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第 3 門課程(共 6 門)
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完成時間大約為7 小時
英語(English)
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Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming
可分享的證書
完成後獲得證書
100% 在線
立即開始,按照自己的計劃學習。
第 3 門課程(共 6 門)
可靈活調整截止日期
根據您的日程表重置截止日期。
高級
完成時間大約為7 小時
英語(English)
字幕:英語(English)

提供方

IBM 徽標

IBM

教學大綱 - 您將從這門課程中學到什麼

1

1

完成時間為 4 小時

Data transforms and feature engineering

完成時間為 4 小時
6 個視頻 (總計 31 分鐘), 14 個閱讀材料, 5 個測驗
6 個視頻
Introduction to Class Imbalance1分鐘
Class Imbalance Deep Dive9分鐘
Introduction to Dimensionality Reduction2分鐘
Dimension Reduction13分鐘
Case study intro / Feature Engineering1分鐘
14 個閱讀材料
Data Transformation: Through the eyes of our Working Example3分鐘
Transforms / Scikit-learn3分鐘
Pipelines3分鐘
Class imbalance: Through the eyes of our Working Example3分鐘
Class Imbalance5分鐘
Sampling techniques2分鐘
Models that naturally handle imbalance2分鐘
Data bias2分鐘
Dimensionality Reduction: Through the eyes of our Working Example3分鐘
Why is dimensionality reduction important?3分鐘
Dimensionality reduction and Topic models5分鐘
Topic modeling: Through the eyes of our Working Example3分鐘
Getting Started with the topic modeling case study (hands-on)2小時
Data transforms and feature engineering: Summary/Review5分鐘
5 個練習
Getting Started: Check for Understanding2分鐘
Class imbalance, data bias: Check for Understanding2分鐘
Dimensionality Reduction: Check for Understanding3分鐘
CASE STUDY - Topic modeling: Check for Understanding2分鐘
Data transforms and feature engineering:End of Module Quiz10分鐘
2

2

完成時間為 3 小時

Pattern recognition and data mining best practices

完成時間為 3 小時
4 個視頻 (總計 10 分鐘), 11 個閱讀材料, 5 個測驗
4 個視頻
Introduction to Outliers2分鐘
Outlier Detection3分鐘
Introduction to Unsupervised learning2分鐘
11 個閱讀材料
ai360: Through the eyes of our Working Example3分鐘
Introduction to ai360 (hands-on)15分鐘
Outlier detection: Through the eyes of our Working Example3分鐘
Outliers3分鐘
Unsupervised learning: Through the eyes of our Working Example3分鐘
An overview of unsupervised learning2分鐘
Clustering3分鐘
Clustering evaluation3分鐘
Clustering: Through the eyes of our Working Example3分鐘
Getting Started with the clustering case study (hands-on)2 小時 10 分
Pattern recognition and data mining best practices: Summary/Review4分鐘
5 個練習
ai360 Tutorial: Check for Understanding2分鐘
Outlier detection: Check for Understanding2分鐘
Unsupervised learning: Check for Understanding2分鐘
CASE STUDY - Clustering: Check for Understanding2分鐘
Pattern recognition and data mining best practices: End of Module Quiz12分鐘

審閱

來自AI WORKFLOW: FEATURE ENGINEERING AND BIAS DETECTION的熱門評論

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關於 IBM AI Enterprise Workflow 專項課程

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

常見問題

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • 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. The certification exam is administered by Pearson VUE and must be taken at one of their testing facilities. You may visit their site at https://home.pearsonvue.com/ for more information.

  • Please visit the Pearson VUE web site at https://home.pearsonvue.com/ for the latest information on taking the AI Enterprise Workflow certification test.

  • It is highly recommended that you have at least a basic working knowledge of design thinking and Watson Studio prior to taking this course. Please visit the IBM Skills Gateway at http://ibm.com/training/badges and "Find a Badge" related to "design thinking" or "Watson Studio". From there you will be directed to courses covering these topics.

  • 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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