Course Code: mlfunpython
Duration: 14 hours
Prerequisites:

了解 Python 编程语言。建议基本熟悉统计学和线性代数。

Overview:

该课程的目的是提供在实践中应用方法的基本技能。 通过使用 Python 编程语言和其各种图书馆,并基于许多实用的例子,该课程教导如何使用最重要的建筑区块,如何做出数据模拟决策,解释算法的结果,并验证结果。

我們的目標是為您提供理解和使用最基本的工具工具的技能,以信心,並避免常見的錯誤的應用程式。

Course Outline:

应用材料简介 Machine Learning

  • 统计学习与机器学习
  • 迭代和评估
  • 偏差-方差权衡

使用 Python 进行机器学习

  • 库的选择
  • 附加工具

回归

  • 线性回归
  • 泛化和非线性
  • 习题

分类

  • 贝叶斯复习
  • 朴素贝叶斯
  • 逻辑回归
  • K-最近邻
  • 习题

交叉验证和重采样

  • 交叉验证方法
  • Bootstrap
  • 习题

无监督学习

  • K-means 聚类
  • 例子
  • 无监督学习和超越 K 均值的挑战
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