Just For My Daily Diary
Just For My Daily Diary
Introduction 介绍 In this micro-course, you’ll learn all about pandas, the most popular Python library for data analysis. 在这个微课程中，您将了解有关pandas的所有信息，这是最流行的数据分析Python库。 Along the way, you’ll complete several hands-on exercises with real-world data. We recommend that you work on the exercises while reading the corresponding tutorials. 在此过程中，您将使用真实数据完成一些实践练习。 我们建议您在阅读相应教程的同时做练习。 To start the first exercise, please click here. 要开始第一个练习，请单击此处。 In […]
This notebook is an exercise in the Introduction to Machine Learning course. You can reference the tutorial at this link. Introduction 介绍 In this exercise, you will create and submit predictions for a Kaggle competition. You can then improve your model (e.g. by adding features) to improve and see how you stack up to others […]
Machine learning competitions are a great way to improve your data science skills and measure your progress. 机器学习竞赛是提高数据科学技能和衡量进步的好方法。 Your Turn 到你了 Use what you’ve learned in the course to create a submission to a Kaggle competition! 使用您在课程中学到的知识创建提交到 Kaggle 竞赛！ In the next exercise, you will create and submit predictions for the House Prices Competition for […]
Machine learning competitions are a great way to improve your data science skills and measure your progress. 机器学习竞赛是提高数据科学技能和衡量进步的好方法。 Your Turn 到你了 Use what you’ve learned in the course to create a submission to a Kaggle competition! 使用您在课程中学到的知识创建提交到 Kaggle 竞赛！ In the next exercise, you will create and submit predictions for the House Prices Competition for […]
Machine learning competitions are a great way to improve your data science skills and measure your progress. 机器学习竞赛是提高数据科学技能和衡量进步的好方法。 Your Turn 到你了 Use what you’ve learned in the course to create a submission to a Kaggle competition! 使用您在课程中学到的知识创建提交到 Kaggle 竞赛！ In the next exercise, you will create and submit predictions for the House Prices Competition for […]
This notebook is an exercise in the Introduction to Machine Learning course. You can reference the tutorial at this link. Recap 回顾 Here’s the code you’ve written so far. 这是您到目前为止编写的代码。 # Code you have previously used to load data import pandas as pd from sklearn.metrics import mean_absolute_error from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeRegressor […]
Introduction 介绍 Decision trees leave you with a difficult decision. A deep tree with lots of leaves will overfit because each prediction is coming from historical data from only the few houses at its leaf. But a shallow tree with few leaves will perform poorly because it fails to capture as many distinctions in the […]
This notebook is an exercise in the Introduction to Machine Learning course. You can reference the tutorial at this link. Recap You’ve built your first model, and now it’s time to optimize the size of the tree to make better predictions. Run this cell to set up your coding environment where the previous step left […]
At the end of this step, you will understand the concepts of underfitting and overfitting, and you will be able to apply these ideas to make your models more accurate. 在此步骤结束时，您将了解欠拟合和过拟合的概念，并且您将能够应用这些想法使您的模型更加准确。 Experimenting With Different Models 尝试不同的模型 Now that you have a reliable way to measure model accuracy, you can experiment with alternative models and see […]
This notebook is an exercise in the Introduction to Machine Learning course. You can reference the tutorial at this link. Recap You’ve built a model. In this exercise you will test how good your model is. Run the cell below to set up your coding environment where the previous exercise left off. 回顾 你已经建立了一个模型。 在本练习中，您将测试您的模型有多好。 […]