비 방법을 다룹니다
비즈니스 니즈를 실용적인 머신 러닝에 적용하는 방법을 설명합니다
학습 데이터의 다양한 출처와 데이터 수집 방법을 소개합니다
실용적인 머신 러닝 프로젝트 라이프사이클을 이해하는 데 도움을 줍니다
비즈니스 니즈를 머신 러닝 문제로 변환하는 방법을 설명합니다
Save this course
Save 실용 머신 러닝 소개 to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in 실용 머신 러닝 소개 with these
activities:
Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron
Show steps
This book provides a comprehensive overview of ML algorithms and their implementation using popular libraries.
View
핸즈온 머신러닝(3판)
on Amazon
Show steps
-
Read the book thoroughly and take notes.
-
Work through the code examples and experiment with different ML techniques.
Refresh your understanding of Python
Show steps
Practicing with Python before beginning the course will improve your confidence in your coding abilities.
Browse courses on
Python
Show steps
-
Review basic Python syntax and data structures.
-
Work through a few coding problems to practice your skills.
Build a machine learning model
Show steps
Applying your knowledge to create a practical model will enhance your understanding of the ML process.
Browse courses on
Machine Learning
Show steps
-
Choose a dataset and define your problem statement.
-
Explore the data and preprocess it.
-
Train and evaluate your model.
-
Deploy your model and monitor its performance.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Participate in a study group or discussion forum
Show steps
Engaging in discussions and peer support will enhance your understanding and provide diverse perspectives.
Browse courses on
Collaboration
Show steps
-
Join a study group or online discussion forum related to ML.
-
Actively participate in discussions and share your knowledge.
-
Seek feedback and learn from others' experiences.
Practice solving machine learning problems
Show steps
Regular practice in solving ML problems will strengthen your analytical and problem-solving skills.
Browse courses on
Problem Solving
Show steps
-
Find online resources or textbooks with ML problems.
-
Attempt to solve the problems on your own.
-
Review your solutions and identify areas for improvement.
Create a blog post or presentation on a specific ML application
Show steps
Creating content on an ML application will deepen your understanding and enhance your communication skills.
Browse courses on
Machine Learning Applications
Show steps
-
Choose an ML application that interests you.
-
Research the application and its technical aspects.
-
Write a blog post or create a presentation that explains the application and its benefits.
Follow guided tutorials on advanced ML techniques
Show steps
Exploring advanced ML techniques through guided tutorials will broaden your knowledge and potential applications.
Browse courses on
Machine Learning
Show steps
-
Identify specific advanced ML techniques you want to learn.
-
Find reputable online tutorials or courses that cover these techniques.
-
Follow the tutorials step-by-step and implement the concepts.
Attend industry events or conferences on ML
Show steps
Connecting with professionals in the field will expose you to real-world applications and career opportunities.
Browse courses on
Networking
Show steps
-
Research upcoming ML events or conferences.
-
Register and attend the events.
-
Network with attendees and learn about their work.
Volunteer on ML-related projects
Show steps
Contributing to ML projects through volunteering will provide practical experience and build your portfolio.
Browse courses on
Volunteering
Show steps
-
Identify organizations or projects that work on ML applications.
-
Contact the organizations and inquire about volunteer opportunities.
-
Contribute your skills and support the project's goals.
Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron
Show steps
This book provides a comprehensive overview of ML algorithms and their implementation using popular libraries.
View
핸즈온 머신러닝(3판)
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly and take notes.
- Work through the code examples and experiment with different ML techniques.
Refresh your understanding of Python
Show steps
Practicing with Python before beginning the course will improve your confidence in your coding abilities.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review basic Python syntax and data structures.
- Work through a few coding problems to practice your skills.
Build a machine learning model
Show steps
Applying your knowledge to create a practical model will enhance your understanding of the ML process.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a dataset and define your problem statement.
- Explore the data and preprocess it.
- Train and evaluate your model.
