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Machine Learning Rapid Prototyping with IBM Watson Studio

Course content updated by LearnQuest
Price
239 USD
9 Hours
W7072GW
Self-Paced Training (WBT)
IBM Business Partner
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Course Description

Overview

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.

 

Purchase this course as part of a subscription

  • IBM Data/AI Individual Subscription (SUBR003G)
  • IBM Digital Learning Subscription — IBM Data/AI Enterprise Subscription (SUBR004G)

Objectives

  • Building a rapid prototype of Watson Studio AI
  • Automated Data Preparation and Model Selection
  • Automated Feature Engineering and Hyperparameter Optimization
  • Evaluation and Deployment of AutoAI-generated Solutions

Audience

This course is intended for practicing Data Scientists. While it showcases the automated AI capabilities of IBM Watson Studio with AutoAI, the course does not explain Machine Learning or Data Science concepts. 

Prerequisites

    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)

Topics

Building a rapid prototype of Watson Studio AI 

- Describe the benefits of AutoAI for rapid prototyping 
- Identify implementations of AutoAI 
- Become familiar with the Watson Studio platform 
- Build rapid prototypes using Watson Studio AutoAI 
- Generate a Python notebook of the prototype with one click 

Automated Data Preparation and Model Selection 

- Evaluate the data preprocessing steps for the use cases 
- Refine data preprocessing using the AutoAI-generated Python notebook 
- Examine the model selection outcome for use cases 
- Refine the Python notebook to make changes to the selected model 

Automated Feature Engineering and Hyperparameter Optimization 

- Explain how the Cognito algorithm can save time by automating feature engineering 
- Evaluate the automated feature engineering performance for the use cases 
- Describe several strategies for HPO in order of increasing sophistication 
- Observe how changes to the model hyperparameters in the Python notebook affect the prototype's performance 

Evaluation and Deployment of AutoAI-generated Solutions 

- Evaluate the prototype for further development or deployment based on calculated performance metrics 
- Deploy the prototype using Watson Machine Learning 
 

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Self-Paced Training Info

Learn at your own pace with anytime, anywhere training

  • Same in-demand topics as instructor-led public and private classes.
  • Standalone learning or supplemental reinforcement.
  • e-Learning content varies by course and technology.
  • View the Self-Paced version of this outline and what is included in the SPVC course.
  • Learn more about e-Learning

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Self-Paced Training Terms & Conditions

IBM WEB BASED TRAINING (WBT) IS SELF-DIRECTED AND SELF-PACED. Before you enroll, review the system requirements to ensure that your system meets the minimum requirements for this course. AFTER YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT. You are billed for the course when you submit the enrollment form. Web Based Training courses are non-refundable. Once you purchase a Web Based Training course, you will be charged the full price.

IBM Web Based Training courses are sold on a per-user basis. WBT courses provide a training advantage for you and your teams, helping you get up to speed quickly. Take the courses you need, at your convenience and at your own pace.

You can start the course at any time within 12 months of enrolling for the course. After you register/start the course, you have 30 days to complete your course. Within this 30 days, the self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.

System Requirements

To participate in this course, the student workstation must meet the following hardware requirements:
  • Minimum of 256 MB of memory
  • Windows Vista or Windows 7-10 (32-bit or 64-bit edition)
  • Internet Explorer 6 or higher or Firefox ESR
  • 128-bit encryption
  • Citrix Receiver for connection to the IBM Remote Lab Platform
  • Java
  • Access to Internet with at least 128 kbps down and 128 kbps up
  • Other platforms/combinations may work but are not officially supported by IBM.

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