课程信息

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第 3 门课程(共 6 门)
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高级
完成时间大约为9 小时
英语(English)
字幕:英语(English)

您将获得的技能

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming
可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
第 3 门课程(共 6 门)
可灵活调整截止日期
根据您的日程表重置截止日期。
高级
完成时间大约为9 小时
英语(English)
字幕:英语(English)

提供方

IBM 徽标

IBM

教学大纲 - 您将从这门课程中学到什么

1

1

完成时间为 5 小时

Data transforms and feature engineering

完成时间为 5 小时
6 个视频 (总计 31 分钟), 14 个阅读材料, 5 个测验
6 个视频
Introduction to Class Imbalance1分钟
Class Imbalance Deep Dive9分钟
Introduction to Dimensionality Reduction2分钟
Dimension Reduction13分钟
Case Study Intro / Feature Engineering1分钟
14 个阅读材料
Data Transformation: Through the eyes of our Working Example3分钟
Transforms with scikit-learn3分钟
Pipelines3分钟
Class imbalance: Through the Eyes of our Working Example3分钟
Class Imbalance5分钟
Sampling Techniques2分钟
Models that Naturally Handle Imbalance2分钟
Data Bias2分钟
Dimensionality Reduction: Through the Eyes of Our Working Example3分钟
Why is Dimensionality Reduction Important?3分钟
Dimensionality Reduction and Topic models5分钟
Topic modeling: Through the Eyes of our Working Example3分钟
Getting Started with the Topic Modeling Case Study (hands-on)2小时
Data Transforms and Feature Engineering: Summary/Review5分钟
5 个练习
Getting Started: Check for Understanding2分钟
Class Imbalance, Data Bias: Check for Understanding2分钟
Dimensionality Reduction: Check for Understanding3分钟
CASE STUDY - Topic Modeling: Check for Understanding2分钟
Data Transforms and Feature Engineering: End of Module Quiz10分钟
2

2

完成时间为 4 小时

Pattern recognition and data mining best practices

完成时间为 4 小时
4 个视频 (总计 10 分钟), 11 个阅读材料, 5 个测验
4 个视频
Introduction to Outliers2分钟
Outlier Detection3分钟
Introduction to Unsupervised learning2分钟
11 个阅读材料
ai360: Through the Eyes of our Working Example3分钟
Introduction to ai360 (hands-on)15分钟
Outlier Detection: Through the Eyes of our Working Example3分钟
Outliers3分钟
Unsupervised learning: Through the Eyes of our Working Example3分钟
An Overview of Unsupervised Learning2分钟
Clustering3分钟
Clustering Evaluation3分钟
Clustering: Through the Eyes of our Working Example3分钟
Getting Started with the Clustering Case Study (hands-on)2 小时 10 分
Pattern Recognition and Data Mining Best Practices: Summary/Review4分钟
5 个练习
ai360 Tutorial: Check for Understanding2分钟
Outlier Detection: Check for Understanding2分钟
Unsupervised Learning: Check for Understanding2分钟
CASE STUDY - Clustering: Check for Understanding2分钟
Pattern Recognition and Data Mining Best Practices: End of Module Quiz12分钟

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关于 IBM AI Enterprise Workflow 专项课程

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

常见问题

  • 讲座和作业的访问权限取决于您的注册类型。如果您以旁听模式参加课程,则可以免费查看大多数课程资料。要访问评分作业并获得证书,您需要在旁听期间或之后购买证书体验。如果看不到旁听选项:

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  • 您注册课程后,将有权访问专项课程中的所有课程,并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中,您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容,可以免费旁听课程。

  • 如果订阅,您可以获得 7 天免费试听,在此期间,您可以取消课程,无需支付任何罚金。在此之后,我们不会退款,但您可以随时取消订阅。请阅读我们完整的退款政策

  • 是的,Coursera 可以为无法承担费用的学生提供助学金。通过点击左侧“注册”按钮下的“助学金”链接可以申请助学金。您可以根据屏幕提示完成申请,申请获批后会收到通知。您需要针对专项课程中的每一门课程完成上述步骤,包括毕业项目。了解更多

  • 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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