Analyze Text Data with Yellowbrick
82 個評分

4,812 人已註冊
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
82 個評分
4,812 人已註冊
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
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由 AH 提供
2020年4月13日It was an amazing test and this lecture i like same with my area teaching.
由 KL 提供
2021年4月1日Could have run through the theory behind the library functions a bit more as a refresher but for brevity's sake it is alright the instructor did not.
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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。
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