About this Course
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다음 전문 분야의 6개 강좌 중 3번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 7시간 필요

권장: This course requires 7.5 to 9 hours of study....

영어

자막: 영어

귀하가 습득할 기술

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming
Course을(를) 수강하는 학습자
  • Data Scientists
  • Researchers
  • Software Engineers

다음 전문 분야의 6개 강좌 중 3번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 7시간 필요

권장: This course requires 7.5 to 9 hours of study....

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 4시간 필요

Data transforms and feature engineering

6개 동영상 (총 31분), 14 readings, 5 quizzes
6개의 동영상
Introduction to Class Imbalance1m
Class Imbalance Deep Dive9m
Introduction to Dimensionality Reduction2m
Dimension Reduction13m
Case study intro / Feature Engineering1m
14개의 읽기 자료
Data Transformation: Through the eyes of our Working Example3m
Transforms / Scikit-learn3m
Pipelines3m
Class imbalance: Through the eyes of our Working Example3m
Class Imbalance5m
Sampling techniques2m
Models that naturally handle imbalance2m
Data bias2m
Dimensionality Reduction: Through the eyes of our Working Example3m
Why is dimensionality reduction important?3m
Dimensionality reduction and Topic models5m
Topic modeling: Through the eyes of our Working Example3m
Getting Started with the topic modeling case study (hands-on)2h
Data transforms and feature engineering: Summary/Review5m
5개 연습문제
Getting Started: Check for Understanding2m
Class imbalance, data bias: Check for Understanding2m
Dimensionality Reduction: Check for Understanding3m
CASE STUDY - Topic modeling: Check for Understanding2m
Data transforms and feature engineering:End of Module Quiz10m
2
완료하는 데 3시간 필요

Pattern recognition and data mining best practices

4개 동영상 (총 10분), 11 readings, 5 quizzes
4개의 동영상
Introduction to Outliers2m
Outlier Detection3m
Introduction to Unsupervised learning2m
11개의 읽기 자료
ai360: Through the eyes of our Working Example3m
Introduction to ai360 (hands-on)15m
Outlier detection: Through the eyes of our Working Example3m
Outliers3m
Unsupervised learning: Through the eyes of our Working Example3m
An overview of unsupervised learning2m
Clustering3m
Clustering evaluation3m
Clustering: Through the eyes of our Working Example3m
Getting Started with the clustering case study (hands-on)2시 10분
Pattern recognition and data mining best practices: Summary/Review4m
5개 연습문제
ai360 Tutorial: Check for Understanding2m
Outlier detection: Check for Understanding2m
Unsupervised learning: Check for Understanding2m
CASE STUDY - Clustering: Check for Understanding2m
Pattern recognition and data mining best practices: End of Module Quiz12m

강사

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Mark J Grover

Digital Content Delivery Lead
IBM Data & AI Learning
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Ray Lopez, Ph.D.

Data Science Curriculum Leader
IBM Data & Artificial Intelligence

IBM 정보

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame....

IBM AI Enterprise Workflow 전문 분야 정보

This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company. Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such a Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools. After successfully completing this specialization, you will be ready to take the official IBM certification examination for the IBM AI Enterprise Workflow....
IBM AI Enterprise Workflow

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

  • This course assumes that you are already familiar with basic data science concepts including probability and statistics, linear algebra, machine learning, and the use of Python and Jupyter. It is assumed you have completed the first two courses of the specialization: AI Workflow: Business Priorities and Data Ingestion, AI Workflow: Data Analysis and Hypothesis Testing.

  • No. Most of the exercises may be completed with open source tools running on your personal computer. However, the exercises are designed with an enterprise focus and are intended to be run in an enterprise environment that allows for easier sharing and collaboration. The exercises in the last two modules of the course are heavily focused on deployment and testing of machine learning models and use the IBM Watson tooling found on the IBM Cloud.

  • Yes. All IBM Cloud Data and AI services are based upon open source technologies.

  • The exercises in the course may be completed by anyone using the IBM Cloud "Lite" plan, which is free for use.

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