- 具備統計學和線性代數的基本知識
- 熟悉數據分析或商業智能概念
- 建議有程式設計經驗（最好是Python或R）
- 對學習應用機器學習於數據驅動項目感興趣
目標受眾
- 數據分析師和科學家
- 統計學家和研究專業人員
- 探索機器學習工具的開發人員和IT專業人員
- 參與數據科學或預測分析項目的任何人
Machine Learning 是现代数据科学和人工智能的基础技术，使计算机能够从数据中学习并做出预测性决策。
本课程为讲师指导的培训（线上或线下），面向中级数据分析师、开发人员或有志于成为数据科学家的学员，旨在帮助他们在Python中应用机器学习技术，提取洞察、做出预测并自动化数据驱动的决策。
课程结束后，学员将能够：
- 理解并区分关键的机器学习范式。
- 探索数据预处理技术和模型评估指标。
- 应用机器学习算法解决现实世界的数据问题。
- 使用Python库和Jupyter笔记本进行实践开发。
- 构建用于预测、分类、推荐和聚类的模型。
课程形式
- 互动式讲座与讨论。
- 大量练习与实践。
- 在实时实验环境中进行动手操作。
课程定制选项
- 如需为本课程定制培训，请联系我们安排。
Machine Learning 簡介
- 機器學習的類型 – 監督式與非監督式
- 從統計學習到機器學習
- 數據挖掘工作流程：業務理解、數據準備、建模、部署
- 為任務選擇合適的算法
- 過擬合與偏差-方差權衡
Python 與 ML 庫概述
- 為何使用程式語言進行機器學習
- 在 R 和 Python 之間選擇
- Python 速成課程與 Jupyter Notebooks
- Python 庫：pandas、NumPy、scikit-learn、matplotlib、seaborn
測試與評估 ML 算法
- 泛化、過擬合與模型驗證
- 評估策略：保留法、交叉驗證、自助法
- 回歸指標：ME、MSE、RMSE、MAPE
- 分類指標：準確率、混淆矩陣、不平衡類別
- 模型性能可視化：利潤曲線、ROC 曲線、提升曲線
- 模型選擇與網格搜索調參
數據準備
- Python 中的數據導入與存儲
- 探索性分析與摘要統計
- 處理缺失值與異常值
- 標準化、正規化與轉換
- 定性數據重新編碼與使用 pandas 進行數據整理
分類算法
- 二分類與多分類
- 邏輯回歸與判別函數
- 朴素貝葉斯、k-近鄰
- 決策樹：CART、Random Forests、Bagging、Boosting、XGBoost
- 支持向量機與核函數
- 集成學習技術
回歸與數值預測
- 最小二乘法與變量選擇
- 正則化方法：L1、L2
- 多項式回歸與非線性模型
- 回歸樹與樣條
Unsupervised Learning
- 聚類技術：k-means、k-medoids、層次聚類、SOMs
- 降維：PCA、因子分析、SVD
- 多維尺度分析
文本挖掘
- 文本預處理與分詞
- 詞袋模型、詞幹提取與詞形還原
- 情感分析與詞頻分析
- 使用詞雲可視化文本數據
推薦系統
- 基於用戶與基於項目的協同過濾
- 設計與評估推薦引擎
關聯模式挖掘
- 頻繁項集與 Apriori 算法
- 市場籃子分析與提升比
異常檢測
- 極值分析
- 基於距離與基於密度的方法
- 高維數據中的異常檢測
Machine Learning 案例研究
- 理解業務問題
- 數據預處理與特徵工程
- 模型選擇與參數調優
- 評估與結果展示
- 部署
總結與下一步
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