대한민국에서 현지 강사가 진행하는 AutoML 교육 과정.
회원 평가
그것은 매우 상호 작용적이고 예상보다 편안하고 비공식적이었다. 우리는 당시에 많은 주제를 다루었으며 트레이너는 주제에 대해 더 자세히 또는 더 많이 이야기하고 그들이 어떻게 관련되어 있는지에 대해 항상 수용적이었습니다. 나는 훈련이 나에게 당신이 주제의 규모와 복잡성을 감안할 때 매우 중요하다고 생각하면 학습이 끝난 후에 중단되는 것과는 달리 학습을 계속할 수있는 도구를 제공한다고 생각합니다.
Jonathan Blease
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
트레이너는 매우 지식이 많았고 내가 관심이있는 영역을 포함했습니다.
Mohamed Salama
Course: Data Mining & Machine Learning with R
Machine Translated
나는 진정으로 운동을 좋아했다
L M ERICSSON LIMITED
Course: Machine Learning
Machine Translated
실험실 연습
Marcell Lorant - L M ERICSSON LIMITED
Course: Machine Learning
Machine Translated
교육 자료가있는 주피터 (Jupyter) 노트북 양식
L M ERICSSON LIMITED
Course: Machine Learning
Machine Translated
많은 연습 문제와 흥미로운 주제가있었습니다.
L M ERICSSON LIMITED
Course: Machine Learning
Machine Translated
트레이너가 깊이 분석 한 몇 가지 훌륭한 실험실 연습 (예 : 선형 회귀의 공변량, 실제 함수와 일치)
L M ERICSSON LIMITED
Course: Machine Learning
Machine Translated
연습을 포함한 모든 자료가 동일한 페이지에 있고 그 다음 즉시 업데이트됩니다. 해결책은 결국 밝혀졌습니다. 시원한! 또한 크 르지 스 토프 (Krzysztof)가 우리의 문제를 이해하기 위해 많은 노력을 기울 였고 가능한 기술을 제안했음을 감사드립니다.
Attila Nagy - L M ERICSSON LIMITED
Course: Machine Learning
Machine Translated
사전 준비된 스크립트로 많은 방법을 보여줍니다. 매우 정교하게 준비된 자료 및 역 추적하기 쉽습니다.
Kamila Begej - GE Medical Systems Polska Sp. Zoo
Course: Machine Learning – Data science
Machine Translated
그 훈련은 예제와 코딩에 초점을 맞추 었습니다. 3 일간의 교육에 많은 콘텐츠를 담을 수는 없다고 생각했지만 잘못되었습니다. 교육은 많은 주제를 다루었으며 모든 것이 매우 세부적인 방식으로 수행되었습니다 (특히 모델 매개 변수 튜닝 - 시간이있을 것으로 기대하지 않았고 매우 놀랐습니다).
Bartosz Rosiek - GE Medical Systems Polska Sp. Zoo
Course: Machine Learning – Data science
Machine Translated
다른 텍스트 요약 방법의 사용법에 더 초점을 맞추고 싶습니다.
Course: Text Summarization with Python
Machine Translated
강사는 주제 분야의 전문가였으며 응용 이론이 우수한 관련 이론
Fahad Malalla - Tatweer Petroleum
Course: Applied AI from Scratch in Python
Machine Translated
Rock Solid Knowledge Ltd
Course: Machine Learning – Data science
Machine Translated
Richard Blewett - Rock Solid Knowledge Ltd
Course: Machine Learning – Data science
Machine Translated
Rock Solid Knowledge Ltd
Course: Machine Learning – Data science
Machine Translated
우리의 필요에 조정하기
Sumitomo Mitsui Finance and Leasing Company, Limited
Course: Kubeflow
Machine Translated
Francesco Ferrara - Inpeco SpA
Course: Introduction to Machine Learning
Machine Translated
Siphelo Mapolisa - University Of South Africa
Course: Data Science: Analysis and Presentation
Machine Translated
Molatelo Tloubatla - University Of South Africa
Course: Data Science: Analysis and Presentation
Machine Translated
Benedikt Chiandetti - HDI Deutschland Bancassurance Kundenservice GmbH
Course: Machine Learning Concepts for Entrepreneurs and Managers
Machine Translated
트레이너는 어렵고 진보 된 주제를 쉽게 설명했습니다.
Leszek K
Course: Artificial Intelligence Overview
Machine Translated
모두 좋아해. 李 蒙 -
蒙 李
Course: Machine Learning Fundamentals with Python
Machine Translated
강사와의 커뮤니케이션 张 文欣 - Accenture
文欣 张
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
좋아해. li
lisa xie
Course: Artificial Neural Networks, Machine Learning, Deep Thinking
Machine Translated
교대 이론 / 연습 효과적!
CIRAD
Course: Introduction au Machine Learning avec MATLAB
Machine Translated
프로그레시브 프리젠 테이션 및 방법 적용
Aurélien Briffaz - CIRAD
Course: Introduction au Machine Learning avec MATLAB
Machine Translated
가용성 및 적응성, 질문에 대한 답변
Jean-Michel MEOT - CIRAD
Course: Introduction au Machine Learning avec MATLAB
Machine Translated
토론 된 문제, 수행 된 운동 (예), 훈련 분위기, 트레이너와의 접촉, 위치.
Wojskowe Zakłady Uzbrojenia S.A. w Grudziądzu
Course: Octave nie tylko dla programistów
Machine Translated
다른 텍스트 요약 방법의 사용법에 더 초점을 맞추고 싶습니다.
Course: Text Summarization with Python
Machine Translated
AutoML서브 카테고리
AutoML코스 개요
코스 이름
Duration
Overview
코스 이름
Duration
Overview
14 시간
Overview
This instructor-led, live training in 대한민국 (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.
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 시간
Overview
This instructor-led, live training in 대한민국 (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.
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.
14 시간
Overview
This instructor-led, live training in 대한민국 (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.
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 시간
Overview
This instructor-led, live training in 대한민국 (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.
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 시간
Overview
This instructor-led, live training in 대한민국 (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.
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.
Last Updated:
향후AutoML 코스
13-12-2021 10:00
14 hours
14-12-2021 10:00
14 hours
21-12-2021 10:00
14 hours
30-12-2021 10:00
14 hours
27-01-2022 10:00
7 hours
Other countries
Consulting
주말AutoML코스, 밤의AutoML트레이닝, AutoML부트 캠프, AutoML 강사가 가르치는, 주말AutoML교육, 밤의AutoML과정, AutoML코칭, AutoML강사, AutoML트레이너, AutoML교육 과정, AutoML클래스, AutoML현장, AutoML개인 강좌, AutoML1 대 1 교육
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