IBM - Machine Learning Rapid Prototyping with IBM Watson Studio
3.0 /5- Offered byCoursera
Machine Learning Rapid Prototyping with IBM Watson Studio at Coursera Overview
Duration | 9 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Machine Learning Rapid Prototyping with IBM Watson Studio at Coursera Highlights
- Earn a shareable certificate upon completion.
- Flexible deadlines according to your schedule.
Machine Learning Rapid Prototyping with IBM Watson Studio at Coursera Course details
- An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio?s AutoAI experiment tool, explaining the underlying technology at work as developed by IBM Research. The focus will be on working with an auto-generated Python notebook. Learners will be provided with test data sets for two use cases.
- This course is intended for practicing Data Scientists. While it showcases the automated AI capabilies of IBM Watson Studio with AutoAI, the course does not explain Machine Learning or Data Science concepts.
- In order to be successful, you should have knowledge of:
- Data Science workflow
- Data Preprocessing
- Feature Engineering
- Machine Learning Algorithms
- Hyperparameter Optimization
- Evaluation measures for models
- Python and scikit-learn library (including Pipeline class)
Machine Learning Rapid Prototyping with IBM Watson Studio at Coursera Curriculum
Building a Rapid Prototype with Watson Studio AutoAI
Welcome/Introduction
Introducing AutoAI
Watson Studio Platform Basics
Building Rapid Prototypes Demo Introduction
Classification Demo
Examining the Notebook
Regression Demo
Course Prerequisites
Learning Outcomes
AutoAI Implementations
References
Summary
Learning Outcomes
Watson Studio Setup
Watson Studio Lab (Activity)
Summary
Learning Outcomes
References
Building Rapid Prototypes Lab (Activity)
Summary
Summary/Review
Check for Understanding
Check for Understanding
Check for Understanding
End of Module Quiz
Automated Data Preparation and Model Selection
Module 2 Introduction
Automated Data Preparation
Classification Prep Demo
Regression Prep Demo
The model selection problem
Multi-armed Bandit Approach
DAUB Algorithm
Demo Classification: Making Changes to the Models
Demo Regression: Making Changes to the Models
Learning Outcomes
Building the Prototype: Prep (graphic)
References
Data Preparation Lab (Activity)
Summary
Learning Outcomes
Building the Prototype: Model selection (graphic)
References
Model Selection Lab (Activity)
Summary
Summary/Review
Check for Understanding
Check for Understanding
End of Module Quiz
Automated Feature Engineering and Hyperparameter Optimization
Module 3 Introduction
Automated Feature Engineering
Cognito - Transforms and the Transformation Graph
Cognito - Transformation Graph Exploration
Demo Classification: Feature Engineering
Demo Regression: Feature Engineering
Automated HPO
RBFOpt
HPO Demo
Learning Outcomes
Building the Prototype: Feature Engineering (graphic)
References
Feature Engineering Lab (Activity)
Summary
Learning Outcomes
Building the Prototype: HPO (graphic)
References
Automated HPO Lab (Activity)
Summary
Summary/Review
Check for Understanding
Check for Understanding
End of Module Quiz
Evaluation and Deployment of AutoAI-generated Solutions
Module 4 Introduction
Evaluation Demo
Deployment Demo
Course Closing
Learning Outcomes
Evaluation Lab (Activity)
References
Summary
Learning Outcomes
Deployment Lab (Activity)
Summary
Summary/Review
More AutoAI Capabilities from IBM / References
Check for Understanding
Check for Understanding
End of Module Quiz
Machine Learning Rapid Prototyping with IBM Watson Studio at Coursera Admission Process
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