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실용 머신 러닝 소개

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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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

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what should give you pause

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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

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Reviews summary

실용적 머신러닝 문제 정의 및 데이터 준비

학생들은 이 강좌가 머신러닝의 실용적 적용에 초점을 맞춰 비즈니스 문제 정의와 데이터 준비에 대한 명확한 이해를 제공한다고 말합니다. 특히 학습 데이터의 중요성과 윤리적 측면을 다루는 부분이 유익하며, 체계적인 ML 프로젝트 라이프사이클(MLPL)을 제시하여 전체 과정을 조망하는 데 큰 도움을 준다고 평가합니다. 그러나 일부 학습자들은 코딩이나 고급 알고리즘 등 기술적인 깊이가 부족하여 더 심도 있는 기술 학습을 원하는 이들에게는 기대와 다를 수 있다고 언급했습니다. 전반적으로 머신러닝을 비즈니스에 활용하려는 전문가나 초급 학습자에게 매우 적합한 과정입니다.

ML 프로젝트의 전체적인 라이프사이클을 체계적으로 제시합니다.

"MLPL 개념을 통해 복잡한 머신러닝 프로젝트를 단계별로 이해할 수 있었습니다."

"프로젝트 정의부터 실행까지의 흐름을 잡아주는 로드맵이 명확했습니다."

"사례 연구를 통해 MLPL의 실제 적용 방법을 배울 수 있었습니다."

데이터 수집, 준비 및 관련 윤리적 고려 사항을 다룹니다.

"데이터 수집과 정제에 대한 심층적인 논의가 실제 프로젝트에 큰 도움이 됩니다."

"머신러닝에서 데이터의 중요성과 윤리적 측면을 깊이 있게 다뤄 좋았습니다."

"어떤 데이터 실수를 피해야 하는지 알게 되어 실용적이었습니다."

비즈니스 상황에 머신러닝을 적용하는 데 중점을 둡니다.

"머신러닝을 비즈니스 문제에 어떻게 적용해야 하는지 명확히 이해할 수 있었습니다."

"현실 세계의 비즈니스 니즈를 ML 문제로 전환하는 방법이 매우 유용했습니다."

"이 강좌는 기술적인 복잡성 없이 ML 프로젝트의 전반적인 그림을 그릴 수 있게 해줍니다."

기술적 구현보다는 개념과 전략에 더 집중되어 있습니다.

"코딩이나 복잡한 알고리즘 설명이 없어 기술적 깊이를 기대한 저에게는 아쉬웠습니다."

"머신러닝의 '무엇'과 '왜'에 집중하고 '어떻게'는 자세히 다루지 않습니다."

"기술적인 전문가보다는 비즈니스 실무자에게 더 적합한 과정이라고 생각합니다."

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

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This book provides a comprehensive overview of ML algorithms and their implementation using popular libraries.

View 핸즈온 머신러닝(3판) on Amazon

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Read the book thoroughly and take notes.
Work through the code examples and experiment with different ML techniques.

Refresh your understanding of Python

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Practicing with Python before beginning the course will improve your confidence in your coding abilities.

Browse courses on Python

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Review basic Python syntax and data structures.
Work through a few coding problems to practice your skills.

Build a machine learning model

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Applying your knowledge to create a practical model will enhance your understanding of the ML process.

Browse courses on Machine Learning

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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.

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Participate in a study group or discussion forum

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Engaging in discussions and peer support will enhance your understanding and provide diverse perspectives.

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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

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Regular practice in solving ML problems will strengthen your analytical and problem-solving skills.

Browse courses on Problem Solving

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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

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Creating content on an ML application will deepen your understanding and enhance your communication skills.

Browse courses on Machine Learning Applications

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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

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Exploring advanced ML techniques through guided tutorials will broaden your knowledge and potential applications.

Browse courses on Machine Learning

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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

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Connecting with professionals in the field will expose you to real-world applications and career opportunities.

Browse courses on Networking

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Research upcoming ML events or conferences.
Register and attend the events.
Network with attendees and learn about their work.

Volunteer on ML-related projects

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Contributing to ML projects through volunteering will provide practical experience and build your portfolio.

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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 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.

See salaries and explore the career path for Machine Learning Engineer

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.