由讲师进行实时指导的Python本地培训课程通过动手实践向学员演示Python编程语言的各个方面。本课程涉及的主题包括Python编程基础、高级Python编程、Python用于测试自动化、Python脚本和自动化，以及Python用于金融、银行、保险等领域的数据分析和大数据应用。
NobleProg Python培训课程还涵盖了用于机器学习和深度学习的Python库和框架的初级和高级课程。
Python培训形式包括“现场实时培训”和“远程实时培训”。现场实时培训可在客户位于中国的所在场所或NobleProg位于中国的企业培训中心进行，远程实时培训可通过交互式远程桌面进行。
NobleProg -- 您的本地培训提供商
客户评论
都很喜欢
蒙 李
课程: Machine Learning Fundamentals with Python
我喜欢练习和学习Python的角落和缝隙
Connor Brierley-Green
课程: Python Programming
Machine Translated
乔伊对编程充满热情。他非常擅长动态适应我们的需求和兴趣。
Randy Enkin
课程: Python Programming
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许多例子让我很容易理解。
Lingmin Cao
课程: Python Programming
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对我们所有问题的指导的全面知识给我的期望提供了答案......讲师进行了很好的讨论......他并不缺乏耐心......
Łukasz Matulewicz
课程: Programowanie w języku Python
Machine Translated
对讲师的丰富知识，工具的多样性以及对该主题的实用方法。
Magdalena Stupak
课程: Programowanie w języku Python
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对培训师很了解，如何翻译。
Renata Cylejowska
课程: Programowanie w języku Python
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认真对待定制的事实。
jurgen linsen
课程: Python Programming
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因为我是唯一的参与者，培训可以适应我的需求。
Kevin THIERRY
课程: Web Development with Web2Py
Machine Translated
我确实喜欢这些练习。
Office for National Statistics
课程: Natural Language Processing with Python
Machine Translated
我喜欢乐于助人且非常善良。
Natalia Machrowicz
课程: Python Programming
Machine Translated
我们做了实践练习（我们编写的脚本可以用于我们的日常工作）。它使课程非常有趣。我也喜欢教练分享他的知识的方式。他以一种非常容易接近的方式做到了。
Malwina Sawa
课程: Python Programming
Machine Translated
记住/重复关键主题的非常好的方法。非常好的“热身”练习。
课程: Python Programming
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*愉快的练习。 *快速进入更高级的主题。 *培训师很友好，很容易上手。 *根据团队需求定制课程。
Matthew Lucas
课程: Python Programming
Machine Translated
我喜欢在课程/课程中添加特定主题的灵活性。
Marc Ammann
课程: Python Programming
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培训师分别查看并帮助每个人。
Szymon Wolny
课程: Programowanie w języku Python
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一组理想的练习。轻松练习和“带星星”
Motorola Solutions Systems Polska Sp. z o.o
课程: Programowanie w języku Python
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良好的平衡理论/练习，将讲课级别调整为听众越来越少，更有经验，这对于使用Jupiter Notebook并在实践中展示理论非常重要。我还喜欢在第二天的培训的第一部分收集匿名反馈，一切都按照我们的建议准备好了，即使它已经非常好，后来它甚至更好:)。
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课程: Programowanie w języku Python
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老师的承诺，准备，倾听者的态度，愿意解释所有含糊之处。
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课程: Programowanie w języku Python
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领导接近每个人，即使他没有求助并检查练习的水平。
Agnieszka Bielak
课程: Programowanie w języku Python
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培训师提出了一个关于某个问题的非常简短的理论，我们立即去练习。一个很好的挂牌方式，给教练信息他需要花费多少时间才能完成某项任务，还有谁还有解决方案问题。
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课程: Programowanie w języku Python
Machine Translated
即使有人没有提出要求，但显然他不会继续完成任务，但Kr ys tof出现并能够提供专家建议。
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课程: Programowanie w języku Python
Machine Translated
总之，所有人都喜欢进行练习的方式，我很高兴我来到这样的教练。
Maksym Kolodiy
课程: Programowanie w języku Python
Machine Translated
练习的真实例子
Motorola Solutions Systems Polska Sp. z o.o
课程: Programowanie w języku Python
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培训材料（Jupyter）的可用性，不断更新笔记本，这取决于课程中出现的问题。消除疑虑，回答任何问题..
