Rzeszów

RISE, Plac Wolności 13, Rzeszów, Poland, 35-073

The training room is located in the very heart of Rzeszow, making it easily accessible for participants. In the immediate vicinity, there are major public transportation hubs, such as city buses (MPK), railways (PKP), and long-distance buses (PKS), facilitating access from various parts of the city and beyond. Additionally, there is an underground parking garage at the Center Park gallery nearby, providing convenient parking for those using their own vehicles.

Głogów Małopolski

NobleProg , Strefowa 1, Głogów Małopolski, Poland, 36-060

The training room is located just 10 km southwest of Rzeszow, directly on the Rzeszow-Radom route, providing easy access from both cities. Additionally, its proximity to the A4 motorway and Jasionka airport facilitates transportation for both car travelers and those utilizing air transport.

Subcategories (2)

Auto-Keras

Auto-Keras
DataRobot

DataRobot

Explore Our Courses

Creating Custom Chatbots with Google AutoML

 14 Hours
This instructor-led, live training in podkarpackie (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.
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AutoML with Auto-Keras

 14 Hours
This instructor-led, live training in podkarpackie (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.
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AutoML

 14 Hours
This instructor-led, live training in podkarpackie (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.
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DataRobot

 7 Hours
This instructor-led, live training in podkarpackie (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.
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Google Cloud AutoML

 7 Hours
This instructor-led, live training in podkarpackie (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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