
由講師進行實時指導的機器學習本地培訓課程通過動手實踐演示如何應用機器學習技術和工具來解決各行業的現實問題。NobleProg機器學習課程涵蓋不同的編程語言和框架,包括Python、R語言、Matlab。機器學習課程適用于多種行業應用,包括金融、銀行、保險,涵蓋機器學習的基礎知識以及深度學習等更高級的方法。
機器學習培訓形式包括“現場實時培訓”和“遠程實時培訓”。現場實時培訓可在客戶位于台灣的所在場所或NobleProg位于台灣的企業培訓中心進行,遠程實時培訓可通過交互式遠程桌面進行。
NobleProg -- 您的本地培訓提供商
客戶評論
它非常互動,比預期更輕鬆和非正式。我們在當時涵蓋了很多主題,培訓師總是樂於接受更詳細的討論,或者更廣泛地討論主題及其相關方式。我覺得培訓給了我繼續學習的工具,相反,它是一次性會議,一旦你完成學習就會停止,這對於主題的規模和復雜性非常重要。
Jonathan Blease
課程: Artificial Neural Networks, Machine Learning, Deep Thinking
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培訓師知識淵博,包括我感興趣的領域。
Mohamed Salama
課程: Data Mining & Machine Learning with R
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這個話題非常有趣。
Wojciech Baranowski
課程: Introduction to Deep Learning
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培訓師的理論知識和培訓後與參與者解決問題的意願。
Grzegorz Mianowski
課程: Introduction to Deep Learning
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話題。很有意思!。
Piotr
課程: Introduction to Deep Learning
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每個主題後的練習都非常有用,儘管最後太複雜了。一般來說,所提供的材料非常有趣並涉及!圖像識別練習很棒。
Dolby Poland Sp. z o.o.
課程: Introduction to Deep Learning
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我認為,如果培訓是在波蘭語中完成的,那麼培訓師就可以更有效地分享他的知識。
Radek
課程: Introduction to Deep Learning
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深度學習的全球概述。
Bruno Charbonnier
課程: Advanced Deep Learning
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這些練習非常實用,不需要Python的高級知識。
Alexandre GIRARD
課程: Advanced Deep Learning
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使用Eras對實例進行練習。意大利完全理解我們對此培訓的期望。
Paul Kassis
課程: Advanced Deep Learning
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我真的很感激克里斯對我們問題的明確答案。
Léo Dubus
課程: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
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我一般都很喜歡知識淵博的教練。
Sridhar Voorakkara
課程: Neural Networks Fundamentals using TensorFlow as Example
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我對這門課程的標準感到驚訝 - 我會說它是大學標準。
David Relihan
課程: Neural Networks Fundamentals using TensorFlow as Example
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非常好的全面概述。 Go OD背景到原因Tensorflow工作,因為它確實。
Kieran Conboy
課程: Neural Networks Fundamentals using TensorFlow as Example
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我喜歡有機會提出問題並對理論進行更深入的解釋。
Sharon Ruane
課程: Neural Networks Fundamentals using TensorFlow as Example
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我們對這個主題有了更多的了解。我們公司內部的一些真實主題進行了一些很好的討論。
Sebastiaan Holman
課程: Machine Learning and Deep Learning
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通過展示理論與實踐如何相輔相成,培訓提供了正確的基礎,使我們能夠進一步擴展。它實際上讓我對這個主題比以前更感興趣。
Jean-Paul van Tillo
課程: Machine Learning and Deep Learning
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我非常喜歡主題的報導和深度。
Anirban Basu
課程: Machine Learning and Deep Learning
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培訓師很容易解釋困難和高級話題。
Leszek K
課程: Artificial Intelligence Overview
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關於該主題的培訓師的深刻知識。
Sebastian Görg
課程: Introduction to Deep Learning
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非常更新的方法或CPI(張量流,時代,學習)做機器學習。
Paul Lee
課程: TensorFlow for Image Recognition
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非常靈活。
Frank Ueltzhöffer
課程: Artificial Neural Networks, Machine Learning and Deep Thinking
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我一般都很喜歡靈活性。
Werner Philipp
課程: Artificial Neural Networks, Machine Learning and Deep Thinking
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鑑於技術前景:未來哪種技術/流程可能變得更加重要;看,這項技術可以用於什麼。
Commerzbank AG
課程: Neural Networks Fundamentals using TensorFlow as Example
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我從主題選擇中受益。訓練風格。練習方向。
Commerzbank AG
課程: Neural Networks Fundamentals using TensorFlow as Example
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都很喜歡
蒙 李
課程: Machine Learning Fundamentals with Python
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教練的指導和舉例
ORANGE POLSKA S.A.
