原文地址:learning-path-data-science-python。 从Python菜鸟到Python Kaggler的旅程(译注:Kaggle是一个数据建模和数据分析竞赛平台) 假如你想成为一个数据科学家,或者已经是数据科学家的你想扩展你的技能,那么你已经来对地方了。本文的目的就是给数据分析方面的Python新手提供一个完整的学习路径。该路径提供了你需要学...
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...
for dataset in combine: for i in range(0, 2): for j in range(0, 3): guess_df = dataset[(dataset['Sex'] == i) & (dataset['Pclass'] == j+1)]['Age'].dropna() age_guess = guess_df.median() guess_ages[i, j] = int(age_guess/0.5 + 0.5) * 0.5 for i in range(0,...
这里(https://machinelearningmastery.com/best-machine-learning-resources-for-getting-started/)不仅给大家列出了一些很不错的机器学习的免费资源,还提供了很多其他指导和教程。由于兴趣爱好的不同,你会发现网上有很多可用的开源数据集。但是在刚开始学的时候,Kaggle (https://www.kaggle.com)维护的数据集,和那些政...
Python is one of the most popular programming languages used across various tech disciplines, especially in data science and machine learning. Python offers an easy-to-code, object-oriented, high-level language with a broad collection of libraries for a multitude of use cases. It has over 137,...
Python 在解决数据科学任务和挑战方面继续处于领先地位。去年,我们曾发表一篇博客文章 Top 15 Python Libraries for Data Science in 2017,概述了当时业已证明最有帮助的Python库。今年,我们扩展了这个清单,增加了新的Python库,并重新审视了去年已经讨论过的 Python 库,重点关注了这一年来的更新。
5. **Kaggle**：Kaggle是一个面向数据科学家和机器学习爱好者的竞赛和协作平台。在Kaggle上，你可以找到许多数据科学竞赛和项目，其中很多参与者会分享他们的代码和解决方案。6. **Medium博客**：Medium是一个知名的博客平台，许多数据科学家和程序员在上面发布了有关数据科学和Python编程的文章。你可以搜索关键词，...
Python’s simplicity, readability, and massive ecosystem of libraries make it a prime choice for tackling everything from exploratory data analysis to machine learning. Below is a quick roadmap to help you begin your Python-for-Data-Science journey and keep things fun along the way! 1. ...
imbalanced-learn - Resampling for imbalanced datasets. tspreprocess - Time series preprocessing: Denoising, Compression, Resampling. Kaggler - Utility functions (OneHotEncoder(min_obs=100)) skrub - Bridge the gap between tabular data sources and machine-learning models. Noisy Labels cleanlab - Machine...
大家好,本次笔记是Datawhale主办的AI夏令营中,AI For Science的*生命科学赛道——生物学年龄评价与年龄相关疾病风险预测*中总结的笔记,内容针对如何处理大数据集 参考资料 https://www.kaggle.com/code/rohanrao/tutorial-on-reading-large-datasets/notebook ...