Local instructor-led live AutoML training courses in България.
Oтзиви от потребители
Регулиране на нашите нужди
Sumitomo Mitsui Finance and Leasing Company, Limited
Course: Kubeflow
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Практика
INTELLIGENT HIVES SPÓŁKA Z OGRANICZONĄ ODPOWIEDZIALNOŚCIĄ
Course: Machine Learning – Data science
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и начина, по който
NetWorkS! Sp. z o.o.
Course: AI Awareness for Telecom
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Упражнения
Orange Szkolenia sp. z o.o.
Course: Fundamentals of Artificial Intelligence and Machine Learning
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Компетентност и поведение.
Orange Szkolenia sp. z o.o.
Course: Fundamentals of Artificial Intelligence and Machine Learning
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теми, приятелско отношение на презентатора
Fujitsu Technology Solutions Sp. z o.o.
Course: Artificial Intelligence Overview
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AutoML Subcategories
AutoML Course Outlines
Име на Kурса
Продължителност
Общ преглед
Име на Kурса
Продължителност
Общ преглед
14 hours
This instructor-led, live training in България (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.
7 hours
Google Cloud AutoML е платформа за машинно обучение (ML), която позволява на потребителите да изграждат, обучават и разпространяват персонализирани ML модели, специфични за техните бизнес нужди.
Този инструктор, обучението на живо (онлайн или онлайн) е насочено към учени на данни, аналитици на данни и разработчици, които искат да изследват AutoML продукти и функции, за да създадат и внедрят персонализирани модели за обучение на ML с минимални усилия.
В края на обучението участниците ще могат да:
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Разгледайте линията на продуктите AutoML, за да изпълните различни услуги за различни видове данни.
Подгответе и етикетирайте набор от данни, за да създадете персонализирани ML модели.
Обучение и управление на модели, за да се произвеждат точни и справедливи модели за машинно обучение.
Направете прогнози с помощта на обучени модели, за да отговарят на бизнес целите и нуждите.
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Интерактивна лекция и дискусия.
Много упражнения и упражнения.
Изпълнение на ръката в живо лабораторна среда.
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За да поискате персонализирано обучение за този курс, моля, свържете се с нас, за да организирате.
14 hours
This instructor-led, live training in България (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.
14 hours
This instructor-led, live training in България (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.
14 hours
This instructor-led, live training in България (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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