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线性代数的本质https://www.3blue1brown.com/ 可汗学院:统计学和概率论https://www.khanacademy.org/math/statistics-probability 2.编程(Python与库) Python基础(数据结构、循环、函数) 学习机器学习库(如Numpy、Pandas、Scikit-learn) 熟悉TensorFlow和Keras 资源: Coursera课程:Python for Everyonehttps://www.cou...
Continually updated data science Python notebooks: Spark, Hadoop MapReduce, HDFS, Kaggle, deep learning (TensorFlow, Theano, Caffe), scikit-learn, matplotlib, pandas, NumPy, SciPy, AWS, and various command lines. https://bit.ly/data-notes Resources Readme License View license Releases No ...
Code and documentation for the winning solution at the BCI Challenge @ NER 2015 : https://www.kaggle.com/c/inria-bci-challenge - tomzhang/bci-challenge-ner-2015
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1.了解Kaggle:注册账号,浏览 Kaggle,熟悉竞赛类型。 2.基础统计知识:学习描述性统计、概率分布、假设检验。 3. 编程语言:选择Python或R,掌握基本语法和数据结构。 4. 数据清洗与 EDA:使用 Pandas、Numpy、Matplotlib、Seaborn。 5.机器学习基础:理解监督学习、无监督学习、强化学习。
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blaze:NumPy 和 Pandas 的大数据接口。链接 Open Mining:使用 Python 挖掘商业情报 (BI) (Pandas web 接口)。链接 orange:通过可视化编程或 Python 脚本进行数据挖掘,数据可视化,分析和机器学习。链接 Pandas:提供高性能,易用的数据结构和数据分析工具。链接 --强烈推荐 facets 机器学习数据集可视化 --推荐 ...
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1 The YouTube channel associated with this code walks you through the entire process of learning to code in Python; all the way from basics to advanced machine learning and deep learning. The primary emphasis will be...
包括 IPython、Jupyter、NumPy、Pandas、Matplotlib、Scikit-Learn 在处理和存储数据上的应用。 3、《Python高级编程》清华大学出版社 稍微进阶型的书籍,在语法基础夯实之后,学习 Python 高级语言,实现复杂功能。包含了函数、如何应用装饰器、上下文管理器与生成器、对象模型、字符串、数据等内容。适用与对 Python 有一定...