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Use visual diagnostic tools from Yellowbrick to steer your machine learning workflow
Vectorize text data using TF-IDF
Cluster documents using embedding techniques and appropriate metrics
Welcome to this project-based course on Analyzing Text Data with Yellowbrick. Tasks such as assessing document similarity, topic modelling and other text mining endeavors are predicated on the notion of "closeness" or "similarity" between documents. In this course, we define various distance metrics (e.g. Euclidean, Hamming, Cosine, Manhattan, etc) and understand their merits and shortcomings as they relate to document similarity. We will apply these metrics on documents within a specific corpus and visualize our results. By the end of this course, you will be able to confidently use visual diagnostic tools from Yellowbrick to steer your machine learning workflow, vectorize text data using TF-IDF, and cluster documents using embedding techniques and appropriate metrics. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, Yellowbrick, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Data Science
Natural Language Processing
Machine Learning
Python Programming
Data Visualization (DataViz)
在與您的工作區一起在分屏中播放的視頻中，您的授課教師將指導您完成每個步驟：
Introduction and Loading the Corpus
Vectorizing the Documents
Clustering Similar Documents with Squared Euclidean Distance And Euclidean Distance
Manhattan (aka “Taxicab” or “City Block”) Distance
Bray Curtis Dissimilarity and Canberra Distance
Cosine Distance
What Metrics Not to Use
Omitting Class Labels - Using KMeans Clustering
您的工作空間就是瀏覽器中的雲桌面，無需下載
在分屏視頻中，您的授課教師會為您提供分步指導
購買指導項目後，您將獲得完成指導項目所需的一切，包括通過 Web 瀏覽器訪問云桌面工作空間，工作空間中包含您需要了解的文件和軟件，以及特定領域的專家提供的分步視頻說明。
由於您的工作空間包含適合筆記本電腦或台式計算機使用的雲桌面，因此指導項目不在移動設備上提供。
指導項目授課教師是特定領域的專家，他們在項目的技能、工具或領域方面經驗豐富，並且熱衷於分享自己的知識以影響全球數百萬的學生。
您可以從指導項目中下載並保留您創建的任何文件。為此，您可以在訪問云桌面時使用‘文件瀏覽器’功能。
指導項目不符合退款條件。 請查看我們完整的退款政策。
指導項目不提供助學金。
指導項目不支持旁聽。
您可在頁面頂部點按此指導項目的經驗級別，查看任何知識先決條件。對於指導項目的每個級別，您的授課教師會逐步為您提供指導。
是，您可以在瀏覽器的雲桌面中獲得完成指導項目所需的一切。
您可以直接在瀏覽器中於分屏環境下完成任務，以此從做中學。在屏幕的左側，您將在工作空間中完成任務。在屏幕的右側，您將看到有授課教師逐步指導您完成項目。
還有其他問題嗎？請訪問 學生幫助中心。
