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所在平台: Udemy |
课程主页: https://www.udemy.com/course/aws-machine-learning-a-complete-guide-with-python/
课程评论：没有评论
课程名称：AWS认证机器学习专业课程 MLS-C01 [2025] 课程概述：欢迎参加AWS机器学习专业课程！本课程将让您深入了解基于云的机器学习算法以及如何将其整合到您的应用程序中，同时为认证做好准备。我们将通过实践课程，利用AWS SageMaker进行动手学习、练习测试和认证准备。您将获得有关AWS SageMaker的实用经验，通过动手实验展示具体概念。 课程内容包括： - SageMaker环境的设置，适合机器学习新手，您将学习如何处理混合数据类型、缺失数据以及验证模型质量，这些都是机器学习从业者和认证考试的基础知识。 - SageMaker使用容器封装算法和框架，如Pytorch和TensorFlow，提供构建和部署模型的标准接口，便于将模型转化为生产应用程序。 - 通过一系列简明实验，您将訓練、部署和调用您的第一个SageMaker模型。 - 探讨如何在生产系统中安全地实施新更改，执行A/B测试，以及在必要时回滚更改而不会影响应用程序的运行。 - 讨论机器学习和AI系统的公平性问题，以及如何应对用户对模型可能存在的种族或性别偏见的指控，涵盖公平性概念、模型决策的解释、不同类型的偏见及其测量方法。 - 云安全以及如何保护您的数据和模型免受未授权使用。 - 推荐系统及如何整合电影和产品推荐等特性。学习最先进的算法并调优以适应您的数据集。 - 在时间序列预测、异常检测和构建自定义深度学习模型方面积累经验。 通过本课程获取的知识以及高质量的练习考试，您将为获得AWS认证机器学习专业认证做好充分准备。期待在课程中见到您，并帮助您取得成功！谢谢！
Learn about cloud based machine learning algorithms, how to integrate with your applications and Certification PrepWelcome to AWS Machine Learning Specialty Course!Experience AWS SageMaker: A Practical Course with Hands-On Learning, Practice Tests and Certification Preparation.***NEW: In this course, you will gain practical experience with AWS SageMaker through hands-on labs that demonstrate specific concepts. We will begin by setting up your SageMaker environment. If you are new to machine learning, you will learn how to handle mixed data types, missing data, and how to verify the quality of the model. These topics are essential for machine learning practitioners and the certification exam.SageMaker uses containers to package algorithms and frameworks, such as Pytorch and TensorFlow. The container-based approach provides a standard interface for building and deploying your models, and it is easy to convert your model into a production application. Through a series of concise labs, you will train, deploy, and invoke your first SageMaker model.Like any other software project, a machine-learning solution also requires continuous improvement. We will look at how to safely incorporate new changes in a production system, perform A/B testing, and even roll back changes when necessary, all with zero downtime to your application.We will also discuss emerging social trends in the fairness of machine learning and AI systems. What will you do if your users accuse your model of being racially or gender-biased? How will you handle it? In this section, we will cover the concept of fairness, how to explain a decision made by the model, different types of bias, and how to measure them.We will also cover cloud security and how to protect your data and model from unauthorized use. You will learn about recommender systems and how to incorporate features such as movie and product recommendations. The algorithms you learn in the course are state-of-the-art, and tuning them for your dataset can be challenging. We will look at how to tune your model with automated tools, and you will gain experience in time series forecasting, anomaly detection, and building custom deep-learning models.With the knowledge you gain in this course, and the included high-quality practice exam, you will be well-prepared to achieve the AWS Certified Machine Learning - Specialty certification. I am looking forward to meeting you and helping you succeed in this course. Thank you!
