Machine Learning courses

1770 Courses

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This is AI

Welcome to a transformative era where artificial intelligence is not a distant dream but a palpable reality reshaping our lives. Explore the incredible capabilities of AI, such as machines engaging in debates that challenge human intellect and facial recognition techniques that might reveal more than just your appearance. Imagine a world whe.

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AI TIME PhD：ACL 专场三

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基于神经网络模型的开放领域对话系统研究

参与这次由XuetangX提供的中文讲座，深入研究基于神经网络的开放领域对话系统。在此次课程中，您将探索自然语言处理的核心概念、深度学习架构以及能够实现与人类相似互动的会话式人工智能技术。这个课程是为希望提升自己在机器学习、深度学习及相关领域技能的学习者设计的。

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系列综述带你了解ChatGPT、AIGC和扩散模型

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人工智能技术与应用

This course is tailored for engineering management graduate students, integrating artificial intelligence theory, experiments, and engineering practice. Theoretical topics include an introduction to AI, knowledge representation and graphs, search strategies, genetic algorithms, swarm intelligence, neural networks, machine learning, deep learni.

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AI TIME：你的KDD小课堂

Welcome to AI TIME: 你的KDD小课堂, a comprehensive course offered by XuetangX, where you can delve into the cutting-edge fields of Artificial Intelligence, Machine Learning, and Data Mining. Enhance your knowledge and skills through meticulously crafted content, tailored to turn complex concepts into approachable, engaging lessons. Join a commun.

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语义解析漫谈：机会与挑战

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初探人工智能

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AI TIME PhD Debate-7：因果推理的应用与发展

Engage in the AI TIME PhD Debate-7, centered around the application and advancement of causal reasoning. Presented by XuetangX, this event delves into crucial topics within artificial intelligence and its interplay with causality. Whether you're a student, researcher, or practitioner, this debate offers a platform to gain insights from pioneeri.

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AI TIME：深度推荐系统的探索与实践

AI TIME: 深度推荐系统的探索与实践 Delve into the world of deep recommendation systems with our in-depth lecture conducted in Chinese, brought to you by XuetangX. This course offers a comprehensive exploration of both the theoretical underpinnings and practical implementations of recommendation algorithms. Engage with key concepts and examine how.

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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!