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.
Machine Learning Rapid Prototyping with IBM Watson Studio
Machine Learning Rapid Prototyping with IBM Watson Studio
Instructors: Mark J Grover
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4.2
(15 reviews)
Intermediate level
Some related experience required
8 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
- Feature Engineering
- MLOps (Machine Learning Operations)
- Automation
- Exploratory Data Analysis
- Data Processing
- Performance Tuning
- Data Transformation
- Artificial Intelligence and Machine Learning (AI/ML)
- Data Cleansing
- Applied Machine Learning
- Predictive Modeling
- Machine Learning
- Scikit Learn (Machine Learning Library)
- Machine Learning Methods
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Assessments
13 assignments
Taught in English
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There are 4 modules in this course
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
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
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
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
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5
Reviewed on Sep 13, 2020
Very much informative and useful with hands on excercise
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