Regression Analysis with Yellowbrick
79 個評分

3,100 人已註冊
Evaluate the performance of regression models using visual diagnostic tools from Yellowbrick
Use visualization techniques to steer your machine learning workflow
79 個評分
3,100 人已註冊
Evaluate the performance of regression models using visual diagnostic tools from Yellowbrick
Use visualization techniques to steer your machine learning workflow
Welcome to this project-based course on Regression Analysis with Yellowbrick. In this project, we will build a machine learning model to predict the compressive strength of high performance concrete (HPC). Although, we will use linear regression, the emphasis of this project will be on using visualization techniques to steer our machine learning workflow. Visualization plays a crucial role throughout the analytical process. It is indispensable for any effective analysis, model selection, and evaluation. This project will make use of a diagnostic platform called Yellowbrick. It allows data scientists and machine learning practitioners to visualize the entire model selection process to steer towards better, more explainable models.Yellowbrick hosts several datasets from the UCI Machine Learning Repository. We’ll be working with the concrete dataset that is well suited for regression tasks. The dataset contains 1030 instances and 8 real valued attributes with a continuous target. We we will cover the following topics in our machine learning workflow: exploratory data analysis (EDA), feature and target analysis, regression modelling, cross-validation, model evaluation, and hyperparamter tuning. 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
Machine Learning
Python Programming
Data Visualization (DataViz)
Scikit-Learn
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:
Data Exploration
Preprocessing the Data
Pairwise Scatterplot
Feature Importances
Target Visualization
Evaluating Lasso Regression
Visualizing Test Set Errors
Cross Validation Scores
Learning Curves
Hyperparamter Tuning - Alpha Selection
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
由 SK 提供
2020年5月11日Great Instructor.
Good Platform for Learning as well as Practice side by side.
Thank you for sharing your knowledge.
由 TT 提供
2021年7月4日Good introduction to Yellowbrick. The audio is not as good as other projects.
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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。
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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。
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。
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