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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ml-foundations
课程评论：没有评论
课程名称：机器学习基础：案例研究方法 课程概述： 您是否拥有数据并想了解它能告诉您的信息？您是否需要更深入地理解机器学习在改善业务方面的核心方式？您想能够与专家交流，从回归、分类到深度学习和推荐系统的各种话题吗？本课程将通过一系列实际案例研究为您提供机器学习的实践经验。在第一个课程结束时，您将学习如何根据房屋特征预测房价，从用户评价中分析情感，检索相关文档，推荐产品和搜索图像。通过这些用例的实践，您将能够在各种领域应用机器学习方法。 本课程将机器学习方法视为一个黑箱，您将重点理解感兴趣的任务，将这些任务与机器学习工具匹配，并评估输出质量。在后续课程中，您将深入探讨这个黑箱的组成部分，研究模型和算法。这些组成部分构成了机器学习管道，您将利用这些构件开发智能应用。 学习目标： 完成本课程后，您将能够： - 确定机器学习在实践中的潜在应用。 - 描述回归、分类和聚类分析的核心区别。 - 为潜在应用选择合适的机器学习任务。 - 应用回归、分类、聚类、检索、推荐系统和深度学习。 - 将数据表示为特征，以作为机器学习模型的输入。 - 根据每个任务的相关误差指标评估模型质量。 - 利用数据集拟合模型以分析新数据。 - 构建一个以机器学习为核心的端到端应用。 - 在 Python 中实现这些技术。 课程大纲： 1. 欢迎 - 机器学习无处不在，但往往在幕后的操作。本课程的介绍将为您提供机器学习的力量和您将能够开发和部署的众多智能应用的洞察。 2. 回归：预测房价 - 本周，您将构建第一个智能应用程序，根据输入特征（如建筑面积、卧室和浴室数量等）预测连续值（房价）。您将分析预测模型的性能，并在 Jupyter Notebook 中实现回归。 3. 分类：分析情感 - 在第二个案例研究中，您将构建模型，根据用户提供的评论文本和用户资料信息预测情感（正面/负面）。您将分析分类器的准确性并在 Jupyter Notebook 中实现它。 4. 聚类与相似性：文档检索 - 第三个案例研究中，您将探讨文档表示和检索算法，构建一个智能文档检索系统，以查找类似的摘要。 5. 产品推荐 - 学习如何构建基于协同过滤的推荐系统，使用矩阵分解等技术在 Jupyter Notebook 中实现真实的产品推荐系统。 6. 深度学习：搜索图像 - 了解深度学习在图像分类和检索任务中的能力，并在 Jupyter Notebook 中构建图像分类器和智能图像检索系统。 7. 结束语 - 本课程总结机器学习工具转化为服务的部署阶段，并讨论机器学习面临的开放挑战以及未来的发展方向。 通过本课程，您将掌握机器学习的基础，并能够在多个领域中应用这些技术。
Name:Welcome
Description:Machine learning is everywhere, but is often operating behind the scenes.
This introduction to the specialization provides you with insights into the power of machine learning, and the multitude of intelligent applications you personally will be able to develop and deploy upon completion.
We also discuss who we are, how we got here, and our view of the future of intelligent applications.Name:Regression: Predicting House Prices
Description:This week you will build your first intelligent application that makes predictions from data.
We will explore this idea within the context of our first case study, predicting house prices, where you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...).
This is just one of the many places where regression can be applied.Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.
You will also examine how to analyze the performance of your predictive model and implement regression in practice using a Jupyter notebook.Name:Classification: Analyzing Sentiment
Description:How do you guess whether a person felt positively or negatively about an experience, just from a short review they wrote?
In our second case study, analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...).This task is an example of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification.
You will analyze the accuracy of your classifier, implement an actual classifier in a Jupyter notebook, and take a first stab at a core piece of the intelligent application you will build and deploy in your capstone.Name:Clustering and Similarity: Retrieving Documents
Description:A reader is interested in a specific news article and you want to find a similar articles to recommend. What is the right notion of similarity? How do I automatically search over documents to find the one that is most similar? How do I quantitatively represent the documents in the first place?
In this third case study, retrieving documents, you will examine various document representations and an algorithm to retrieve the most similar subset. You will also consider structured representations of the documents that automatically group articles by similarity (e.g., document topic).
You will actually build an intelligent document retrieval system for Wikipedia entries in an Jupyter notebook.Name:Recommending Products
Description:Ever wonder how Amazon forms its personalized product recommendations? How Netflix suggests movies to watch? How Pandora selects the next song to stream? How Facebook or LinkedIn finds people you might connect with? Underlying all of these technologies for personalized content is something called collaborative filtering.
You will learn how to build such a recommender system using a variety of techniques, and explore their tradeoffs.
One method we examine is matrix factorization, which learns features of users and products to form recommendations. In a Jupyter notebook, you will use these techniques to build a real song recommender system.Name:Deep Learning: Searching for Images
Description:You’ve probably heard that Deep Learning is making news across the world as one of the most promising techniques in machine learning. Every industry is dedicating resources to unlock the deep learning potential, including for tasks such as image tagging, object recognition, speech recognition, and text analysis.
In our final case study, searching for images, you will learn how layers of neural networks provide very descriptive (non-linear) features that provide impressive performance in image classification and retrieval tasks. You will then construct deep features, a transfer learning technique that allows you to use deep learning very easily, even when you have little data to train the model.
Using iPhython notebooks, you will build an image classifier and an intelligent image retrieval system with deep learning.Name:Closing Remarks
Description:In the conclusion of the course, we will describe the final stage in turning our machine learning tools into a service: deployment.
We will also discuss some open challenges that the field of machine learning still faces, and where we think machine learning is heading. We conclude with an overview of what's in store for you in the rest of the specialization, and the amazing intelligent applications that are ahead for us as we evolve machine learning.
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.