- Deploy your model and monitor its performance.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Participate in a study group or discussion forum
Show steps
Engaging in discussions and peer support will enhance your understanding and provide diverse perspectives.
Browse courses on
Collaboration
Show steps
Show steps
Show steps
- Join a study group or online discussion forum related to ML.
- Actively participate in discussions and share your knowledge.
- Seek feedback and learn from others' experiences.
Practice solving machine learning problems
Show steps
Regular practice in solving ML problems will strengthen your analytical and problem-solving skills.
Browse courses on
Problem Solving
Show steps
Show steps
Show steps
- Find online resources or textbooks with ML problems.
- Attempt to solve the problems on your own.
- Review your solutions and identify areas for improvement.
Create a blog post or presentation on a specific ML application
Show steps
Creating content on an ML application will deepen your understanding and enhance your communication skills.
Browse courses on
Machine Learning Applications
Show steps
Show steps
Show steps
- Choose an ML application that interests you.
- Research the application and its technical aspects.
- Write a blog post or create a presentation that explains the application and its benefits.
Follow guided tutorials on advanced ML techniques
Show steps
Exploring advanced ML techniques through guided tutorials will broaden your knowledge and potential applications.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify specific advanced ML techniques you want to learn.
- Find reputable online tutorials or courses that cover these techniques.
- Follow the tutorials step-by-step and implement the concepts.
Attend industry events or conferences on ML
Show steps
Connecting with professionals in the field will expose you to real-world applications and career opportunities.
Browse courses on
Networking
Show steps
Show steps
Show steps
- Research upcoming ML events or conferences.
- Register and attend the events.
- Network with attendees and learn about their work.
Volunteer on ML-related projects
Show steps
Contributing to ML projects through volunteering will provide practical experience and build your portfolio.
Browse courses on
Volunteering
Show steps
Show steps
Show steps
- Identify organizations or projects that work on ML applications.
- Contact the organizations and inquire about volunteer opportunities.
- Contribute your skills and support the project's goals.
Career center
Learners who complete 실용 머신 러닝 소개 will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models and systems. This course provides a solid foundation for this role by covering key concepts in machine learning, including data preprocessing, model selection, and performance evaluation. Learners will gain hands-on experience in building and evaluating machine learning models that can be applied to real-world problems.
Data Scientist
Data Scientist
Data Scientists use their knowledge of math, statistics, and computer science to extract insights from data. This course can be a valuable asset for aspiring Data Scientists as it provides a comprehensive overview of the machine learning lifecycle, from problem definition to model deployment. Learners will gain the skills necessary to effectively apply machine learning techniques to a wide range of data science problems.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course can be helpful for Quantitative Analysts who want to learn more about machine learning techniques. Learners will gain an understanding of how to apply machine learning algorithms to financial data, and how to interpret the results.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment decisions. This course can be helpful for Financial Analysts who want to learn more about machine learning techniques. Learners will gain an understanding of how to apply machine learning algorithms to financial data, and how to interpret the results.
Data Analyst
Data Analyst
Data Analysts collect, analyze, interpret, and present data. This course aligns well with the goals of this role as it provides foundational knowledge and skills in data preparation, model evaluation, and communication of results. By taking this course, learners will gain a strong foundation in the practical application of machine learning for data analysis.
Risk Manager
Risk Manager
Risk Managers identify and assess risks to an organization. This course can be beneficial for Risk Managers who want to learn more about machine learning techniques. Learners will gain an understanding of how to apply machine learning algorithms to risk data, and how to interpret the results.
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. By taking this course, Business Analysts can gain a better understanding of how to use machine learning to analyze data and make more informed decisions. The course will provide learners with the skills to define business problems in a way that can be solved using machine learning, and how to evaluate the results of machine learning models.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course can be beneficial for Product Managers who want to incorporate machine learning into their products. Learners will gain an understanding of the potential applications of machine learning, and how to evaluate the feasibility and impact of machine learning projects.
