不需要 NLP 背景。
要求：熟悉任何程式設計語言（Java、Python、PHP、VBA 等）。
預期成績：合理的數學技能（A-level 標準），尤其是在概率、統計和微積分方面。
有益的：熟悉正則表達式。
本課程專為有興趣從書面英語文本中提取含義的人而設計，儘管這些知識也可以應用於其他人類語言。
該課程將介紹如何使用人類編寫的文本，例如 博客文章、推文等......
例如，分析師可以設置一種演算法，該演算法將根據廣泛的數據源自動得出結論。
NLP 方法簡介
- 單詞和句子分詞
- 文本分類
- 情感分析
- 拼寫更正
- 資訊提取
- 解析
- 含義提取
- 問答
NLP 理論概述
- 概率
- 統計學
- 機器學習
- N-gram 語言建模
- 樸素貝葉斯
- Maxent 分類器
- 序列模型 （Hidden Markov Models）
- 概率依賴性
- 成分解析
- 向量空間意義模型
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