Machine Learning courses

1795 Courses

人工智能

《人工智能》英文版慕课从教学实际需求出发,面向学生国际化发展目标,教学内容紧紧围绕人工智能的基本思想、基本理论、基本方法及其应用展开,融合前沿内容与典型应用。具体包括:智能体、知识表示、知识推理、搜索、进化计算、群体智能、人工神经网络、机器学习、深度学习和专家系统等基本理论与实用方法。了解新兴应用等前沿内容,重点介绍人工智能的核心思想、基本理论,.
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IA como Ferramenta de Aprendizagem e Pesquisa

O curso IA como Ferramenta de Aprendizagem e Pesquisa aborda uma visão abrangente sobre a IA, com enfoque especial na sua aplicação como ferramenta de aprendizagem e de pesquisa, preparando os estudantes para navegar e aproveitar as oportunidades que essa tecnologia revolucionária oferece.
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AI and Machine Learning in Cybersecurity

Explore this fascinating field and develop the skills to leverage AI in your cybersecurity challenges. In this course, AI and Machine Learning in Cybersecurity, you’ll learn to use AI to leverage your cybersecurity skills. First, you’ll explore machine learning and anomaly detection. Next, you’ll discover AI cybersecurity strengths like ransomware,.

Enhancing Traditional ML with Generative AI

As AI evolves, professionals are challenged with understanding how emerging Generative AI capabilities can coexist with traditional machine learning approaches. In this course, Enhancing Traditional ML with Generative AI, you’ll learn when to use these technologies and how they compliment each other. First, you’ll explore the differences and synerg.

Introduction to Generative AI vs. Traditional ML

Understanding the difference between traditional machine learning and generative AI is crucial for making informed decisions about AI implementation. In this course, Introduction to Generative AI vs. Traditional ML, you’ll learn to evaluate, compare, and choose the best AI approach for your projects. First, you’ll explore the fundamentals of tradit.

AWS Certified Machine Learning Engineer - Associate (MLA-C01): ML Model Development

AWS has a broad range of machine learning services to help businesses approach complex problems. In this course, AWS Certified Machine Learning Engineer - Associate (MLA-C01): ML Model Development, you’ll learn to navigate these services and select the approach that is most appropriate for your machine learning (ML) solutions. First, you’ll explore.

AIOps: CI/CD for AI Systems

DevOps is a concept that has been around a long time and has been applied to ML and, recently, LLMs. In this course, AIOps: CI/CD for AI Systems, you’ll learn to apply CI/CD, a fundamental concept of DevOps, to ML, AI, and LLMs. First, you’ll explore how CI/CD pipelines work with ML models. Next, you’ll discover how to deploy to cloud environments.

AWS AI Practitioner

The AWS Certified AI Practitioner Specialization is meant for those who desire to establish a solid base in Artificial Intelligence (AI) and Machine Learning (ML) and have the ability to utilize AWS cloud services. This specialization is aligned with the AWS AI Practitioner Certification exam and offers a broad understanding of AI concepts, generat.
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Q-Learning Unleashed: Building Intelligent Agents

In this course, we focus on building a Q-learning agent step by step. We start with the Bellman equation and the Q-table update, then implement a basic Q-learning function. Next, we incorporate an exploration policy (ε-greedy), and finally we demonstrate how to use the learned Q-table for decision making.
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Azure AI Services: Core applications

"Core Applications of Azure AI Services" is an introductory course designed to familiarize users with the practical application of Microsoft's Azure AI services in various real-world scenarios. It aims to bridge the gap between theoretical AI concepts and their actual implementation. You will learn how Azure AI services are utilized to solve real-.
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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!