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. Onsite live AutoML training can be carried out locally on customer premises in Lima or in NobleProg corporate training centers in Lima.
NobleProg -- Your Local Training Provider
Lima - Edificio Tempus San Isidro
Av. Santo Toribio 115, Lima, peru, 27
Edificio Tempus is a Class A brand new office building is strategically located in the corner of Av Victor Belaunde and Av. San Toribio in the heart of San Isidro. There is plenty of parking on 4 parking decks. This center is only a short 300 mt walk in the same block from our exiting Real Seis in located in Centro Empresarial.
Lima - Miguel Grau
Av. Almte. Miguel Grau 709 Lima , Lima, Peru, 15063
Expand your business horizons with a workspace in Barranco, one of Lima's 43 districts, boasting excellent connectivity to its surroundings. Enjoy effortless commuting to your office via the Avenida Almirante Miguel Grau S/N and Bulevar - Embarque Sur 2 bus stops.
This instructor-led, live training in Lima (online or onsite) is aimed at participants with varying levels of expertise who wish to leverage Google's AutoML platform to build customized chatbots for various applications.
By the end of this training, participants will be able to:
Understand the fundamentals of chatbot development.
Navigate the Google Cloud Platform and access AutoML.
Prepare data for training chatbot models.
Train and evaluate custom chatbot models using AutoML.
Deploy and integrate chatbots into various platforms and channels.
Monitor and optimize chatbot performance over time.
This instructor-led, live training in Lima (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 Lima (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 Lima (online or onsite) is aimed at data scientists and data analysts who wish to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities.
By the end of this training, participants will be able to:
Load datasets in DataRobot to analyze, assess, and quality check data.
Build and train models to identify important variables and meet prediction targets.
Interpret models to create valuable insights that are useful in making business decisions.
Monitor and manage models to maintain an optimized prediction performance.
This instructor-led, live training in Lima (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.
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