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课程: Programowanie w języku Python
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交付教材的方便性和有趣的方式。
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课程: Programowanie w języku Python
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处理xlsx和csv文件
Łukasz Olczyk
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深入报道机器学习主题，特别是神经网络。揭开了很多话题的神秘面纱。
Sacha Nandlall
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我喜欢定制的内部文件处理和数据分析。
Glycom A/S
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案例研究帮助我们了解了如何在行业中应用Python 。在练习期间非常感谢教练的帮助
Rajiv Dhingra - TCS
课程: Python Programming
Machine Translated
由于我们是PHP开发人员，他了解情况并允许我们在两者之间慢慢映射。我喜欢他添加的例子和幽默。
Soumya Tyagi - TCS
课程: Python Programming
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我很高兴我们使用自己的数据作为例子。
Glycom A/S
课程: Data Analysis in Python using Pandas and Numpy
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一切。
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课程: Programowanie w języku Python
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有趣的问题。
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各种准备的问题和例子
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课程: Programowanie w języku Python
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培训不是演示风格。我们和教练一起编码..
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我最喜欢这一切。
Thukten Dendup - Bhutan Telecom
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它是一种全新的体验，一种新的框架，并期待使用课堂上学到的知识做一些事情。
Jigme - Bhutan Telecom
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我真的很喜欢很多实验室和实践。
Vivian Feng - Destination Canada
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练习/实验室是根据我们自己的组织需求量身定制的。
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我一般都喜欢这个主题。
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培训师正在分享真正的文字体验，向真正的专业人士学习是很好的。
Fednot
课程: Python Programming
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培训师非常出色，他随时准备回答我的问题并尽可能多地分享知识。
Fahad Malalla - Tatweer Petroleum
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1：1非常密集，但学到了很多东西。
Karen Dyke - BT
课程: Python: Automate the Boring Stuff
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强调编码“在投影机上”的例子肯定是汤姆的+。
ADVA OPTICAL NETWORKING SP. ZO O.
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我最喜欢这个话题。
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课程: Python Programming
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演习的组织方式: 都是按照自己的节奏进行的, 安东尼奥在那里帮助你进一步。
Proximus
课程: Python Programming
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我喜欢足够和非常详细的阅读材料和例子 (幻灯片)。
HC Consumer Finance Philippines, Inc.
课程: Python Programming
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我真的很喜欢那娜
HC Consumer Finance Philippines, Inc.
课程: Python Programming
Machine Translated
记住/重复关键主题的非常好的方法。非常好的“热身”练习。
课程: Python Programming
Machine Translated
Python课程大纲
课程名称
课程时长
课程概览
课程名称
课程时长
课程概览
28小时
课程概览
This course is designed for those wishing to learn the Python programming language. The emphasis is on the Python language, the core libraries, as well as on the selection of the best and most useful libraries developed by the Python community. Python drives businesses and is used by scientists all over the world – it is one of the most popular programming languages.
The course can be delivered using the latest Python version 3.x with practical exercises making use of the full power. This course can be delivered on any operating system (all flavours of UNIX, including Linux and Mac OS X, as well as Microsoft Windows).
The practical exercises constitute about 70% of the course time, and around 30% are demonstrations and presentations. Discussions and questions can be asked throughout the course.
Note: the training can be tailored to specific needs upon prior request ahead of the proposed course date.
The course can be delivered using the latest Python version 3.x with practical exercises making use of the full power. This course can be delivered on any operating system (all flavours of UNIX, including Linux and Mac OS X, as well as Microsoft Windows).
The practical exercises constitute about 70% of the course time, and around 30% are demonstrations and presentations. Discussions and questions can be asked throughout the course.
Note: the training can be tailored to specific needs upon prior request ahead of the proposed course date.
28小时
课程概览
This is a practical course, which shows why programming is a powerful tool in the context of solving biological problems. During the course participants will be taught the Python programming language, a language widely considered both powerful as well as easy to use. This course might be considered as a demonstration how bioinformatics improves biologists lives.
The course is designed and aimed for people without computer science background who want to learn to program.
