Machine Learning courses

1656 Courses

人工智能导论

探索人工智能基础概念,研究数据驱动的网络模型、算法与平台。本课程梳理人工智能的关键发展节点,激发您的学习兴趣。通过此课程,您将牢牢掌握人工智能基础,为深度学习的最前沿铺路,不仅适合计算机专业学生,也对其他学科的学生适宜。
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大模型技术及交叉应用

Embark on a comprehensive journey to understand the intricacies of large-scale AI models and their diverse applications. With XuetangX, delve into nine meticulously structured lessons that encompass the essential principles, intricate technical implementations, and practical applications across various sectors. Whether you're passionate about.
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提示语工程训练营

参与提示语工程训练营,深入学习如何掌握和运用提示语工程的重要技术。这门课程提供丰富的知识传授和实践练习,帮助学员设计、优化交互提示语,以提高与人工智能模型的交互效果。 您将探索提示语工程的核心概念、最佳实践以及常见的应用场景。通过课程的实际案例和练习,您将加深对这些概念的理解,并获得在人工智能应用开发中实际应用提示语工程的宝贵经验。 课程由Xue.
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AI大模型应用开发工程师

Learn to become an AI Large Language Model Application Development Engineer through a comprehensive training that encapsulates essential core concepts, development frameworks, and practical fields of application. Master critical skills including prompt engineering, LLM fine-tuning, and API integration while delving into real-world use cases suc.
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Desarrollo de soluciones de machine learning (Español LATAM) | Developing Machine Learning Solutions (LATAM Spanish)

En este curso, aprenderá acerca del ciclo de vida del machine learning y de cómo utilizar los servicios de AWS en cada etapa. Conocerá las diversas fuentes de modelos de machine learning y aprenderá técnicas para evaluar su rendimiento. También comprenderá la importancia de las operaciones de machine learning (MLOps) para optimizar el desarrollo.
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神经网络理论及应用

Course Objective Since the mid-1980s, artificial neural networks have rapidly evolved into a forefront research field in information technology. This parallel information processing system significantly impacts fields such as computer science, AI, cognitive science, neuroscience, information science, automatic control, and robotics. The course "N.
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大数据与机器智能

在这门课程中,您将掌握Python编程技能并进行机器智能的实验。通过深入学习机器学习的基本原理,您将完成机器智能的课程项目。参与Scikit-learn、TensorFlow2和Keras2技术的实践,体验计算机视觉和语音识别等人工智能应用。 此外,课程帮助您理解人工智能产业的发展方向和生态系统。您将熟悉各种技术工具及应用,拓展人工智能领域的视野,培养利用人工智能技术进行创新和.
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商务大数据基础与实务

本课程是实现高校人才培养目标的专业课程,旨在通过教学使学生对大数据技术有基本的认识和了解。在教学内容组织上,每一个知识点都以理论+案例形式形成工作任务,由学生围绕工作任务寻求大数据相关理论知识支撑,训练其分析问题、解决问题的能力,培养其职业能力。 课程分为七个部分,包括走进大数据、大数据与云计算、物联网、人工智能、大数据采集与清洗、数据存储和处.
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是我们创造了算法,还是算法控制着我们?

"我们对你生活的影响比你想象的要大,我们会告诉你家在哪,你在学校的位置,决定你是否能得到贷款去买你梦寐以求的车,告诉你公交车什么时候会来,你该认识哪些朋友,甚至是男朋友或女朋友……我们叫算法,而且我们无处不在。" ——《算法:如何主导人类世界》 这门课程由XuetangX提供,涵盖人工智能、机器学习、道德、隐私和算法的核心内容。
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人工智能基础

本课程专为电气信息类专业本科生设计,重点介绍人工智能的前沿技术及其应用基础。课程内容覆盖广泛,通过系统的知识讲解和丰富的研究案例,学生将拓宽视野,增进科学素养,并优化知识结构。结合最新的人工智能技术讨论,配以视频资料和文献,激发学生对各研究领域的兴趣,提高学习热情,掌握创新方法,培养将理论与实际相结合的能力,以及问题解决能力。与类似课程相比,本课程.
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More and more products are now being developed using artificial intelligence. To avoid being left on the sidelines of progress, managers must understand how the robot’s “brains” work

Artificial intelligence (AI) and machine learning technologies have been used for many years, but now the intensity of their use has increased significantly. For example, machine learning is being actively implemented in telecommunications, retail, marketing and e-commerce. But many still do not fully understand what it is.

Machine learning involves the system processing a large number of examples, during which it identifies patterns and uses them to predict the characteristics of new data. In other words, this is the process of giving AI ml courses “consciousness”, the ability to remember and analyze.

Machine learning use cases

The use of machine learning has touched many areas in our lives. Let's look at the most striking examples of the use of computer intelligence:

Facial recognition in the subway will help identify violators or criminals in a huge mass of people. Ordinary observers cannot cope with this task. But a fast-learning machine will do this job without any problems.

What do you need for machine learning (ML)?

For those interested in training, there are several requirements to be met in order to be successful in this field. So, there are the main points you need to know about the machine learning course. These requirements include:

  1. Basic knowledge of programming languages such as Python, R, Java, JavaScript, etc.

  2. Average knowledge of statistics and probability.

  3. Basic knowledge of linear algebra in the ml course. In a linear regression model, a line is drawn through all the data points, and that line is used to calculate new values.

  4. Understanding Calculus.

  5. Knowledge of how to clean and structure raw data into the desired format to reduce the time required for decision making.

Machine learning courses from AI Eeducation are the best choice!