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Machine Learning Engineer Nanodegree program from Udacity
Contributor II
Please read this overview. There is so much stuff here which I've seen available in RapidMiner Studio. Okay, so I'd have to learn Python. Even there RapidMiner can help. Helpful suggestions are appreciated.
Overview This goal of the Machine Learning Engineer Nanodegree program is to help students learn the key skills they need to perform well as a machine learning engineer. A graduate of this program will be able to: • Test Python code and build a Python package of their own. • Build predictive models using a variety of unsupervised and supervised machine learning techniques. • Use Amazon SageMaker to deploy machine learning models to production environments, such as a web application or piece of hardware.. • A/B test two different deployed models and evaluate their performance. • Utilize an API to deploy a model to a website such that it responds to user input, dynamically. • Update a deployed model, in response to changes in the underlying data source. This program is comprised of 4 courses and 4 projects. Each project you build will be an opportunity to demonstrate what you’ve learned in the lessons. Your completed projects will become part of a career portfolio that will demonstrate to potential employers that you have skills in feature engineering, building machine learning algorithms, and model deployment.
Overview This goal of the Machine Learning Engineer Nanodegree program is to help students learn the key skills they need to perform well as a machine learning engineer. A graduate of this program will be able to: • Test Python code and build a Python package of their own. • Build predictive models using a variety of unsupervised and supervised machine learning techniques. • Use Amazon SageMaker to deploy machine learning models to production environments, such as a web application or piece of hardware.. • A/B test two different deployed models and evaluate their performance. • Utilize an API to deploy a model to a website such that it responds to user input, dynamically. • Update a deployed model, in response to changes in the underlying data source. This program is comprised of 4 courses and 4 projects. Each project you build will be an opportunity to demonstrate what you’ve learned in the lessons. Your completed projects will become part of a career portfolio that will demonstrate to potential employers that you have skills in feature engineering, building machine learning algorithms, and model deployment.
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