This course is suited for:
- Researchers dealing with biological data.
- Scientists who would like to learn how to automate everyday tasks and analyse data.
- Managers who want to learn how programming improves workflows and conducting projects.
By the end of the course, participants will be able to write short programs, which will allow them to manipulate, analyse and deal with biological data and present results in a graphical format.
The course is designed and aimed for people without computer science background who want to learn to program.
This course is suited for:
- Researchers dealing with biological data.
- Scientists who would like to learn how to automate everyday tasks and analyse data.
- Managers who want to learn how programming improves workflows and conducting projects.
By the end of the course, participants will be able to write short programs, which will allow them to manipulate, analyse and deal with biological data and present results in a graphical format.
14小时
课程概览
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.
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.
28小时
课程概览
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.
14小时
课程概览
Pandas is a Python package that provides data structures for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data.
14小时
课程概览
Selenium是一个开源库，用于跨多个浏览器自动化Web应用程序测试。Selenium像人一样与浏览器交互：通过单击链接、填写表单和验证文本。它是Web应用程序测试自动化最流行的工具。Selenium构建在WebDriver框架上，对许多脚本语言（包括Python）具有出色的绑定。
在本次培训中，学员将把Python的强大功能与Selenium结合起来，以自动化示例Web应用程序的测试。通过在实验室环境中将理论与实践相结合，学员将学习并操作如何用Python和Selenium自动化自己的Web测试项目。
受众
- 测试人员和开发人员
课程形式
- 部分讲座、部分讨论、大量实操
在本次培训中，学员将把Python的强大功能与Selenium结合起来，以自动化示例Web应用程序的测试。通过在实验室环境中将理论与实践相结合，学员将学习并操作如何用Python和Selenium自动化自己的Web测试项目。
受众
- 测试人员和开发人员
课程形式
- 部分讲座、部分讨论、大量实操
28小时
课程概览
在这一由讲师引导的培训中，参与者将学习高级Python编程技术，包括如何将这种多功能语言应用于解决分布式应用、财务、数据分析和可视化、UI编程及维护脚本等领域的问题。
受众
- 开发人员
课程形式
- 部分讲座、部分讨论、练习和大量实操
注意事项
- 如果您想添加、移除或自定义本课程中的任一部分或主题，请联系我们以作安排。
受众
- 开发人员
课程形式
- 部分讲座、部分讨论、练习和大量实操
注意事项
- 如果您想添加、移除或自定义本课程中的任一部分或主题，请联系我们以作安排。
14小时
课程概览
这一由讲师引导的培训是基于Al Sweigart所著的知名书籍——“用Python自动化枯燥的事物（Automate the Boring Stuff with Python）”。它针对初学者，通过实际操作练习和讨论涵盖了Python编程的基本概念。重点在于学习编写代码以显着提高办公效率。
在本次培训结束后，参与者将知道如何用Python进行编程，并将这项新技能应用于：
- 通过编写简单的Python程序来自动执行任务。
- 编写可以使用“正则表达式”进行文本模式识别的程序。
- 以编程方式生成和更新Excel电子表格。
- 解析PDF和Word文档。
- 抓取网站，并从线上来源提取信息。
- 编写发送电子邮件通知的程序。
- 使用Python的调试工具来快速解决错误。
- 以编程方式控制鼠标和键盘，以执行点击和输入。
受众
- 希望学习用Python编程的非程序员
- 希望优化办公效率的专业人士和公司团队
- 希望自动化繁琐程序和工作流程的经理
课程形式
- 部分讲座、部分讨论、练习和大量实操
在本次培训结束后，参与者将知道如何用Python进行编程，并将这项新技能应用于：
- 通过编写简单的Python程序来自动执行任务。
- 编写可以使用“正则表达式”进行文本模式识别的程序。
- 以编程方式生成和更新Excel电子表格。
- 解析PDF和Word文档。
- 抓取网站，并从线上来源提取信息。
- 编写发送电子邮件通知的程序。
- 使用Python的调试工具来快速解决错误。
- 以编程方式控制鼠标和键盘，以执行点击和输入。
受众
- 希望学习用Python编程的非程序员
- 希望优化办公效率的专业人士和公司团队
- 希望自动化繁琐程序和工作流程的经理
课程形式
- 部分讲座、部分讨论、练习和大量实操
28小时
课程概览
The aim of this course is to provide general 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.