課程: Machine Learning and Deep Learning
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可以自己討論提議的問題。
ORANGE POLSKA S.A.
課程: Machine Learning and Deep Learning
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與講師的交流環節
文欣 张
課程: Artificial Neural Networks, Machine Learning, Deep Thinking
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都喜歡
lisa xie
課程: Artificial Neural Networks, Machine Learning, Deep Thinking
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深入報導機器學習主題,特別是神經網絡。揭開了很多話題的神秘面紗。
Sacha Nandlall
課程: Python for Advanced Machine Learning
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我真的很喜歡練習
L M ERICSSON LIMITED
課程: Machine Learning
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實驗練習
Marcell Lorant - L M ERICSSON LIMITED
課程: Machine Learning
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Jupyter筆記本表格,其中提供培訓材料
L M ERICSSON LIMITED
課程: Machine Learning
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有很多練習和有趣的話題。
L M ERICSSON LIMITED
課程: Machine Learning
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一些偉大的實驗室練習由教練深入分析和解釋(例如線性回歸中的協變量,匹配實際功能)
L M ERICSSON LIMITED
課程: Machine Learning
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包括練習在內的所有材料都在同一頁面上然後它會立即更新。最終揭示了解決方案。涼!此外,我很欣賞Krzysztof花了很多精力去理解我們的問題,並向我們提出了可行的技巧。
Attila Nagy - L M ERICSSON LIMITED
課程: Machine Learning
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領先和實際應用示例的大量和最新知識。
ING Bank Śląski S.A.
課程: Introduction to Deep Learning
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很多練習,與團隊很好的合作。
Janusz Chrobot - ING Bank Śląski S.A.
課程: Introduction to Deep Learning
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關於colaborators的工作,
ING Bank Śląski S.A.
課程: Introduction to Deep Learning
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很明顯,所提出主題的愛好者都在領先。運動時使用了有趣的例子。
ING Bank Śląski S.A.
課程: Introduction to Deep Learning
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涵蓋廣泛的主題和領導者的實質性知識。
ING Bank Śląski S.A.; Kamil Kurek Programowanie
課程: Understanding Deep Neural Networks
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缺乏
ING Bank Śląski S.A.; Kamil Kurek Programowanie
課程: Understanding Deep Neural Networks
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講師的大量理論和實踐知識。培訓師的溝通能力。在課程中,您可以提出問題並獲得滿意的答案。
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
課程: Understanding Deep Neural Networks
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實用部分,我們實現了算法。這樣可以更好地理解該主題。
ING Bank Śląski S.A.; Kamil Kurek Programowanie
課程: Understanding Deep Neural Networks
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練習和實施的例子
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
課程: Understanding Deep Neural Networks
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討論的例子和問題。
ING Bank Śląski S.A.; Kamil Kurek Programowanie
課程: Understanding Deep Neural Networks
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實質性知識,承諾,熱情的知識轉移方式。理論講座後的實例。
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
課程: Understanding Deep Neural Networks
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Maciej先生準備的實踐練習
ING Bank Śląski S.A.; Kamil Kurek Programowanie
課程: Understanding Deep Neural Networks
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我受益於教學的熱情,並專注於使事情變得合情合理。
Zaher Sharifi - GOSI
課程: Advanced Deep Learning
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Machine Learning (ML)子類別
機器學習課程大綱
我們的目標是讓您能夠自信地理解和使用機器學習工具箱中最基本的工具, 並避免資料科學應用的常見陷阱。
在本課程中,我們將介紹神經網絡的原理,並使用OpenNN來實現示例應用程序。
聽眾
希望創建深度學習應用程序的軟件開發人員和程序員。
課程形式
講座和討論以及動手練習。
在本次培訓結束時,參與者將擁有實施OpenNMT實時解決方案所需的知識和實踐。
源和目標語言樣本將根據受眾的要求進行預先安排。
課程格式
- 部分講座,部分討論,重點實踐練習
This instructor-led, live training (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.
By the end of this training, participants will be able to:
- View, load, and classify images and videos using OpenCV 4.
- Implement deep learning in OpenCV 4 with TensorFlow and Keras.
- Run deep learning models and generate impactful reports from images and videos.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
這種訓練更注重基本面,但會幫助你選擇合適的技術: TensorFlow , Caffe ,泰亞諾,DeepDrive, Keras ,等等這些例子中所作TensorFlow 。
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Apache MXNet to build and deploy a deep learning model for image recognition.
