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.
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Machine Learning Rapid Prototyping with IBM Watson Studio
IBMAbout this Course
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Intermediate Level
Approx. 9 hours to complete
English
Subtitles: 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.
Intermediate Level
Approx. 9 hours to complete
English
Subtitles: English
Offered by
IBM
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
Syllabus - What you will learn from this course
2 hours to complete
Building a Rapid Prototype with Watson Studio AutoAI
In this module, you'll learn about the developing landscape of AutoAI technologies. You'll also become familiar with the Watson Studio platform in order to be able to perform your own AutoAI Experiments. After observing the AutoAI tool build prototypes for two use cases, you will try out the tool for yourself to build additional prototypes.
2 hours to complete
7 videos (Total 36 min), 14 readings, 4 quizzes
7 videos
Introducing AutoAI2m
Watson Studio Platform Basics2m
Building Rapid Prototypes Demo Introduction2m
Classification Demo11m
Examining the Notebook4m
Regression Demo9m
14 readings
Course Prerequisites2m
Learning Outcomes2m
AutoAI Implementations2m
References2m
Summary2m
Learning Outcomes2m
Watson Studio Setup20m
Watson Studio Lab (Activity)20m
Summary2m
Learning Outcomes2m
References2m
Building Rapid Prototypes Lab (Activity)30m
Summary2m
Summary/Review2m
4 practice exercises
Check for Understanding5m
Check for Understanding5m
Check for Understanding5m
End of Module Quiz5m
2 hours to complete
Automated Data Preparation and Model Selection
In this module, you will learn about the automated data preparation techniques performed by AutoAI and get a chance to experiment with different settings for data preprocessing in the AutoAI-generated Python notebook. You'll also learn about the procedure for automated model selection and experiment using different models on the datasets.
2 hours to complete
9 videos (Total 34 min), 11 readings, 3 quizzes
9 videos
Automated Data Preparation10m
Classification Prep Demo3m
Regression Prep Demo4m
The model selection problem2m
Multi-armed Bandit Approach4m
DAUB Algorithm5m
Demo Classification: Making Changes to the Models1m
Demo Regression: Making Changes to the Models1m
11 readings
Learning Outcomes2m
Building the Prototype: Prep (graphic)2m
References2m
Data Preparation Lab (Activity)30m
Summary2m
Learning Outcomes2m
Building the Prototype: Model selection (graphic)2m
References10m
Model Selection Lab (Activity)30m
Summary2m
Summary/Review2m
3 practice exercises
Check for Understanding5m
Check for Understanding5m
End of Module Quiz5m
2 hours to complete
Automated Feature Engineering and Hyperparameter Optimization
In this module, you will learn about the algorithm for automated feature engineering and perform some exploratory data analysis to try to understand why the algorithm performed particular feature transformations. You'll also learn about sophisticated methods for optimizing hyperparameters and explore hyperparameter tuning on the datasets using the AutoAI-generated Python notebook.
2 hours to complete
9 videos (Total 36 min), 11 readings, 3 quizzes
9 videos
Automated Feature Engineering6m
Cognito - Transforms and the Transformation Graph4m
Cognito - Transformation Graph Exploration3m
Demo Classification: Feature Engineering6m
Demo Regression: Feature Engineering3m
Automated HPO5m
RBFOpt2m
HPO Demo3m
11 readings
Learning Outcomes2m
Building the Prototype: Feature Engineering (graphic)2m
References2m
Feature Engineering Lab (Activity)30m
Summary2m
Learning Outcomes2m
Building the Prototype: HPO (graphic)2m
References2m
Automated HPO Lab (Activity)30m
Summary2m
Summary/Review2m
3 practice exercises
Check for Understanding5m
Check for Understanding5m
End of Module Quiz5m
2 hours to complete
Evaluation and Deployment of AutoAI-generated Solutions
In this module, you will evaluate prototypes using the different evaluation metrics calculated by the AutoAI tool. You will also deploy the prototype for testing using the Watson Machine Learning API.
2 hours to complete
4 videos (Total 8 min), 9 readings, 4 quizzes
9 readings
Learning Outcomes2m
Evaluation Lab (Activity)10m
References2m
Summary2m
Learning Outcomes2m
Deployment Lab (Activity)15m
Summary2m
Summary/Review2m
More AutoAI Capabilities from IBM / References2m
3 practice exercises
Check for Understanding5m
Check for Understanding5m
End of Module Quiz5m
Reviews
TOP REVIEWS FROM MACHINE LEARNING RAPID PROTOTYPING WITH IBM WATSON STUDIO
by NBSep 13, 2020
Very much informative and useful with hands on excercise
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