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小时
课程概览
在这一由讲师引导的现场培训中，参与者将学习Python中最相关及最尖端的机器学习技术，因为它们构建了一系列涉及图像、音乐、文本和财务数据的演示应用程序。
在本次培训结束后，参与者将能够：
- 运用用于解决复杂问题的机器学习算法和技术
- 将深度学习和半监督学习应用于涉及图像、音乐、文本和财务数据的应用程序
- 推动Python算法达到其最大潜力
- 使用例如NumPy和Theano的库和包
受众
- 开发人员
- 分析师
- 数据科学家
课程形式
- 部分讲座、部分讨论、练习和大量实操
在本次培训结束后，参与者将能够：
- 运用用于解决复杂问题的机器学习算法和技术
- 将深度学习和半监督学习应用于涉及图像、音乐、文本和财务数据的应用程序
- 推动Python算法达到其最大潜力
- 使用例如NumPy和Theano的库和包
受众
- 开发人员
- 分析师
- 数据科学家
课程形式
- 部分讲座、部分讨论、练习和大量实操
21小时
课程概览
在这一由讲师引导的现场培训中，参与者将学习如何使用正确的机器学习和NLP（自然语言处理）技术从基于文本的数据中提取价值。
在本次培训结束后，参与者将能够：
- 用高质量、可重用的代码解决基于文本的数据科学问题
- 运用scikit-learn的不同方面（分类、聚类、回归、降维）来解决问题
- 使用基于文本的数据建立有效的机器学习模型
- 创建一个数据集并从非结构化文本中提取特征
- 用Matplotlib可视化数据
- 构建和评估模型以获得洞察力
- 解决文本编码错误
受众
- 开发人员
- 数据科学家
课程形式
- 部分讲座、部分讨论、练习和大量实操
在本次培训结束后，参与者将能够：
- 用高质量、可重用的代码解决基于文本的数据科学问题
- 运用scikit-learn的不同方面（分类、聚类、回归、降维）来解决问题
- 使用基于文本的数据建立有效的机器学习模型
- 创建一个数据集并从非结构化文本中提取特征
- 用Matplotlib可视化数据
- 构建和评估模型以获得洞察力
- 解决文本编码错误
受众
- 开发人员
- 数据科学家
课程形式
- 部分讲座、部分讨论、练习和大量实操
21小时
课程概览
Natural language generation (NLG) refers to the production of natural language text or speech by a computer.
In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content.
By the end of this training, participants will be able to:
- Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting
- Select and organize source content, plan sentences, and prepare a system for automatic generation of original content
- Understand the NLG pipeline and apply the right techniques at each stage
- Understand the architecture of a Natural Language Generation (NLG) system
- Implement the most suitable algorithms and models for analysis and ordering
- Pull data from publicly available data sources as well as curated databases to use as material for generated text
- Replace manual and laborious writing processes with computer-generated, automated content creation
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content.
By the end of this training, participants will be able to:
- Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting
- Select and organize source content, plan sentences, and prepare a system for automatic generation of original content
- Understand the NLG pipeline and apply the right techniques at each stage
- Understand the architecture of a Natural Language Generation (NLG) system
- Implement the most suitable algorithms and models for analysis and ordering
- Pull data from publicly available data sources as well as curated databases to use as material for generated text
- Replace manual and laborious writing processes with computer-generated, automated content creation
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21小时
课程概览
In this instructor-led, live training in 中国, participants will learn how to use PyTest to write short, maintainable tests that are elegant, expressive and readable.
By the end of this training, participants will be able to:
- Write readable and maintainable tests without the need for boilerplate code.
- Use the fixture model to write small tests.
- Scale tests up to complex functional testing for applications, packages, and libraries.
- Understand and apply PyTest features such as hooks, assert rewriting and plug-ins.
- Reduce test times by running tests in parallel and across multiple processors.
- Run tests in a continuous integration environment, together with other utilities such as tox, mock, coverage, unittest, doctest and Selenium.
- Use Python to test non-Python applications.
