- Python 程式設計經驗。
- 具有 Numpy、Pandas 和 scikit-learn 的經驗。
- 熟悉機器學習演算法。
觀眾
- 開發人員
- 數據科學家
- 數據分析師
特徵工程是選擇和轉換數據以提高機器學習算法準確性的過程。它需要深入了解主題專家的數據。
這種以講師為主導的現場培訓（現場或遠程）是針對那些希望應用特徵工程技術以更好地處理數據和獲得更好的機器學習模型的人。
在培訓結束時，參與者將能夠：
- 設置最佳開發環境，包括所有需要的Python包。
- 通過分析數據集的功能獲得重要的見解。
- 通過調整原始數據本身來優化機器學習模型。
- 清理和轉換數據集以準備機器學習。
課程格式
- 互動講座和討論。
- 大量的練習和練習。
- 在實時實驗室環境中親自實施。
課程自定義選項
- 要申請本課程的定制培訓，請聯繫我們安排。
介紹
- 在模式識別、分類和回歸方面構建有效的演算法。
設置開發環境
- Python 庫
- 在線編輯器與離線編輯器
特徵工程概述
- 輸入與輸出變數（特徵）
- 特徵工程的優缺點
原始數據中遇到的問題類型
- 不乾淨的數據、缺失的數據等。
預處理變數
- 處理缺失數據
處理數據中的缺失值
使用類別變數
將標籤轉換為數位
處理分類變數中的標籤
轉換變數以提高預測能力
- 數位、分類、日期等。
清理數據集
Machine Learning 建模
處理數據中的異常值
- 數值變數、分類變數等
總結和結論
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