- Data Science
- Deep Learning
- Artificial Intelligence (AI)
- Machine Learning
- Python Programming
- Feature Engineering
- Statistical Hypothesis Testing
- Exploratory Data Analysis
- Regression Analysis
- Supervised Learning
- Linear Regression
- Ridge Regression
IBM 기계 학습 전문 자격증
Machine Learning, Time Series & Survival Analysis. Develop working skills in the main areas of Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. Also gain practice in specialized topics such as Time Series Analysis and Survival Analysis.
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응용 학습 프로젝트
This Professional Certificate has a strong emphasis on developing the skills that help you advance a career in Machine Learning. All the courses include a series of hands-on labs and final projects that help you focus on a specific project that interests you. Throughout this Professional Certificate, you will gain exposure to a series of tools, libraries, cloud services, datasets, algorithms, assignments and projects that will provide you with practical skills with applicability to Machine Learning jobs. These skills include:
Tools: Jupyter Notebooks and Watson Studio
Libraries: Pandas, NumPy, Matplotlib, Seaborn, ipython-sql, Scikit-learn, ScipPy, Keras, and TensorFlow.
관련 경험이 어느 정도 필요합니다.
관련 경험이 어느 정도 필요합니다.
전문 자격증이란 무엇인가요?
기술을 쌓아서 실무에 대비
새로운 분야에서 커리어를 찾고 있거나 현재 커리어에 변화를 주고 싶다면, Coursera의 전문 자격증을 취득하여 준비된 인재로 거듭날 수 있습니다. 가장 편리한 시간과 장소에서 나에게 맞는 속도로 배워보세요. 지금 바로 등록하고 7일 무료 평가판을 통해 새로운 진로를 탐색해보세요. 언제든지 학습을 일시 중지하거나 구독을 종료할 수 있습니다.
실습 프로젝트
실습 프로젝트에서 기술을 적용해보고, 미래 고용주에게 실무적으로 준비된 인재임을 보여주는 포트폴리오를 만들어보세요. 자격증을 취득하려면 프로젝트를 성공적으로 완료해야 합니다.
경력 자격 증명 취득
프로그램의 모든 강좌를 완료하면 전문가 네트워크에서 공유할 수 있는 자격증을 얻게 되며, 새로운 커리어를 시작하는 데 도움이 되는 커리어 지원 리소스에 액세스할 수 있게 됩니다. 많은 전문 자격증은 해당 전문 자격증의 자격 증명을 인정해주는 채용 파트너가 있거나, 자격증 시험을 준비하는 데 도움이 됩니다. 해당하는 경우 개별 전문 자격증 페이지에서 자세한 내용을 알아볼 수 있습니다.

이 전문 자격증에는 6개의 강좌가 있습니다.
Exploratory Data Analysis for Machine Learning
This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing.
Supervised Machine Learning: Regression
This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.
Supervised Machine Learning: Classification
This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.
Unsupervised Machine Learning
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.
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IBM
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
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하나의 강좌에만 등록할 수 있나요?
전문 분야를 완료하는 데 얼마나 걸리나요?
What background knowledge is necessary?
Do I need to take the courses in a specific order?
전문 분야를 완료하면 대학 학점을 받을 수 있나요?
What will I be able to do upon completing the Specialization?
이 강좌는 100% 온라인으로 진행되나요? 직접 참석해야 하는 수업이 있나요?
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