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课程大纲
基础 Machine Learning
- Machine Learning 概念和工作流简介
- 监督学习与无监督学习
- 评估机器学习模型：指标和技术
贝叶斯方法
- 朴素贝叶斯和多项式模型
- 贝叶斯分类数据分析
- 贝叶斯图形模型
回归技术
- 线性回归
- Logistic 回归
- 广义线性模型 （GLM）
- 混合模型和增材模型
降维
- 主成分分析 （PCA）
- 因数分析 （FA）
- 独立成分分析 （ICA）
分类方法
- K 最近邻 （KNN）
- 用于回归与分类的支援向量机 （SVM）
- 提升和整合模型
Neural Networks
- 神经网路简介
- 深度学习在分类和回归中的应用
- 训练和调整神经网路
高级演算法和模型
- 隐玛律可夫模型 （HMM）
- 状态空间模型
- EM 演算法
聚类技术
- 聚类和无监督学习简介
- 流行的聚类演算法：K-Means、Hierarchical Clustering
- 集群的使用案例和实际应用
总结和后续步骤
要求
- 对统计和数据分析有基本的了解
- Programming 具有 R、Python 或其他相关程式设计语言的经验
观众
- 数据科学家
- 统计
14 小时
客户评论 (5)
有运动和展示的变化。
Ida Sjoberg - Swedish National Debt Office
课程 - Econometrics: Eviews and Risk Simulator
机器翻译
the trainer had patience, and was eager to make sure we all understood the topics, the classes were fun to attend
Mamonyane Taoana - Road Safety Department
课程 - Statistical Analysis using SPSS
The pace was just right and the relaxed atmosphere made candidates feel at ease to ask questions.
Rhian Hughes - Public Health Wales NHS Trust
课程 - Introduction to Data Visualization with Tidyverse and R
Michael the trainer is very knowledgeable and skillful about the subject of Big Data and R. He is very flexible and quickly customize the training meeting clients' need. He is also very capable to solve technical and subject matter problems on the go. Fantastic and professional training!.
Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
课程 - Programming with Big Data in R
I enjoyed the Excel sheets provided having the exercises with examples. This meant that if Tamil was held up helping other people, I could crack on with the next parts.
