課程信息
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第 3 門課程(共 6 門)

100% 在線

立即開始,按照自己的計劃學習。

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6 小時

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

英語(English)

字幕:英語(English)

您將獲得的技能

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming

第 3 門課程(共 6 門)

100% 在線

立即開始,按照自己的計劃學習。

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6 小時

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

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 4 小時

Data transforms and feature engineering

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
完成時間為 3 小時

Pattern recognition and data mining best practices

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

講師

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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

關於 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....

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

  • 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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