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Machine Learning Rapid Prototyping with IBM Watson Studio

Approx. 9 hours to complete

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Course Summary

Learn how to use IBM Rapid Prototyping and Watson Studio AutoAI to create machine learning models quickly and easily, without the need for coding.

Key Learning Points

Learn how to use IBM Rapid Prototyping and Watson Studio AutoAI to create machine learning models without coding
Understand how to use AutoAI to build, train, and deploy machine learning models
Explore various use cases for AutoAI in industries such as healthcare, finance, and retail

Job Positions & Salaries of people who have taken this course might have

- USA: $112,000 - $150,000
- India: ₹1,000,000 - ₹2,500,000
- Spain: €30,000 - €60,000
- USA: $112,000 - $150,000
- India: ₹1,000,000 - ₹2,500,000
- Spain: €30,000 - €60,000
- USA: $92,000 - $130,000
- India: ₹600,000 - ₹1,800,000
- Spain: €25,000 - €45,000
- USA: $112,000 - $150,000
- India: ₹1,000,000 - ₹2,500,000
- Spain: €30,000 - €60,000
- USA: $92,000 - $130,000
- India: ₹600,000 - ₹1,800,000
- Spain: €25,000 - €45,000
- USA: $145,000 - $200,000
- India: ₹2,000,000 - ₹3,500,000
- Spain: €40,000 - €75,000

Learning Outcomes

Create machine learning models using AutoAI without coding
Understand how to build, train, and deploy machine learning models
Apply AutoAI to various industries and use cases

Prerequisites or good to have knowledge before taking this course

Basic understanding of machine learning concepts
Familiarity with Python programming language

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

Similar Courses

IBM Watson: Building AI
Data Science Methodology

Related Education Paths

Notable People in This Field

Hilary Mason
Andrew Ng

Related Books

Description

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)

Outline

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

Summary of User Reviews

Discover how to create an AI-powered application with IBM Watson Studio AutoAI. This course is highly recommended by users, who found it to be engaging and comprehensive. One key aspect that many users thought was good is the practical approach to learning, which allowed them to apply what they learned in real-world scenarios.