Machine Learning courses

1795 Courses

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AI TIME PHD - AAAI 专场二

XuetangX Delve into the latest advancements in artificial intelligence at the AI TIME PHD - AAAI 专场二. This Chinese-language session is your gateway to understanding the forefront of AI research. Attendees will benefit from presentations and discussions led by top researchers, focusing on innovative methodologies and practical applications of.

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智能技术与系统

通过这门全面的中文课程，深入了解智能技术与系统，掌握人工智能、机器学习和智能系统开发的关键概念。探索智能技术背后的基本原则，学习系统架构与框架，积累实施AI解决方案的实用知识。课程涵盖核心主题，包括知识表示、推理方法、神经网络以及智能信息处理。学习如何设计和优化适用于现实世界的智能系统，提高你的专业技能。此课程由XuetangX提供，适合希望提升.

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ChatGPT与AIGC技术的前沿探索

探索ChatGPT和AIGC技术的前沿，通过现场直播课程获取深刻的见解。这些课程涵盖了人工智能和内容生成的最新发展、实际应用以及未来的发展趋势，为您揭示这些令人兴奋的技术的广阔可能性。由XuetangX提供的尖端课程适合对AI和生成内容充满热情的学习者。

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图灵密语，AI将如何定义编程新范式

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大模型为什么是AI领域的“兵家必争之地”？

通过观看本视频，你将深入了解大模型在人工智能领域中扮演的核心角色。视频用中文详细讲解了大模型的重要性、最近的技术进展，以及这些模型如何对未来的AI创新产生深远影响。加入我们，探索AI发展的最前沿。本课程由XuetangX提供，属于以下类别：人工智能课程机器学习课程深度学习课程神经网络课程 Transformer 架构课程

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论道AI Open

Discover the forefront of artificial intelligence advancements with 论道AI Open, a Chinese-language conference talk series. This event gathers eminent experts and researchers dedicated to unveiling the latest AI topics, providing industry insights, and discussing practical applications across multiple domains. Don't miss this opportunity to expa.

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AI TIME：顶会知识唠唠嗑

Discover the forefront of artificial intelligence with AI TIME, an opportunity to delve into the newest advancements in the field. Participate in relaxed discussions that cover high-impact AI conference papers and groundbreaking research, explained in a manner that simplifies complex ideas for better comprehension. No need for prior deep tech.

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ChatGPT会引领AI新范式吗？

更多由XuetangX提供的课程加入我们的直播讨论，深入探讨ChatGPT的创新潜力和它在未来人工智能领域中的重要作用。了解其如何可能作为新范式的先驱，对科技发展产生深远影响。不要错过这个绝佳机会，进一步了解前沿技术的未来走向。

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AI TIME PhD 华南理工实验室专场一

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AI TIME PhD ICLR专场一

Participate in AI TIME PhD ICLR专场一 to stay at the forefront of artificial intelligence innovation. This specialized lecture series, conducted in Chinese, offers a deep dive into recent advancements showcased at the International Conference on Learning Representations (ICLR). Engage with in-depth discussions and analyses led by experts to.

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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:

Voice assistant Alisa. This system, without human help, will always find the answer to any question of interest, turn on music, answer the call and much more.

Selecting relevant advertising in browsers is not an easy process for a person, but it is quick for a computer. The system will analyze the viewing data and select what a particular user will like. The same system is used on various marketplaces - products that a person viewed or purchased are offered.

Chatbots in various dialogues. For example, many banks use bots to solve customer problems. It's convenient and fast.

Data security: Machine learning models can identify data security vulnerabilities before they become breaches. By analyzing past experiences, machine learning models can predict future high-risk actions so that risk can be mitigated proactively.

Finance: Banks, trading brokers and fintech firms use machine learning algorithms to automate trading and provide financial advisory services to investors. Bank of America uses chatbot Erica to automate customer support.

Healthcare: Machine learning is used to analyze massive medical data sets to speed up the discovery of treatments and cures, improve patient outcomes, and automate routine processes to prevent human errors. For example, IBM Watson uses data mining to provide doctors with data they can use to personalize patient care.

Fraud detection: AI is used in the finance and banking sector to autonomously analyze large numbers of transactions to identify fraudulent activities in real time. Technology services company Capgemini says fraud detection systems using machine learning and analytics reduce fraud investigation time by 70% and increase detection accuracy by 90% .

Retail: AI researchers and developers are using machine learning algorithms to develop AI recommendation engines that make relevant product suggestions based on shoppers' past choices, as well as historical, geographic and demographic data.

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:

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

Average knowledge of statistics and probability.

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.

Understanding Calculus.

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!