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自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

上級レベル

約6時間で修了

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

英語

字幕:英語

習得するスキル

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming

次における6の3コース

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

上級レベル

約6時間で修了

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

英語

字幕:英語

シラバス - 本コースの学習内容

1
4時間で修了

Data transforms and feature engineering

6件のビデオ (合計31分), 14 readings, 5 quizzes
6件のビデオ
Introduction to Class Imbalance1 分
Class Imbalance Deep Dive9 分
Introduction to Dimensionality Reduction2 分
Dimension Reduction13 分
Case study intro / Feature Engineering1 分
14件の学習用教材
Data Transformation: Through the eyes of our Working Example3 分
Transforms / Scikit-learn3 分
Pipelines3 分
Class imbalance: Through the eyes of our Working Example3 分
Class Imbalance5 分
Sampling techniques2 分
Models that naturally handle imbalance2 分
Data bias2 分
Dimensionality Reduction: Through the eyes of our Working Example3 分
Why is dimensionality reduction important?3 分
Dimensionality reduction and Topic models5 分
Topic modeling: Through the eyes of our Working Example3 分
Getting Started with the topic modeling case study (hands-on)2 時間
Data transforms and feature engineering: Summary/Review5 分
5の練習問題
Getting Started: Check for Understanding2 分
Class imbalance, data bias: Check for Understanding2 分
Dimensionality Reduction: Check for Understanding3 分
CASE STUDY - Topic modeling: Check for Understanding2 分
Data transforms and feature engineering:End of Module Quiz10 分
2
3時間で修了

Pattern recognition and data mining best practices

4件のビデオ (合計10分), 11 readings, 5 quizzes
4件のビデオ
Introduction to Outliers2 分
Outlier Detection3 分
Introduction to Unsupervised learning2 分
11件の学習用教材
ai360: Through the eyes of our Working Example3 分
Introduction to ai360 (hands-on)15 分
Outlier detection: Through the eyes of our Working Example3 分
Outliers3 分
Unsupervised learning: Through the eyes of our Working Example3 分
An overview of unsupervised learning2 分
Clustering3 分
Clustering evaluation3 分
Clustering: Through the eyes of our Working Example3 分
Getting Started with the clustering case study (hands-on)2 時間 10 分
Pattern recognition and data mining best practices: Summary/Review4 分
5の練習問題
ai360 Tutorial: Check for Understanding2 分
Outlier detection: Check for Understanding2 分
Unsupervised learning: Check for Understanding2 分
CASE STUDY - Clustering: Check for Understanding2 分
Pattern recognition and data mining best practices: End of Module Quiz12 分

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

IBMについて

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専門講座について

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

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  • 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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