By the end of this training, participants will be able to:
- Write readable and maintainable tests without the need for boilerplate code.
- Use the fixture model to write small tests.
- Scale tests up to complex functional testing for applications, packages, and libraries.
- Understand and apply PyTest features such as hooks, assert rewriting and plug-ins.
- Reduce test times by running tests in parallel and across multiple processors.
- Run tests in a continuous integration environment, together with other utilities such as tox, mock, coverage, unittest, doctest and Selenium.
- Use Python to test non-Python applications.
35小时
课程概览
By the end of the training the delegates are expected to be sufficiently equipped with the essential python concepts and should be able to sufficiently use NLTK to implement most of the NLP and ML based operations. The training is aimed at giving not just an executional knowledge but also the logical and operational knowledge of the technology therein.
21小时
课程概览
在这一由讲师引导的现场培训中，参与者将学习如何应用机器学习技术和工具来解决银行业的现实问题。Python将被用作编程语言。
参与者首先学习关键原则，然后通过建立自己的机器学习模型并使用模型来完成一些现场项目以将所学知识运用到实践中。
受众
- 开发人员
- 数据科学家
课程形式
- 部分讲座、部分讨论、练习和大量实操
参与者首先学习关键原则，然后通过建立自己的机器学习模型并使用模型来完成一些现场项目以将所学知识运用到实践中。
受众
- 开发人员
- 数据科学家
课程形式
- 部分讲座、部分讨论、练习和大量实操
35小时
课程概览
Python是一门在金融行业拥有巨大声望的编程语言。最大的投资银行和对冲基金正在使用它来构建包括核心交易项目及风险管理系统在内的广泛的金融应用。
在这一由讲师引导的现场培训中，参与者将学习如何使用Python开发实际的应用程序以解决一些特定的财务相关的问题。
在本次培训结束后，参与者将能够：
- 了解Python编程语言的基础知识
- 下载、安装和维护用Python创建财务应用程序的最佳开发工具
- 选择和利用最合适的Python软件包和编程技术来组织、可视化和分析从各种来源（CSV、Excel、数据库、网站等）得来的财务数据。
- 构建解决资产配置、风险分析、投资绩效等相关问题的应用程序
- 故障排除、集成部署和优化他们的应用程序
受众
- 开发人员
- 分析师
- 宽客
课程形式
- 部分讲座、部分讨论、练习和大量实操
注意事项
- 该培训旨在为金融专业人士所面对的一些原则问题提供解决方案。但是，如果您有一个特定的主题、工具或技术想要附加或详细说明，请联系我们以作安排。
在这一由讲师引导的现场培训中，参与者将学习如何使用Python开发实际的应用程序以解决一些特定的财务相关的问题。
在本次培训结束后，参与者将能够：
- 了解Python编程语言的基础知识
- 下载、安装和维护用Python创建财务应用程序的最佳开发工具
- 选择和利用最合适的Python软件包和编程技术来组织、可视化和分析从各种来源（CSV、Excel、数据库、网站等）得来的财务数据。
- 构建解决资产配置、风险分析、投资绩效等相关问题的应用程序
- 故障排除、集成部署和优化他们的应用程序
受众
- 开发人员
- 分析师
- 宽客
课程形式
- 部分讲座、部分讨论、练习和大量实操
注意事项
- 该培训旨在为金融专业人士所面对的一些原则问题提供解决方案。但是，如果您有一个特定的主题、工具或技术想要附加或详细说明，请联系我们以作安排。
14小时
课程概览
