数据集是训练深度学习模型的 “粮食”,没有数据,模型就无从学习。我们可以从公开的数据平台获取数据集,比如 Kaggle,这里有各种各样的数据集,涵盖了图像、文本、医疗等多个领域。获取到数据集后,还需要对数据进行预处理,比如数据清洗、归一化等。以图像数据集为例,我们可能需要将图像的大小调整为统一尺寸,将像素值...
kaggle.com/learn/python 4小时入门机器学习: kaggle.com/learn/machin 4小时了解深度学习: kaggle.com/learn/deep-l 3小时喜提SQL: kaggle.com/learn/sql 4小时get Pandas: kaggle.com/learn/pandas 7小时搞懂数据可视化: kaggle.com/learn/data-v 以上课程汇总: kaggle.com/learn/overvi 值得先码后看,祝你...
前些天报名参加了 Kaggle 的 Data Cleaning 5天挑战,5天的任务如下: Day 1: Handling missing values Day 2: Data scaling and normalization Day 3: Cleaning and parsing dates Day 4: Fixing encoding errors (no more messed up text fields!) Day 5: Fixing inconsistent data entry & spelling errors ...
train1_x, test1_x, train1_y, test1_y = model_selection.train_test_split(data1[data1_x_calc], data1[Target], random_state = 0) train1_x_bin, test1_x_bin, train1_y_bin, test1_y_bin = model_selection.train_test_split(data1[data1_x_bin], data1[Target] , random_state = 0...
KaggleKaggle is a platform for data science competitions with an aim to solve problems, recruit strong teams, and amplify the power of the data science talent.0 articles R Project Python Categories Introduction Getting Data Data Management Visualizing Data Basic Statistics Regression Models Advanced ...
print(data.info()) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 通过以上操作,可以确定数据中是否有缺失值、异常数据,了解各列的分布特征。 3. 缺失值与异常值处理 数据清洗是一个关键步骤,对缺失值或异常值的处理会直接影响模型的效果。常见处理方法有: ...
data-scienceexploratory-data-analysisedadata-visualizationkaggle-competitiondata-analyticsdata-analysisdata-wranglingdata-cleaningkaggle-datasetdata-cleansingdata-science-pythondata-analysis-pythonkaggle-used-cars-dataset UpdatedJan 2, 2019 Jupyter Notebook ...
Gain the skills you need to do independent data science projects. newsstand Courses We pare down complex topics to their key practical components, so you gain usable skills in a few hours (instead of weeks or months). The courses are provided at no cost to you, and you can now earn cert...
Machine learning is one of the most sought-after skills in today's job market. By completing this Track, you'll be well-prepared to: Apply for machine learning scientist positions across industries Collaborate with data science teams to solve complex problems Participate in Kaggle competitions and...
该数据采用Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0)许可,可以用于商业目的。 为了使用方便,我使用了Kaggle上根据Meteostat JSON API整合好的parquet数据集。 我使用的是pandas 2.0版本,该版本的pandas.read_parquet方法可以很方便地读取parquet数据。但是读取之前需要安装...