了解 Python 编程语言。建议基本熟悉统计学和线性代数。
本课程的目的是提供在实践中应用机器学习方法的一般熟练程度。通过使用 Python 编程语言及其各种库, 并基于大量的实际示例, 本课程教授如何使用机器学习最重要的构建块, 如何做出数据建模决策, 解释输出并验证结果
我们的目标是让您能够自信地理解和使用机器学习工具箱中最基本的工具, 并避免数据科学应用的常见陷阱。
应用材料简介 Machine Learning
- 统计学习 vs. 机器学习
- 迭代与评估
- 偏差-方差权衡
监督学习和无监督学习
- Machine Learning Languages, 类型与范例
- 监督学习 vs Unsupervised Learning
监督学习
- 决策树
- Random Forests
- 模型评估
机器学习与 Python
- 函式库选择
- 附加工具
回归
- 线性回归
- 泛化与非线性
- 练习
分类
- 贝叶斯复习
- 朴素贝叶斯
- 逻辑回归
- K-近邻算法
- 练习
交叉验证和重采样
- 交叉验证方法
- Bootstrap
- 练习
无监督学习
- K-均值聚类
- 范例
- 无监督学习的挑战与超越K-均值
神经网路
- 层与节点
- Python 神经网路函式库
- 使用scikit-learn
- 使用PyBrain
- Deep Learning
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