By the end of this training, participants will be able to:
- Install and configure Apache MXNet and its components.
- Understand MXNet's architecture and data structures.
- Use Apache MXNet's low-level and high-level APIs to efficiently build neural networks.
- Build a convolutional neural network for image classification.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
它概述了模式識別背景下的現有方法,動機和主要思想。
在簡短的理論背景之後,參與者將使用開源(通常為R)或任何其他流行軟件進行簡單的練習。
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Azure Machine Learning and Azure DevOps to facilitate MLOps practices.
By the end of this training, participants will be able to:
- Build reproducible workflows and machine learning models.
- Manage the machine learning lifecycle.
- Track and report model version history, assets, and more.
- Deploy production ready machine learning models anywhere.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
聽眾
本課程面向希望為任何機器學習任務創建預測引擎的開發人員和數據科學家。
This instructor-led, live training (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.
By the end of this training, participants will be able to:
- Install and configure various MLOps frameworks and tools.
- Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
- Prepare, validate and version data for use by ML models.
- Understand the components of an ML Pipeline and the tools needed to build one.
- Experiment with different machine learning frameworks and servers for deploying to production.
- Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
聽眾
熟悉機器學習並熟悉如何編程的數據科學家和統計學家。本課程的重點是數據/模型準備,執行,事後分析和可視化的實際方面。目的是向有興趣在工作中應用這些方法的參與者提供機器學習的實用介紹
行業特定示例用於使培訓與受眾相關。
我們的目標是讓您自信地理解和使用Machine Learning工具箱中最基本的工具,並避免Data Science應用程序的常見缺陷。
我們的目標是讓您自信地理解和使用Machine Learning工具箱中最基本的工具,並避免Data Science應用程序的常見缺陷。
我們的目標是讓您自信地理解和使用Machine Learning工具箱中最基本的工具,並避免Data Science應用程序的常見缺陷。
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to go beyond building ML models and optimize the ML model creation, tracking, and deployment process.
By the end of this training, participants will be able to:
- Install and configure MLflow and related ML libraries and frameworks.
- Appreciate the importance of trackability, reproducability and deployability of an ML model
- Deploy ML models to different public clouds, platforms, or on-premise servers.
- Scale the ML deployment process to accommodate multiple users collaborating on a project.
- Set up a central registry to experiment with, reproduce, and deploy ML models.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
在這個以講師為主導的現場培訓中,參與者將學習如何應用機器學習技術和工具來解決金融行業中的現實問題。 R將用作編程語言。
參與者首先學習關鍵原則,然後通過構建自己的機器學習模型並將其用於完成一些團隊項目,將他們的知識付諸實踐。
在培訓結束時,參與者將能夠:
- 理解機器學習的基本概念
- 了解金融機器學習的應用和用途
- 使用R的機器學習開發自己的算法交易策略
聽眾
- 開發商
- 數據科學家
課程形式
- 部分講座,部分討論,練習和繁重的實踐練習
在這一由講師引導的現場培訓中,參與者將學習如何應用機器學習技術和工具來解決財務的現實問題。Python將被用作編程語言。
參與者首先學習關鍵原則,然後通過建立自己的機器學習模型並使用模型來完成一些團隊項目以將所學知識運用到實踐中。
在本次培訓結束後,參與者將能夠:
- 了解機器學習的基本概念
- 了解機器學習在金融領域的應用和使用
- 使用Python機器學習開發自己的算法交易策略
受衆
- 開發人員
- 數據科學家
課程形式
- 部分講座、部分討論、練習和大量實操
目標觀眾
- 投資者和人工智能企業家
- 管理者和工程師,他們的公司正在進入AI領域
- Business分析師和投資者
This instructor-led, live training (online or onsite) is aimed at data scientists and developers who wish to use ML.NET machine learning models to automatically derive projections from executed data analysis for enterprise applications.
By the end of this training, participants will be able to:
- Install ML.NET and integrate it into the application development environment.
- Understand the machine learning principles behind ML.NET tools and algorithms.
- Build and train machine learning models to perform predictions with the provided data smartly.
- Evaluate the performance of a machine learning model using the ML.NET metrics.
- Optimize the accuracy of the existing machine learning models based on the ML.NET framework.
- Apply the machine learning concepts of ML.NET to other data science applications.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
課程格式
- 本課程介紹了模式匹配領域中使用的方法,技術和算法,因為它適用於Machine Vision 。