課程簡介
基礎 Machine Learning
- Machine Learning 概念和工作流簡介
- 監督學習與無監督學習
- 評估機器學習模型:指標和技術
貝葉斯方法
- 樸素貝葉斯和多項式模型
- 貝葉斯分類數據分析
- 貝葉斯圖形模型
回歸技術
- 線性回歸
- Logistic 回歸
- 廣義線性模型 (GLM)
- 混合模型和增材模型
降維
- 主成分分析 (PCA)
- 因數分析 (FA)
- 獨立成分分析 (ICA)
分類方法
- K 最近鄰 (KNN)
- 用於回歸與分類的支援向量機 (SVM)
- 提升和整合模型
Neural Networks
- 神經網路簡介
- 深度學習在分類和回歸中的應用
- 訓練和調整神經網路
高級演算法和模型
- 隱瑪律可夫模型 (HMM)
- 狀態空間模型
- EM 演算法
聚類技術
- 聚類和無監督學習簡介
- 流行的聚類演算法:K-Means、Hierarchical Clustering
- 集群的使用案例和實際應用
總結和後續步驟
最低要求
- 對統計和數據分析有基本的瞭解
- Programming 具有 R、Python 或其他相關程式設計語言的經驗
觀眾
- 數據科學家
- 統計
客戶評論 (5)
有運動和展示的變化。
Ida Sjoberg - Swedish National Debt Office
Course - Econometrics: Eviews and Risk Simulator
機器翻譯
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
Course - Statistical Analysis using SPSS
Day 1 and Day 2 were really straight forward for me and really enjoyed that experience.
Mareca Sithole - Africa Health Research Institute
Course - R Fundamentals
The pace was just right and the relaxed atmosphere made candidates feel at ease to ask questions.
Rhian Hughes - Public Health Wales NHS Trust
Course - 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!.