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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/wharton-ai-fundamentals-non-data-scientists
课程评论：没有评论
课程名称：非数据科学家的人工智能基础 课程概述：在本课程中，您将深入探索机器学习如何处理和解释大数据。您将详细了解创建算法的各种方法，以便将其纳入您的业务中，并使用如Teachable Machine和TensorFlow等工具。您还将学习不同的机器学习方法、深度学习的基本概念及其局限性，了解如何提高算法的准确性以及如何使用最佳训练数据。随后，您将探讨生成对抗网络（GANs）和变分自编码器（VAEs），利用您新获得的知识与AutoML互动，帮助您构建满足您需求的算法。此外，您还将欣赏到来自行业领袖的独家访谈，他们在麦当劳和Visa等公司的大数据管理中具有丰富经验。通过本课程的学习，您将掌握多种编码方式，包括如何使用无代码工具，理解深度学习，如何测量和评估算法中的错误，以及如何使用大数据来维护客户隐私，同时利用这些数据制定推动业务发展的不同策略。 大纲： 1. 模块1 – 大数据与人工智能 本模块将介绍大数据，并探讨机器学习在各个业务领域中的应用。您将学习数据是如何分析和提取的，以及数字技术如何扩展和转型企业。模块结束时，您将了解到机器学习作为通用技术的应用及数据挖掘的一些最佳技术和实践。 2. 模块2 – 训练与评估机器学习算法 在本模块中，您将深入了解对比机器学习方法，包括逻辑回归和神经网络，并学习深度学习及其与神经网络的关系。您还将了解损失函数，以及如何有效测量和审查算法中的错误，以保持算法的完整性。 3. 模块3 – 机器学习应用与新兴方法 本模块将聚焦于自然语言处理中的机器学习应用，以及使用生成建模创建新数据的技术。您还将研究AutoML，并了解如何有效利用自动化流程来提高算法效率。您将能够在实践中使用Teachable Machine进行无代码解决方案的算法构建。 4. 模块4 - 行业访谈 本模块将与行业领袖进行交流，深入了解数据抽样和构建可用模型的经验。您将听取来自麦当劳全球菜单战略与营销副总裁Ed Lee的见解，了解成功品牌如何处理数据问题。通过这一模块，您将获得关于大数据如何维护隐私及其在市场营销和内容优化中的重要性的一手知识。
Part: 1
Title:Module 1 – Big Data and Artificial Intelligence
Description:In this module, you will be introduced to Big Data and examine how machine learning is used throughout various business segments. You will also learn how data is analyzed and extracted, and how digital technologies have been used to expand and transform businesses. You will also get a detailed look at data management tools and how they are best implemented and the value of data warehouses. By the end of this module, you will have gained insight into how machine learning can be used as a general-purpose technology, and some best techniques and practices for data mining.
Part: 2
Title:Module 2 – Training and Evaluating Machine Learning Algorithms
Description:In this module, you will get an in-depth look at contrasting Machine Learning methods, including logistic regression and neural nets. You will also learn about Deep Learning and its relationship to neural networks and how to best optimize Machine Learning algorithms. Lastly, you will be introduced to loss functions and how to best measure and review errors to maintain the integrity of your algorithms. By the end of this module, you will have learned about Machine Learning methods, the limitations and value of Deep Learning, how best to drive precision and accuracy in algorithms, and how to get the best training data for those algorithms.
Part: 3
Title:Module 3 – ML Application and Emerging Methods
Description:In this module, you will take a look at Machine Learning within natural language processing and using generative modeling to create new data. You will also focus on AutoML and how to best utilize automated processes to make your algorithms more efficient. You will also review the no-code Machine Learning tool Teachable Machine, which serves to make Deep and Machine Learning more accessible. By the end of this module, you will be able to use AutoML in your algorithms and be able to navigate and use Teachable Machine in practice for no-code solutions to building an algorithm.
Part: 4
Title:Module 4 - Industry Interview
Description:In this module, you will hear from an industry leader and gain valuable insight into data sampling and building realistic usable models. Ed Lee, VP of Global Menu Strategy & Global Marketing at McDonald's, will allow you to review real-world solutions and how they handle data issues as one of the most successful global brands. By the end of this module, you will have heard from a top industry expert in their field and gained firsthand knowledge and understanding of how Big Data plays into maintaining privacy in data and also utilizing that data to enhance your marketing, content, and refine your algorithms.
In this course, you will go in-depth to discover how Machine Learning is used to handle and interpret Big Data. You will get a detailed look at the various ways and methods to create algorithms to incorporate into your business with such tools as Teachable Machine and TensorFlow. You will also learn different ML methods, Deep Learning, as well as the limitations but also how to drive accuracy and use the best training data for your algorithms. You will then explore GANs and VAEs, using your newfound knowledge to engage with AutoML to help you start building algorithms that work to suit your needs. You will also see exclusive interviews with industry leaders, who manage Big Data for companies such as McDonald's and Visa. By the end of this course, you will have learned different ways to code, including how to use no-code tools, understand Deep Learning, how to measure and review errors in your algorithms, and how to use Big Data to not only maintain customer privacy but also how to use this data to develop different strategies that will drive your business.
