Machine Learning Rapid Prototyping with IBM Watson Studio

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Course Features

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Duration

9 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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Video Content

9 hours

Course Description

The availability of technologies that automate model selection and feature engineering is a new trend in AI. Data Scientists will be able to focus their efforts on fine-tuning models and domain knowledge. This course will help the learner create an automated, end to end pipeline using Watson Studio's AutoAI experiment software. This course also covers the technology used by IBM Research to create the automated process. This course will teach you how to create an automated Python notebook. Learners will be given two use cases.

This course is intended for Data Scientists who have already started to practice. It demonstrates the AI capabilities of IBM Watson Studio and AutoAI, but it doesn't cover Machine Learning nor Data Science concepts. Knowledge about Data Science workflow Data Processing, Feature Engineering Machine Learning Algorithms, Hyperparameter Optimization Evaluation methods of models Python (including Pipeline).

Course Overview

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International Faculty

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Post Course Interactions

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Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Data Preprocessing

Data Science workflow

Evaluation measures for models

Feature Engineering

Hyperparameter Optimization

Machine Learning Algorithms

Python and scikit-learn library (including Pipeline class)

What You Will Learn

Automated data preparation techniques performed by AutoAI

Developing landscape of AutoAI technologies

Evaluate prototypes using the different evaluation metrics calculated by the AutoAI tool

Familiarity with the Watson Studio platform

Sophisticated methods for optimizing Hyperparameter

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