课程信息
3,102 次近期查看

第 3 门课程(共 6 门)

100% 在线

立即开始,按照自己的计划学习。

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5 小时

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

英语(English)

字幕:英语(English)

您将获得的技能

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming

第 3 门课程(共 6 门)

100% 在线

立即开始,按照自己的计划学习。

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5 小时

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

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 4 小时

Data transforms and feature engineering

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 / 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
完成时间为 3 小时

Pattern recognition and data mining best practices

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

讲师

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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

关于 IBM

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....

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

  • 注册以便获得证书后,您将有权访问所有视频、测验和编程作业(如果适用)。只有在您的班次开课之后,才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程,可能无法访问某些作业。

  • 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。

  • 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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