Machine Learning Rapid Prototyping with IBM Watson Studio

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Machine Learning Rapid Prototyping with IBM Watson Studio

Instructors: Mark J Grover

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4 modules

Gain insight into a topic and learn the fundamentals.

16 reviews

Intermediate level

Some related experience required

9 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

16 reviews

Intermediate level

Some related experience required

9 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

13 assignments¹

AI Graded see disclaimer

Taught in English

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There are 4 modules in this course

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)

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.

What's included

7 videos14 readings4 assignments

7 videos Total 36 minutes

Welcome/Introduction 2 minutes
Introducing AutoAI 3 minutes
Watson Studio Platform Basics 3 minutes
Building Rapid Prototypes Demo Introduction 3 minutes
Classification Demo 11 minutes
Examining the Notebook 5 minutes
Regression Demo 9 minutes

14 readings Total 92 minutes

Course Prerequisites 2 minutes
Learning Outcomes 2 minutes
AutoAI Implementations 2 minutes
References 2 minutes
Summary 2 minutes
Learning Outcomes 2 minutes
Watson Studio Setup 20 minutes
Watson Studio Lab (Activity) 20 minutes
Summary 2 minutes
Learning Outcomes 2 minutes
References 2 minutes
Building Rapid Prototypes Lab (Activity) 30 minutes
Summary 2 minutes
Summary/Review 2 minutes

4 assignments Total 20 minutes

Check for Understanding 5 minutes
Check for Understanding 5 minutes
Check for Understanding 5 minutes
End of Module Quiz 5 minutes

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.

What's included

9 videos11 readings3 assignments

9 videos Total 34 minutes

Module 2 Introduction 0 minutes
Automated Data Preparation 11 minutes
Classification Prep Demo 4 minutes
Regression Prep Demo 4 minutes
The model selection problem 2 minutes
Multi-armed Bandit Approach 4 minutes
DAUB Algorithm 6 minutes
Demo Classification: Making Changes to the Models 2 minutes
Demo Regression: Making Changes to the Models 1 minute

11 readings Total 86 minutes

Learning Outcomes 2 minutes
Building the Prototype: Prep (graphic) 2 minutes
References 2 minutes
Data Preparation Lab (Activity) 30 minutes
Summary 2 minutes
Learning Outcomes 2 minutes
Building the Prototype: Model selection (graphic) 2 minutes
References 10 minutes
Model Selection Lab (Activity) 30 minutes
Summary 2 minutes
Summary/Review 2 minutes

3 assignments Total 15 minutes

Check for Understanding 5 minutes
Check for Understanding 5 minutes
End of Module Quiz 5 minutes

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.

What's included

9 videos11 readings3 assignments

9 videos Total 36 minutes

Module 3 Introduction 0 minutes
Automated Feature Engineering 7 minutes
Cognito - Transforms and the Transformation Graph 4 minutes
Cognito - Transformation Graph Exploration 3 minutes
Demo Classification: Feature Engineering 7 minutes
Demo Regression: Feature Engineering 3 minutes
Automated HPO 5 minutes
RBFOpt 3 minutes
HPO Demo 3 minutes

11 readings Total 78 minutes

Learning Outcomes 2 minutes
Building the Prototype: Feature Engineering (graphic) 2 minutes
References 2 minutes
Feature Engineering Lab (Activity) 30 minutes
Summary 2 minutes
Learning Outcomes 2 minutes
Building the Prototype: HPO (graphic) 2 minutes
References 2 minutes
Automated HPO Lab (Activity) 30 minutes
Summary 2 minutes
Summary/Review 2 minutes

3 assignments Total 15 minutes

Check for Understanding 5 minutes
Check for Understanding 5 minutes
End of Module Quiz 5 minutes

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.

What's included

4 videos9 readings3 assignments1 peer review

4 videos Total 8 minutes

Module 4 Introduction 0 minutes
Evaluation Demo 3 minutes
Deployment Demo 3 minutes
Course Closing 1 minute

9 readings Total 39 minutes

Learning Outcomes 2 minutes
Evaluation Lab (Activity) 10 minutes
References 2 minutes
Summary 2 minutes
Learning Outcomes 2 minutes
Deployment Lab (Activity) 15 minutes
Summary 2 minutes
Summary/Review 2 minutes
More AutoAI Capabilities from IBM / References 2 minutes

3 assignments Total 15 minutes

Check for Understanding 5 minutes
Check for Understanding 5 minutes
End of Module Quiz 5 minutes

1 peer review Total 60 minutes

Choose a Data Set and Perform an AutoAI Experiment 60 minutes

Instructors

Instructor ratings

(6 ratings)

Mark J Grover

13 Courses 163,762 learners

Offered by

IBM

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Reviewed on Sep 13, 2020

Very much informative and useful with hands on excercise

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