在Python机器学习中，文本摘要功能可以读取输入文本并生成文本摘要。这个功能可以从命令行或从Python API / 库中获得。一个令人兴奋的应用是执行摘要的快速创建；这对在做报告和演讲前需要审阅大量文本数据的组织特别有用。
在这一由讲师引导的现场培训中，学员将学习使用Python创建一个简单的可自动生成输入文本摘要的应用程序。
在本次培训结束后，学员将能够：
- 使用一个命令行工具来总结文本。
- 使用Python库设计和创建文本摘要代码。
- 评估三个Python摘要库：sumy 0.7.0、psisummarization 1.0.4、readless 1.0.17
受众
- 开发人员
- 数据科学家
课程形式
- 部分讲座、部分讨论、练习和大量实操
在这一由讲师引导的现场培训中，学员将学习使用Python创建一个简单的可自动生成输入文本摘要的应用程序。
在本次培训结束后，学员将能够：
- 使用一个命令行工具来总结文本。
- 使用Python库设计和创建文本摘要代码。
- 评估三个Python摘要库：sumy 0.7.0、psisummarization 1.0.4、readless 1.0.17
受众
- 开发人员
- 数据科学家
课程形式
- 部分讲座、部分讨论、练习和大量实操
14小时
课程概览
面向对象编程（Object-Oriented Programming，OOP）是一种基于对象概念的编程范式。OOP更注重数据而不是注重逻辑。Python是一种高级编程语言，因其清晰的语法和代码可读性而闻名。
在这一由讲师引导的现场培训中，学员将学习如何使用Python开始面向对象编程。
在本次培训结束后，学员将能够：
- 了解面向对象编程的基本概念
- 了解Python中的OOP语法
- 用Python编写自己的面向对象程序
受众
- 想了解面向对象编程的初学者
- 有兴趣学习用Python进行OOP的开发人员
- 有兴趣学习OOP的Python程序员
课程形式
- 部分讲座、部分讨论、练习和大量实操
在这一由讲师引导的现场培训中，学员将学习如何使用Python开始面向对象编程。
在本次培训结束后，学员将能够：
- 了解面向对象编程的基本概念
- 了解Python中的OOP语法
- 用Python编写自己的面向对象程序
受众
- 想了解面向对象编程的初学者
- 有兴趣学习用Python进行OOP的开发人员
- 有兴趣学习OOP的Python程序员
课程形式
- 部分讲座、部分讨论、练习和大量实操
21小时
课程概览
机器学习是人工智能的一个分支，指计算机可以在不被明确编程的情况下学习。
在这一由讲师引导的现场培训中，参与者将学习如何应用机器学习技术和工具来解决财务的现实问题。Python将被用作编程语言。
参与者首先学习关键原则，然后通过建立自己的机器学习模型并使用模型来完成一些团队项目以将所学知识运用到实践中。
在本次培训结束后，参与者将能够：
- 了解机器学习的基本概念
- 了解机器学习在金融领域的应用和使用
- 使用Python机器学习开发自己的算法交易策略
受众
- 开发人员
- 数据科学家
课程形式
- 部分讲座、部分讨论、练习和大量实操
在这一由讲师引导的现场培训中，参与者将学习如何应用机器学习技术和工具来解决财务的现实问题。Python将被用作编程语言。
参与者首先学习关键原则，然后通过建立自己的机器学习模型并使用模型来完成一些团队项目以将所学知识运用到实践中。
在本次培训结束后，参与者将能够：
- 了解机器学习的基本概念
- 了解机器学习在金融领域的应用和使用
- 使用Python机器学习开发自己的算法交易策略
受众
- 开发人员
- 数据科学家
课程形式
- 部分讲座、部分讨论、练习和大量实操
21小时
课程概览
Spark是一个用于查询、分析和转换大数据的数据处理引擎。Python是一种高级编程语言，因其清晰的语法和代码可读性而闻名。PySpark允许用户将Spark与Python连接。
在这一由讲师引导的现场培训中，学员将通过实践练习学习如何使用Python和Spark一起分析大数据。
在本次培训结束后，学员将能够：
- 了解如何使用Spark和Python一起分析大数据
- 开展模拟真实世界环境的练习
- 用不同的工具和技术通过PySpark进行大数据分析
受众
- 开发人员
- IT专业人士
- 数据科学家
课程形式
- 部分讲座、部分讨论、练习和大量实操
在这一由讲师引导的现场培训中，学员将通过实践练习学习如何使用Python和Spark一起分析大数据。
在本次培训结束后，学员将能够：
- 了解如何使用Spark和Python一起分析大数据
- 开展模拟真实世界环境的练习
- 用不同的工具和技术通过PySpark进行大数据分析
受众
- 开发人员
- IT专业人士
- 数据科学家
课程形式
- 部分讲座、部分讨论、练习和大量实操
35小时
课程概览
Python is a high-level programming language famous for its clear syntax and code readibility.
