Online or onsite, instructor-led live AutoML training courses demonstrate through interactive hands-on practice the fundamentals and advanced concepts of AutoML.
AutoML training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Liege onsite live AutoML trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Liège, Place des Guillemins
NobleProg Liège, 2 Place des Guillemins, Liège, Belgium, 4000
Luik is de hoofdstad van de provincie Luik en de economische hoofdstad van Wallonië. Door het aantal inwoners is het de eers...
Luik is de hoofdstad van de provincie Luik en de economische hoofdstad van Wallonië. Door het aantal inwoners is het de eerste Waalse agglomeratie, de derde agglomeratie van België na Brussel en Antwerpen en de vierde gemeente na Antwerpen, Gent en Charleroi. Luik is lange tijd een grote industriestad geweest, maar sinds de jaren zestig heeft het een lange neergang ondergaan, waarbij de fabrieken in verval raken. Luik is ook sterk afhankelijk van transport en multimodaliteit. Luik is een belangrijk economisch centrum met zijn autonome haven, de tweede binnenhaven in West-Europa en zijn luchthaven gespecialiseerd in vracht. In feite blijft de stad de economische hoofdstad van Wallonië.
This instructor-led, live training in Liege (online or onsite) is aimed at technical persons with a background in machine learning who wish to optimize the machine learning models used for detecting complex patterns in big data.
By the end of this training, participants will be able to:
Install and evaluate various open source AutoML tools (H2O AutoML, auto-sklearn, TPOT, TensorFlow, PyTorch, Auto-Keras, TPOT, Auto-WEKA, etc.)
Train high quality machine learning models.
Efficiently solve different types of supervised machine learning problems.
Write just the necessary code to initiate the automated machine learning process.
This instructor-led, live training in Liege (online or onsite) is aimed at data scientists, data analysts, and developers who wish to explore AutoML products and features to create and deploy custom ML training models with minimal effort.
By the end of this training, participants will be able to:
Explore the AutoML product line to implement different services for various data types.
Prepare and label datasets to create custom ML models.
Train and manage models to produce accurate and fair machine learning models.
Make predictions using trained models to meet business objectives and needs.
This instructor-led, live training in Liege (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model.
By the end of this training, participants will be able to:
Automate the process of training highly efficient machine learning models.
Automatically search for the best parameters for deep learning models.
Build highly accurate machine learning models.
Use the power of machine learning to solve real-world business problems.
This instructor-led, live training in Liege (online or onsite) is aimed at machine learning practitioners who wish to use Auto-sklearn to automate the process of selecting and optimizing a machine learning model.
By the end of this training, participants will be able to:
Automate the process of training highly efficient machine learning models.
Build highly accurate machine learning models while bypassing the more tedious tasks of selecting, training and testing different models.
Use the power of machine learning to solve real-world business problems.
This instructor-led, live training in Liege (online or onsite) is aimed at data scientists who wish to use H2O AutoML to automoate the process of building and selecting the best machine learning algorithm and parameters.
By the end of this training, participants will be able to:
Automate the machine learning workflow.
Automatically train and tune many machine learning models within a specified time range.
Train stacked ensembles to arrive at highly predictive ensemble models.
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