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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ibm-ai-workflow-machine-learning-vr-nlp
课程评论：没有评论
课程名称：AI工作流程：机器学习、视觉识别与自然语言处理 课程概述： 该课程是IBM AI企业工作流程认证专业化的第四门课程。强烈建议按照顺序完成这些课程，因为它们并不是独立的课程，而是构成一个工作流程，每门课程都建立在前一门课程的基础上。第四门课程涵盖了一个假设的流媒体公司的模型设置及其相关数据管道的下一个阶段。首先要介绍的是复杂的评估指标主题，您将学习多种不同指标的最佳实践，包括回归指标、分类指标和多类指标，这些将用于选择解决商业挑战的最佳模型。接下来的主题则涵盖线性模型、树基模型和神经网络等不同模型的最佳实践。课程中将使用IBM Watson的自然语言理解和视觉识别模型。案例研究将集中于自然语言处理和图像分析，以提供模型管道的现实背景。 课程目标： 完成本课程后，您将能够： - 讨论常见的回归、分类和多标签分类指标 - 解释线性和逻辑回归在监督学习应用中的使用 - 描述网格搜索和交叉验证的常见策略 - 使用评估指标选择适合生产的模型 - 解释树基算法在监督学习中的应用 - 解释神经网络在监督学习中的应用 - 讨论神经网络的主要变体及其最新进展 - 在TensorFlow中创建神经网络模型 - 创建和测试Watson视觉识别实例 - 创建和测试Watson自然语言理解实例 适合人群： 本课程主要面向已有数据科学经验、具备构建机器学习模型能力的从业者，旨在加深他们在大型企业中构建和部署人工智能的技能。如果您是一名有志于成为数据科学家的初学者，本课程并不适合您，因为内容需要真实世界的专业知识。 先决技能： 学员需完成IBM AI企业工作流程专业化的前3门课程，并在开始本课程之前对以下主题有扎实的理解： - 线性代数的基本知识 - 采样、概率论和概率分布的理解 - 描述性和推断性统计概念的知识 - 对机器学习技术和最佳实践的普遍理解 - 对Python及数据科学常用包（如NumPy、Pandas、matplotlib、scikit-learn）的实践理解 - 熟悉IBM Watson Studio - 熟悉设计思维过程 课程大纲： 第1部分：模型评估与性能指标 本周关注模型选择、评估和性能指标，重点是迭代评估模型以寻求改进。您将调查评估指标和线性模型的全景，以确保您能熟练使用基准模型。在案例研究中，您将使用自然语言处理进行分类。完成模型迭代后，您将把模型性能与商业指标联系起来，以更好地理解模型的实用性。 第2部分：构建机器学习与深度学习模型 本周主要集中于构建监督学习模型。我们将调查机器学习中两种流行有效方法的可用方法：树基算法和深度学习算法。我们将覆盖随机森林、提升法等树基方法及其他集成方法，其中许多方法充当可解释的线性模型与难以解释的深度学习模型之间的重要中间层。对于深度学习，我们将使用预构建的视觉识别模型，并利用TensorFlow演示如何构建、调优和迭代神经网络。此外，您还将理解流行的神经网络架构。在案例研究中，您将实现卷积神经网络并为部署做好准备。
Part: 1
Title:Model Evaluation and Performance Metrics
Description:This week covers model selection, evaluation and performance metrics. The focus is on evaluating models iteratively for improvements. You will survey the landscape of evaluation metrics and linear models in order to ensure you are comfortable using implementing baseline models. The materials build up to the case study where you will use natural language processing in a classification setting. When you are done iterating on your model you will connect its model performance to business metrics as an approach to better understand model utility.
Part: 2
Title:Building Machine Learning and Deep Learning Models
Description:This week is primarily focused on building supervised learning models. We will survey available methods in two popular and effective areas of machine learning: Tree based algorithms and deep learning algorithms. We will cover the use of tree based methods like random forests and boosting along with other ensemble approaches. Many of these approaches serve as an important middle layer between interpretable linear models and difficult to interpret deep-learning models. For deep learning we will use a pre-built visual recognition model and use TensorFlow to demonstrate how to build, tune, and iterate on neural networks. We will also make sure that you understand popular neural network architectures. In the case study you will implement a convolutional neural network and ready it for deployment.
This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company. The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your business challenge. The next topics cover best practices for different types of models including linear models, tree-based models, and neural networks. Out-of-the-box Watson models for natural language understanding and visual recognition will be used. There will be case studies focusing on natural language processing and on image analysis to provide realistic context for the model pipelines. By the end of this course you will be able to: Discuss common regression, classification, and multilabel classification metrics Explain the use of linear and logistic regression in supervised learning applications Describe common strategies for grid searching and cross-validation Employ evaluation metrics to select models for production use Explain the use of tree-based algorithms in supervised learning applications Explain the use of Neural Networks in supervised learning applications Discuss the major variants of neural networks and recent advances Create a neural net model in Tensorflow Create and test an instance of Watson Visual Recognition Create and test an instance of Watson NLU Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 3 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
