Machine Learning courses

1656 Courses

人工智能

课程结构分为三部分,共八章内容,涵盖人工智能导引、大数据、机器学习、计算机视觉、智能语音、自然语言处理、智能机器人以及教育应用与伦理安全。 课程介绍了人工智能的核心技术,与教育应用紧密结合,并设计实践环节,采用理论与案例结合的启发式教学方法,激发同学们对人工智能的学习热情。课程特点包括: 理论讲授:通过课件、多媒体等方式讲解人工智能技术。 理.
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计算思维与人工智能基础

计算思维与人工智能课程融合理论与实际,是多学科交叉且广泛应用的前沿课程。基于多年人工智能教学实践,课程结合计算机技术发展,分析和认知人工智能学科特点,以计算思维视角探讨其基本理论和方法,运用Python实现相关理论的案例。 课程特点包括: 注重实例的教学方式: 专注于激发学生学习兴趣,结合课程属性,以解决问题的原理和思路为重点,讲解经典实例,帮助学.
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小白学人工智能

中国也在大力发展新一代人工智能技术,并致力于将其应用于各行各业。本课程完成后,学生将能够: 了解人工智能行业的最新应用和发展趋势。 从数据、算法和计算力的角度理解人工智能的发展。 用行业或生活术语比喻人工智能的概念和原理。 体验和理解深度学习原理,涉及CNN、图像风格迁移、RNN等架构。 通过实例理解深度学习特征,如输入层、隐藏层、输.
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人工智能

本课程的特色包括: 注重人工智能核心技术体系的构建,讲解全面完整,涵盖多个核心技术途径,如机器学习和神经网络等。 从“认识你自己”角度出发,将内容有机结合为一个整体,便于系统理解和掌握。 培养解决实际问题的思路,通过案例示例展示基本思路。 注重人工智能思维模式的建构,强调技术与非技术的区别。 注重技术创新思维的培养和建构。 本课程自2008年起.
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数据科学导论

新技术如云计算、大数据、物联网和人工智能彻底改变了我们对数据的理解,带来了许多新问题,而这些问题在传统理论中尚无解决方案。每个领域现在已发展出许多新的学科如农业大数据、工业大数据等,从学科角度探讨大数据的挑战和解决方案。因此,我们亟需更新我们的知识结构。 课程介绍数据科学的基本概念、理论,并展示其应用和发展前景。学生将掌握获取、处理、管理、分析和.
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人工智能方法与技术

本课程系统、深入地介绍人工智能的理论方法、领域应用和前沿技术,主要内容包括:知识表示与因果推理、语言文本分析、集成学习及大数据分类与预测、智能推荐新技术、社交媒体的图模型计算与传播机制、博弈论与智能决策方法、复杂系统网络建模与控制决策等。注重对智能媒体计算相关的语言文本、智能推荐、互联网传播等场景下的实战应用分析。 适合于研究生或高年级本.
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人工智能教育应用

人工智能被视为继蒸汽机、电力、互联网之后最有可能带来新的产业革命浪潮的技术,而教育领域则是人工智能技术影响最为深刻的领域之一。无论你是在校学生还是一线教师,只要对人工智能和教育感兴趣,都可以来学习这门课程。这门课程将介绍人工智能的核心技术与教育创新应用场景,帮助你深刻认识人工智能对教育体系的变革与推动作用,以及未来人工智能在教育领域的发展趋势.
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AI TIME PHD AAAI专场七

Immerse yourself in the forefront of artificial intelligence by attending the AI TIME PHD AAAI专场七, a compelling talk presented in Chinese. Delve into the revolutionary research and developments showcased at the AAAI (Association for the Advancement of Artificial Intelligence) conference, with a focus on transformative insights and technol.
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AI TIME PHD AAAI专场六

Discover the forefront of artificial intelligence by joining the AI TIME PHD AAAI专场六. This Chinese-language conference presents an opportunity to delve into the latest advancements in AI with comprehensive discussions and research presentations led by top experts in the field. Ideal for anyone interested in artificial intelligence, machine.
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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!