In this instructor-led, live training, participants will learn how to use Python for quantitative finance.
By the end of this training, participants will be able to:
- Understand the fundamentals of Python programming
- Use Python for financial applications including implementing mathematical techniques, stochastics, and statistics
- Implement financial algorithms using performance Python
Audience
- Developers
- Quantitative analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use Python for quantitative finance.
By the end of this training, participants will be able to:
- Understand the fundamentals of Python programming
- Use Python for financial applications including implementing mathematical techniques, stochastics, and statistics
- Implement financial algorithms using performance Python
Audience
- Developers
- Quantitative analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14小时
课程概览
计算机视觉是一个涉及从数字媒体自动提取、分析和理解有用信息的领域。Python是一种高级编程语言，因其清晰的语法和代码可读性而闻名。
在这一由讲师引导的现场培训中，学员将使用Python逐步创建简单的计算机视觉应用程序，并从中习得计算机视觉的基础知识。
在本次培训结束后，学员将能够：
- 了解计算机视觉的基础知识
- 使用Python来实现计算机视觉任务
- 使用Python构建自己的计算机视觉应用程序
受众
- 对计算机视觉感兴趣的Python程序员
课程形式
- 部分讲座、部分讨论、练习和大量实操
在这一由讲师引导的现场培训中，学员将使用Python逐步创建简单的计算机视觉应用程序，并从中习得计算机视觉的基础知识。
在本次培训结束后，学员将能够：
- 了解计算机视觉的基础知识
- 使用Python来实现计算机视觉任务
- 使用Python构建自己的计算机视觉应用程序
受众
- 对计算机视觉感兴趣的Python程序员
课程形式
- 部分讲座、部分讨论、练习和大量实操
28小时
课程概览
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.
In this instructor-led, live training, participants will learn how to implement deep learning models for banking 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 banking
- Use Python, Keras, and TensorFlow to create deep learning models for banking
- Build their own deep learning credit risk model using Python
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to implement deep learning models for banking 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 banking
- Use Python, Keras, and TensorFlow to create deep learning models for banking
- Build their own deep learning credit risk model using Python
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
28小时
课程概览
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.
In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction 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 finance
- Use Python, Keras, and TensorFlow to create deep learning models for finance
- Build their own deep learning stock price prediction model using Python
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction 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 finance
- Use Python, Keras, and TensorFlow to create deep learning models for finance
- Build their own deep learning stock price prediction model using Python
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
7小时
课程概览
Microservices refer to an application architecture style that promotes the use of independent, self-contained programs. Python is a dynamic high-level programming language that is ideal for both scripting as welll as application development. Python's expansive library of open source tools and frameworks make it a practical choice for building microservices.
In this instructor-led, live training, participants will learn the fundamentals of microservices as they step through the creation of a microservice using Python.
By the end of this training, participants will be able to:
- Understand the basics of building microservices
- Learn how to use Python to build microservices
- Learn how to use Docker to deploy Python based microservices
Audience
- Developers
- Programmers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the fundamentals of microservices as they step through the creation of a microservice using Python.
By the end of this training, participants will be able to:
- Understand the basics of building microservices
- Learn how to use Python to build microservices
- Learn how to use Docker to deploy Python based microservices
Audience
- Developers
- Programmers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21小时
课程概览
A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data. The acronym GIS is sometimes used for geographic information science (GIScience) to refer to the academic discipline that studies geographic information systems and is a large domain within the broader academic discipline of geoinformatics.
The use of Python with GIS has substantially increased over the last two decades, particularly with the introduction of Python 2.0 series in 2000, which included many new programming features that made the language much easier to deploy. Since that time, Python has not only been utilized within commercial GIS such as products by Esri but also open source platforms, including as part of QGIS and GRASS. In fact, Python today is by far the most widely used language by GIS users and programmers.
This program covers the usage of Python and its advance libraries like geopandas, pysal, bokeh and osmnx to implement your own GIS features. The program also covers introductory modules around ArcGIS API, and QGIS toolboox.
