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Title: GitHub - lcdevelop/MachineLearningCourse: 机器学习精简入门教程

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X Title: GitHub - lcdevelop/MachineLearningCourse: 机器学习精简入门教程

Description: 机器学习精简入门教程. Contribute to lcdevelop/MachineLearningCourse development by creating an account on GitHub.

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https://patch-diff.githubusercontent.com/lcdevelop/MachineLearningCourse#机器学习精简入门教程
机器学习教程 一-不懂这些线性代数知识 别说你是搞机器学习的https://blog.codemeteors.com/tutorial/1
机器学习教程 二-安装octave绘制3D函数图像https://blog.codemeteors.com/tutorial/28
机器学习教程 三-用scikit-learn求解一元线性回归问题https://blog.codemeteors.com/tutorial/53
机器学习教程 四-用scikit-learn求解多元线性回归问题https://blog.codemeteors.com/tutorial/54
机器学习教程 五-用matplotlib绘制精美的图表https://blog.codemeteors.com/tutorial/55
机器学习教程 六-用scikit-learn求解多项式回归问题https://blog.codemeteors.com/tutorial/56
机器学习教程 七-用随机梯度下降法(SGD)做线性拟合https://blog.codemeteors.com/tutorial/57
机器学习教程 八-用scikit-learn做特征提取https://blog.codemeteors.com/tutorial/58
机器学习教程 九-二元分类效果的评估方法https://blog.codemeteors.com/tutorial/59
机器学习教程 十-用scikit-learn的网格搜索快速找到最优模型参数https://blog.codemeteors.com/tutorial/60
机器学习教程 十一-用scikit-learn做聚类分析https://blog.codemeteors.com/tutorial/61
机器学习教程 十二-神经网络模型的原理https://blog.codemeteors.com/tutorial/91
机器学习教程 十三-用scikit-learn做逻辑回归https://blog.codemeteors.com/tutorial/93
机器学习教程 十四-利用tensorflow做手写数字识别https://blog.codemeteors.com/tutorial/94
机器学习教程 十五-细解卷积神经网络https://blog.codemeteors.com/tutorial/95
机器学习教程 十六-深究熵的概念和公式以及最大熵原理https://blog.codemeteors.com/tutorial/98
机器学习教程 十七-逻辑回归公式的数学推导https://blog.codemeteors.com/tutorial/102
机器学习教程 十八-R语言特征工程实战https://blog.codemeteors.com/tutorial/106
机器学习教程 十九-看数据科学家是如何找回丢失的数据的（一）https://blog.codemeteors.com/tutorial/107
机器学习教程 二十-看数据科学家是如何找回丢失的数据的（二）https://blog.codemeteors.com/tutorial/108
机器学习教程 二十一-R语言炫技必备基本功https://blog.codemeteors.com/tutorial/109
机器学习教程 二十二-一小时掌握R语言数据可视化https://blog.codemeteors.com/tutorial/110
机器学习教程 二十三-R语言强大工具包ggplot绘图以外的那些事https://blog.codemeteors.com/tutorial/111
机器学习教程 二十四-[吐血整理]涉及面最广的机器学习资料大全https://blog.codemeteors.com/tutorial/123
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