Artificial Intelligence (AI)培訓

Artificial Intelligence (AI)培訓

由講師進行實時指導的人工智能本地培訓課程通過動手實踐演示如何實施人工智能解決方案以解決實際問題。

人工智能培訓形式包括“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。

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AI課程大綱

課程名稱
課程時長
概觀
課程名稱
課程時長
概觀
7小時
This instructor-led, live training in 台灣 (online or onsite) is aimed at software engineers or anyone who wish to learn how to use Vertex AI to perform and complete machine learning activities. By the end of this training, participants will be able to:
  • Understand how Vertex AI works and use it as a machine learning platform.
  • Learn about machine learning and NLP concepts.
  • Know how to train and deploy machine learning models using Vertex AI.
7小時
This instructor-led, live training in 台灣 (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies. By the end of this training, participants will be able to:
  • Understand the basic principles of AlphaFold.
  • Learn how AlphaFold works.
  • Learn how to interpret AlphaFold predictions and results.
14小時
This instructor-led, live training in 台灣 (online or onsite) is aimed at data analysts and data scientists who wish to use Weka to perform data mining tasks. By the end of this training, participants will be able to:
  • Install and configure Weka.
  • Understand the Weka environment and workbench.
  • Perform data mining tasks using Weka.
14小時
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
21小時
在這一由講師引導的現場培訓中,參與者將學習Python中最相關及最尖端的機器學習技術,因爲它們構建了一系列涉及圖像、音樂、文本和財務數據的演示應用程序。 在本次培訓結束後,參與者將能夠:
  • 運用用于解決複雜問題的機器學習算法和技術
  • 將深度學習和半監督學習應用于涉及圖像、音樂、文本和財務數據的應用程序
  • 推動Python算法達到其最大潛力
  • 使用例如NumPy和Theano的庫和包
受衆
  • 開發人員
  • 分析師
  • 數據科學家
課程形式
  • 部分講座、部分討論、練習和大量實操
21小時
據估計, 非結構化資料占所有資料的 9 0% 以上, 其中大部分是文本形式的。博客帖子、推特、社交媒體和其他數位出版物不斷增加了這一不斷增長的資料 。 本講師指導的現場課程的核心是從這些資料中提取見解和意義。利用 R 語言和自然語言處理 (NLP) 庫, 我們將電腦科學、人工智慧和計算語言學的概念和技術結合起來, 以演算法方式理解文本資料背後的含義。資料樣本可根據客戶要求提供各種語言版本. 到本培訓結束時, 學員將能夠準備來自不同來源的資料集 (大小), 然後應用正確的演算法分析和報告其意義
課程 的 格式
  • 部分講座、部分討論、繁重的動手實踐、偶爾的測試來衡量理解
28小時
本課程的目的是提供在實踐中應用機器學習方法的一般熟練程度。通過使用 Python 程式設計語言及其各種庫, 並基於大量的實際示例, 本課程教授如何使用機器學習最重要的構建塊, 如何做出資料建模決策, 解釋輸出並驗證結果 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。
28小時
This course introduces linguists or programmers to NLP in Python. During this course we will mostly use nltk.org (Natural Language Tool Kit), but also we will use other libraries relevant and useful for NLP. At the moment we can conduct this course in Python 2.x or Python 3.x. Examples are in English or Mandarin (普通话). Other languages can be also made available if agreed before booking.
35小時
這是為期5天的Data Science和AI入門。 本課程隨附使用Python示例和練習
28小時
This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course. 
21小時
Deep Reinforcement Learning refers to the ability of an "artificial agent" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches. In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent. By the end of this training, participants will be able to:
  • Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning
  • Apply advanced Reinforcement Learning algorithms to solve real-world problems
  • Build a Deep Learning Agent
Audience
  • Developers
  • Data Scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
14小時
This instructor-led, live training in 台灣 (online or onsite) is aimed at data scientists who wish to use IBM Cloud Pak to prepare data for use in AI solutions. By the end of this training, participants will be able to:
  • Install and configure Cloud Pak for Data.
  • Unify the collection, organization and analysis of data.
  • Integrate Cloud Pak for Data with a variety of services to solve common business problems.
  • Implement workflows for collaborating with team members on the development of an AI solution.
28小時
In this instructor-led, live training in 台灣, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to:
  • Understand the fundamental concepts of deep learning.
  • Learn the applications and uses of deep learning in telecom.
  • Use Python, Keras, and TensorFlow to create deep learning models for telecom.
  • Build their own deep learning customer churn prediction model using Python.
14小時
嵌入式投影儀是一款開源Web應用程序,用于可視化用于訓練機器學習系統的數據。由Google創建,它是TensorFlow的一部分。 這個有指導意義的現場培訓介紹了嵌入式投影儀背後的概念,並讓參與者通過演示項目的設置。 在培訓結束後,參與者將能夠: 探索機器學習模型如何解釋數據浏覽數據的3D和2D視圖以了解機器學習算法如何解釋它理解嵌入背後的概念及其在表示圖像,單詞和數字的數學向量中的作用。 探索特定嵌入的屬性以了解模型的行爲將嵌入項目應用于真實世界的用例,例如爲音樂愛好者建立歌曲推薦系統 聽衆 開發商數據科學家 課程的格式 部分講座,部分討論,練習和沈重的練習
7小時
This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.
21小時
本課程採用實用的方法教授OptaPlanner 。它為參與者提供了執行此工具基本功能所需的工具。
28小時
這個為期四天的課程旨在教授遺傳算法的工作原理;它還包括如何選擇遺傳算法的模型參數;在本課程中遺傳算法有很多應用,遺傳算法可以解決優化問題。
7小時
這是一個以課堂為基礎的培訓課程,採用演示和問答形式
14小時
智能過程自動化(IPA)是指使用Artificial Intelligence (AI) ,機器人技術以及與第三方服務的集成來擴展RPA的功能。 這種由講師指導的實時培訓(現場或遠程)針對希望建立或擴展具有更智能功能的RPA系統的技術人員。 在培訓結束時,參與者將能夠:
  • 安裝和配置UiPath IPA。