The use of Python with GIS has substantially increased over the last two decades, particularly with the introduction of Python 2.0 series in 2000, which included many new programming features that made the language much easier to deploy. Since that time, Python has not only been utilized within commercial GIS such as products by Esri but also open source platforms, including as part of QGIS and GRASS. In fact, Python today is by far the most widely used language by GIS users and programmers.
This program covers the usage of Python and its advance libraries like geopandas, pysal, bokeh and osmnx to implement your own GIS features. The program also covers introductory modules around ArcGIS API, and QGIS toolboox.
14小时
课程概览
Tableau is a business intelligence and data visualization tool. Python is a widely used programming language which provides support for a wide variety of statistical and machine learning techniques. Tableau's data visualization power and Python's machine learning capabilities, when combined, help developers rapidly build advanced data analytics applications for various business use cases.
In this instructor-led, live training, participants will learn how to combine Tableau and Python to carry out advanced analytics. Integration of Tableau and Python will be done via the TabPy API.
By the end of this training, participants will be able to:
- Integrate Tableau and Python using TabPy API
- Use the integration of Tableau and Python to analyze complex business scenarios with few lines of Python code
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to combine Tableau and Python to carry out advanced analytics. Integration of Tableau and Python will be done via the TabPy API.
By the end of this training, participants will be able to:
- Integrate Tableau and Python using TabPy API
- Use the integration of Tableau and Python to analyze complex business scenarios with few lines of Python code
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21小时
课程概览
深度强化学习是指“人工智能体”通过反复试验和奖惩来学习的能力。人工智能体旨在模仿人类直接从原始输入（如视觉）获取和构建知识的能力。为了实现强化学习，深度学习和神经网络会被用到。强化学习与机器学习不同，不依赖于有监督和无监督的学习方法。
在这一由讲师引导的现场培训中，学员将在逐步创建深度学习智能体的过程中学习深度强化学习的基础知识。
在本次培训结束后，学员将能够：
- 理解深度强化学习的基本概念，及其与机器学习的区别
- 运用先进的强化学习算法来解决实际问题
- 构建深度学习智能体
受众
- 开发人员
- 数据科学家
课程形式
- 部分讲座、部分讨论、练习和大量实操
在这一由讲师引导的现场培训中，学员将在逐步创建深度学习智能体的过程中学习深度强化学习的基础知识。
在本次培训结束后，学员将能够：
- 理解深度强化学习的基本概念，及其与机器学习的区别
- 运用先进的强化学习算法来解决实际问题
- 构建深度学习智能体
受众
- 开发人员
- 数据科学家
课程形式
- 部分讲座、部分讨论、练习和大量实操
14小时
课程概览
Internet of Things (IoT) is a network infrastructure that connects physical objects and software applications wirelessly, allowing them to communicate with each other and exchange data via network communications, cloud computing, and data capture. Python is a high-level programming language recommended for IoT due to its clear syntax and large community support.
In this instructor-led, live training, participants will learn how to program IoT solutions with Python.
By the end of this training, participants will be able to:
- Understand the fundamentals of IoT architecture
- Learn the basics of using Raspberry Pi
- Install and configure Python on Raspberry Pi
- Learn the benefits of using Python in programming IoT systems
- Build, test, deploy, and troubleshoot an IoT system using Python and Raspberry Pi
Audience
- Developers
- Engineers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
In this instructor-led, live training, participants will learn how to program IoT solutions with Python.
By the end of this training, participants will be able to:
- Understand the fundamentals of IoT architecture
- Learn the basics of using Raspberry Pi
- Install and configure Python on Raspberry Pi
- Learn the benefits of using Python in programming IoT systems
- Build, test, deploy, and troubleshoot an IoT system using Python and Raspberry Pi
Audience
- Developers
- Engineers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
14小时
课程概览
In this instructor-led, live training in 中国 (onsite or remote), participants will learn how to combine the capabilities of Python and Excel.
By the end of this training, participants will be able to:
- Install and configure packages for integrating Python and Excel.
- Read, write, and manipulate Excel files using Python.
- Call Python functions from Excel.
By the end of this training, participants will be able to:
- Install and configure packages for integrating Python and Excel.
- Read, write, and manipulate Excel files using Python.
- Call Python functions from Excel.
近期Python培训课程
16-08-2021 09:30
上海 - 上海中区广场
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