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. Santiago onsite live AutoML trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
The Center occupies the fifth and ninth floors of a modern office block with an attractive, spacious reception area. It has i...
The Center occupies the fifth and ninth floors of a modern office block with an attractive, spacious reception area. It has its own secure underground parking and is protected by 24-hour CCTV. It is just a few minutes' walk from an underground railway station. Arturo Meriono Benitez International Airport is 26km away.
The Santiago Vitacura Center is located on the 15th floor of one of the newest high-rise buildings in the city, situated near...
The Santiago Vitacura Center is located on the 15th floor of one of the newest high-rise buildings in the city, situated near El Bosque, an attractive business and residential district, and adjacent to the Santiago World Trade Center. The Center has its own on-site coffee bar and secure underground parking. There are good public transport connections, and the international airport is 40 minutes away.
This instructor-led, live training in Santiago (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 Santiago (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 Santiago (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 Santiago (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 Santiago (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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