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Machine Learning Rapid Prototyping with IBM Watson Studio
  • Level Professional
  • المدة 9 ساعات hours
  • الطبع بواسطة IBM
  • Offered by

عن

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)

الوحدات

Introducing AutoAI

    • 2 Videos
    • 5 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

2 Videos

  • Welcome/Introduction
  • Introducing AutoAI

5 Readings

  • Course Prerequisites
  • Learning Outcomes
  • AutoAI Implementations
  • References
  • Summary

Watson Studio Platform Basics

    • 1 Videos
    • 4 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

1 Videos

  • Watson Studio Platform Basics

4 Readings

  • Learning Outcomes
  • Watson Studio Setup
  • Watson Studio Lab (Activity)
  • Summary

Building Rapid Prototypes

    • 4 Videos
    • 4 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

4 Videos

  • Building Rapid Prototypes Demo Introduction
  • Classification Demo
  • Examining the Notebook
  • Regression Demo

4 Readings

  • Learning Outcomes
  • References
  • Building Rapid Prototypes Lab (Activity)
  • Summary

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • End of Module Quiz

1 Readings

  • Summary/Review

Automated Data Preparation

    • 4 Videos
    • 5 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

4 Videos

  • Module 2 Introduction
  • Automated Data Preparation
  • Classification Prep Demo
  • Regression Prep Demo

5 Readings

  • Learning Outcomes
  • Building the Prototype: Prep (graphic)
  • References
  • Data Preparation Lab (Activity)
  • Summary

Automated Model Selection using DAUB Algorithm

    • 5 Videos
    • 5 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

5 Videos

  • The model selection problem
  • Multi-armed Bandit Approach
  • DAUB Algorithm
  • Demo Classification: Making Changes to the Models
  • Demo Regression: Making Changes to the Models

5 Readings

  • Learning Outcomes
  • Building the Prototype: Model selection (graphic)
  • References
  • Model Selection Lab (Activity)
  • Summary

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • End of Module Quiz

1 Readings

  • Summary/Review

Automated Feature Engineering with Cognito

    • 6 Videos
    • 5 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

6 Videos

  • Module 3 Introduction
  • Automated Feature Engineering
  • Cognito - Transforms and the Transformation Graph
  • Cognito - Transformation Graph Exploration
  • Demo Classification: Feature Engineering
  • Demo Regression: Feature Engineering

5 Readings

  • Learning Outcomes
  • Building the Prototype: Feature Engineering (graphic)
  • References
  • Feature Engineering Lab (Activity)
  • Summary

Automated Hyperparameter Optimization with RBFOpt

    • 3 Videos
    • 5 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

3 Videos

  • Automated HPO
  • RBFOpt
  • HPO Demo

5 Readings

  • Learning Outcomes
  • Building the Prototype: HPO (graphic)
  • References
  • Automated HPO Lab (Activity)
  • Summary

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • End of Module Quiz

1 Readings

  • Summary/Review

Evaluating AutoAI-generated Prototypes

    • 2 Videos
    • 4 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

2 Videos

  • Module 4 Introduction
  • Evaluation Demo

4 Readings

  • Learning Outcomes
  • Evaluation Lab (Activity)
  • References
  • Summary

Deploying AutoAI-generated Prototypes

    • 1 Videos
    • 3 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

1 Videos

  • Deployment Demo

3 Readings

  • Learning Outcomes
  • Deployment Lab (Activity)
  • Summary

End of module review & evaluation

    • 1 Videos
    • 2 Readings
    • 1 PeerReview
    • 1 Assignment
Show info about module content

1 Assignment

  • End of Module Quiz

1 Peer Review

  • Choose a Data Set and Perform an AutoAI Experiment

1 Videos

  • Course Closing

2 Readings

  • Summary/Review
  • More AutoAI Capabilities from IBM / References

Auto Summary

Embark on a journey into the world of automated AI with the Machine Learning Rapid Prototyping with IBM Watson Studio course, tailored specifically for seasoned data scientists. Delve into the cutting-edge trend of leveraging automation for model selection, feature engineering, and hyperparameter optimization, allowing you to streamline the prototyping process and focus on applying your domain expertise. Guided by the innovative AutoAI experiment tool within IBM Watson Studio, this course offers a comprehensive walkthrough of creating an end-to-end automated pipeline. You'll get hands-on with auto-generated Python notebooks and work with test datasets across two practical use cases, all while gaining insights into the sophisticated technology pioneered by IBM Research. Designed for professionals, this course assumes a solid foundation in data science workflows, preprocessing, feature engineering, machine learning algorithms, hyperparameter optimization, model evaluation, and proficiency in Python and scikit-learn library, including the Pipeline class. Offered through Coursera, the course spans 540 minutes of in-depth content and is available under Starter and Professional subscription plans. Elevate your data science skills and harness the power of automation with IBM Watson Studio's AutoAI in this expert-level course.

Mark J Grover
Instructors

Mark J Grover

Meredith Mante
Instructors

Meredith Mante