Consultant
Consultant
Consultants provide advice and guidance to organizations on a variety of topics. This course can be beneficial for Consultants who want to learn more about machine learning and its potential applications. Learners will gain an understanding of how to identify business problems that can be solved using machine learning, and how to evaluate the results of machine learning projects.
Researcher
Researcher
Researchers use scientific methods to investigate and solve problems. This course can be useful for Researchers who want to use machine learning in their research. Learners will gain an understanding of the fundamental principles of machine learning, and how to apply them to a variety of research problems.
Operations Manager
Operations Manager
Operations Managers are responsible for the day-to-day operations of a business. This course can be helpful for Operations Managers who want to use machine learning to improve the efficiency and effectiveness of their operations. Learners will gain an understanding of how to use machine learning to optimize processes, reduce costs, and improve customer satisfaction.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course can be beneficial for Software Engineers who want to incorporate machine learning into their projects. Learners will gain an understanding of the fundamental principles of machine learning and how to apply them in a software engineering context.
Entrepreneur
Entrepreneur
Entrepreneurs start and run their own businesses. This course can be helpful for Entrepreneurs who want to use machine learning to improve their business operations. Learners will gain an understanding of how to identify business problems that can be solved using machine learning, and how to evaluate the results of machine learning projects.
Sales Manager
Sales Manager
Sales Managers are responsible for managing sales teams and achieving sales targets. This course can be helpful for Sales Managers who want to use machine learning to improve the performance of their teams. Learners will gain an understanding of how to use machine learning to identify sales opportunities, target sales leads, and close deals.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. By taking this course, Marketing Managers can learn how to use machine learning to improve the effectiveness of their campaigns. Learners will gain an understanding of how to use machine learning to segment their audience, target their marketing messages, and measure the results of their campaigns.
For more career information including salaries, visit:
OpenCourser.com/course/02bjct/silyong
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
실용 머신 러닝 소개.
이 책은 머신 러닝에 사용되는 통계적 방법의 포괄적인 참고서입니다. 이 주제에 대한 심도 있는 지식을 제공합니다.
이 책은 R 프로그래밍 언어를 사용한 머신 러닝을 다룹니다. R 사용자에게 유용합니다.
이 책은 머신 러닝 라이브러리와 도구를 사용한 실습적인 접근 방식을 제공합니다. 코드 예제와 연습 문제를 통해 학습을 강화합니다.
이 책은 실시간 머신 러닝 프로젝트를 위한 단계별 가이드를 제공합니다. 데이터 처리, 모델 선택 및 평가에 대한 실용적인 지침을 제공합니다.
파이썬 프로그래밍 언어를 사용하여 머신 러닝 모델을 개발하고 구축하는 데 도움이 되는 책입니다.
머신 러닝 모델을 실제 시스템에 구축하고 배포하는 방법을 설명하는 책으로, 엔지니어링 관점에 중점을 둡니다.
머신 러닝의 기본 개념과 이론을 다루는 책으로, 알고리즘 선택, 모델 평가, 데이터 전처리 방법을 설명합니다.
Save
딥 러닝에 관한 포괄적인 참고서로, 이 분야의 기본 개념과 최신 연구를 다룹니다.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/02bjct/silyong
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Level
Intermediate
Via
Coursera
Institution
Alberta Machine Intelligence Institute
Instructor
Anna Koop
Language
한국어
Transcripts
English
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Português
Deutsch
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tiếng Việt
Good to know
강좌는 문제를 정의하고 데이터 준비 방법을 다룹니다
비즈니스 니즈를 실용적인 머신 러닝에 적용하는 방법을 설명합니다
학습 데이터의 다양한 출처와 데이터 수집 방법을 소개합니다
실용적인 머신 러닝 프로젝트 라이프사이클을 이해하는 데 도움을 줍니다
비즈니스 니즈를 머신 러닝 문제로 변환하는 방법을 설명합니다
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