  • 使機器人可以管理其他機器人。
  • 應用計算機視覺來精確定位屏幕對象。
  • 使機器人能夠檢測語言模式並對非結構化內容進行情感分析。
課程形式
  • 互動式講座和討論。
  • 很多練習和練習。
  • 在現場實驗室環境中動手實施。
課程自定義選項
  • 要請求此課程的定制培訓,請與我們聯繫以安排。
  • 要了解有關UiPath IPA的更多信息,請訪問:https:// www。 UiPath .com / rpa / intelligent-process-automation
14小時
軟體測試是評估軟體應用程式功能有效性的過程。將人工智慧集成到軟體測試環境中,使該過程能夠以 AI 為驅動,從而加快測試的創作、執行和維護。 此講師指導的現場培訓(現場或遠端)面向希望擁有 AI 驅動的軟體測試環境的軟體測試人員。 培訓結束時,學員將能夠:
  • 使用 AI 自動生成和參數化單元測試。
  • 在真實用例中應用機器學習學習。
  • 使用 AI 自動生成和維護 API 測試。
  • 使用機器學習方法自我修復Selenium測試的執行。
課程格式
  • 互動講座和討論。
  • 大量的練習和練習。
  • 在即時實驗室環境中實際實現。
課程自訂選項
  • 如需申請本課程的定制培訓,請聯繫我們安排。
7小時
This instructor-led, live training in 台灣 (online or onsite) is aimed at marketers who wish to use AI to improve improve digital marketing strategies through valuable customer insights. By the end of this training, participants will be able to:
  • Leverage AI software to improve the way brands connect to users.
  • Use chatbots to optimize the user-experience.
  • Increase productivity and revenue through the automation of tasks.
21小時
This instructor-led, live training in 台灣 (online or onsite) is aimed at engineers who wish to program and create robots through basic AI methods. By the end of this training, participants will be able to:
  • Implement filters (Kalman and particle) to enable the robot to locate moving objects in its environment.
  • Implement search algorithms and motion planning.
  • Implement PID controls to regulate a robot's movement within an environment.
  • Implement SLAM algorithms to enable a robot to map out an unknown environment.
7小時
This instructor-led, live training in 台灣 (online or onsite) is aimed at managers and business leaders who wish to learn about the fundamentals of artificial intelligence and manage AI projects for their organization. By the end of this training, participants will be able to understand AI at a technical level and strategize using their organization’s data and resources to successfully manage AI projects.
80小時
In this instructor-led, live training in 台灣 (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems. The 4-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge. The target hardware for this course will be simulated in 3D through simulation software. The code will then be loaded onto physical hardware (Arduino or other) for final deployment testing. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots. By the end of this training, participants will be able to:
  • Understand the key concepts used in robotic technologies.
  • Understand and manage the interaction between software and hardware in a robotic system.
  • Understand and implement the software components that underpin robotics.
  • Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
  • Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
  • Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
  • Implement search algorithms and motion planning.
  • Implement PID controls to regulate a robot's movement within an environment.
  • Implement SLAM algorithms to enable a robot to map out an unknown environment.
  • Test and troubleshoot a robot in realistic scenarios.
120小時
In this instructor-led, live training in 台灣 (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems. The 6-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge. The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots. By the end of this training, participants will be able to:
  • Understand the key concepts used in robotic technologies.
  • Understand and manage the interaction between software and hardware in a robotic system.
  • Understand and implement the software components that underpin robotics.
  • Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
  • Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
  • Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
  • Implement search algorithms and motion planning.
  • Implement PID controls to regulate a robot's movement within an environment.
  • Implement SLAM algorithms to enable a robot to map out an unknown environment.
  • Extend a robot's ability to perform complex tasks through Deep Learning.
  • Test and troubleshoot a robot in realistic scenarios.
7小時
該培訓針對的是那些想要學習神經網絡及其應用基礎知識的人。
14小時
本課程介紹了使用R-project軟件將神經網絡應用於現實世界的問題。
14小時
本培訓課程面向希望在實際應用中應用Machine Learning人員。 聽眾本課程適用於對統計學有一定了解並且知道如何編寫R(或Python或其他選定語言)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踐方面。 目的是為有興趣在工作中應用這些方法的參與者提供Machine Learning實際應用。 行業特定示例用於使培訓與受眾相關。
21小時
人工神經網絡是一種計算數據模型,用於開發能夠執行“智能”任務的Artificial Intelligence (AI)系統。 Neural Networks通常用於Machine Learning (ML)應用程序,它們本身就是AI的一種實現。 Deep Learning是ML的一個子集。
21小時
人工神經網絡是一種計算數據模型,用於開發能夠執行“智能”任務的Artificial Intelligence (AI)系統。 Neural Networks通常用於Machine Learning (ML)應用程序,它們本身就是AI的一種實現。 Deep Learning是ML的一個子集。

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