About this Course

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Advanced Level
Approx. 12 hours to complete
English

Skills you will gain

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 12 hours to complete
English

Offered by

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IBM

Syllabus - What you will learn from this course

Week
1

Week 1

6 hours to complete

Data transforms and feature engineering

6 hours to complete
6 videos (Total 31 min), 14 readings, 5 quizzes
6 videos
Introduction to Class Imbalance1m
Class Imbalance Deep Dive9m
Introduction to Dimensionality Reduction2m
Dimension Reduction13m
Case Study Intro / Feature Engineering1m
14 readings
Data Transformation: Through the eyes of our Working Example3m
Transforms with scikit-learn3m
Pipelines3m
Class imbalance: Through the Eyes of our Working Example3m
Class Imbalance5m
Sampling Techniques2m
Models that Naturally Handle Imbalance2m
Data Bias2m
Dimensionality Reduction: Through the Eyes of Our Working Example3m
Why is Dimensionality Reduction Important?3m
Dimensionality Reduction and Topic models5m
Topic modeling: Through the Eyes of our Working Example3m
Getting Started with the Topic Modeling Case Study (hands-on)2h
Data Transforms and Feature Engineering: Summary/Review5m
5 practice exercises
Getting Started: Check for Understanding30m
Class Imbalance, Data Bias: Check for Understanding30m
Dimensionality Reduction: Check for Understanding3m
CASE STUDY - Topic Modeling: Check for Understanding30m
Data Transforms and Feature Engineering: End of Module Quiz10m
Week
2

Week 2

6 hours to complete

Pattern recognition and data mining best practices

6 hours to complete
5 videos (Total 16 min), 11 readings, 5 quizzes
5 videos
Introduction to Outliers2m
Outlier Detection3m
Introduction to Unsupervised learning2m
Unsupervised Learning5m
11 readings
ai360: Through the Eyes of our Working Example3m
Introduction to ai360 (hands-on)15m
Outlier Detection: Through the Eyes of our Working Example3m
Outliers3m
Unsupervised learning: Through the Eyes of our Working Example3m
An Overview of Unsupervised Learning2m
Clustering3m
Clustering Evaluation3m
Clustering: Through the Eyes of our Working Example3m
Getting Started with the Clustering Case Study (hands-on)2h 10m
Pattern Recognition and Data Mining Best Practices: Summary/Review4m
5 practice exercises
ai360 Tutorial: Check for Understanding30m
Outlier Detection: Check for Understanding30m
Unsupervised Learning: Check for Understanding30m
CASE STUDY - Clustering: Check for Understanding30m
Pattern Recognition and Data Mining Best Practices: End of Module Quiz12m

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IBM AI Enterprise Workflow

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