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课程大纲
Neural Networks 简介
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
机器学习与 Python
- 库的选择
- 附加工具
机器学习概念和应用
回归
- 线性回归
- 泛化和非线性
- 使用案例
分类
- 贝叶斯复习
- 朴素贝叶斯
- 逻辑回归
- K-最近邻
- Use Case秒
交叉验证和重采样
- 交叉验证方法
- 启动
- Use Case秒
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
NLP方法简介
- 单词和句子标记化
- 文本分类
- 情绪分析
- 拼写更正
- 信息提取
- 解析
- 意义提取
- 问答
人工智能 & Deep Learning
技术概述
- R 与秒 Python
- Caffe 与 Tensor Flow
- 各种机器学习库
行业案例研究
要求
- 应具备业务运营的基本知识和技术知识
- 必须对软件和系统有基本的了解
- 基本了解 Statistics(在 Excel 级别中)
21 小时
客户评论 (1)
The enthusiasm to the topic. The examples he made an he explained it very well. Sympatic. A little to detailed for beginners. For managers, it could be more abstract in fewer days. But it was designed to fit and we had a good alignment in advance.