Data Science continues to be a hot topic, but more specifically, Natural Language Processing (NLP) is increasing in demand.

數據科學仍然是一個熱門話題，但更具體地說，自然語言處理(NLP)的需求正在增長。

Broadly speaking, NLP is a subset of artificial intelligence and machine learning that helps computers understand, interpret, and manipulate human language. It has MANY applications including speech recognition, automated chatbots, sentiment analysis, and more.

從廣義上講，NLP是人工智能和機器學習的子集，可幫助計算機理解，解釋和操縱人類語言。 它具有許多應用程序，包括語音識別，自動聊天機器人，情緒分析等。

Below are several high-quality courses on Natural Language Processing that are FREE:

以下是一些免費的自然語言處理高質量課程：

1.從語言到信息(斯坦福大學) (1. From Languages to Information (Stanford University))

If you’re looking for an introduction to NLP, this course is it. Keep in mind that this course was made even for those who don’t have any experience in Python, hence the tutorial on Python. Personally, I feel like this definitely covers a lot on theoretically, but there are definitely other courses out there that are better for applications.

如果您正在尋找有關NLP的介紹，那么就是這門課程。 請記住，本課程甚至是為那些沒有Python經驗的人開設的，因此請參閱Python教程。 就個人而言，我覺得這在理論上肯定涵蓋了很多內容，但是肯定還有其他課程更適合于應用程序。

This course covers the basics on text processing, sentiment analysis, information retrieval, chatbots, and more. I highly recommend this course if you are new to programming or know absolutely nothing about NLP.

本課程涵蓋了文本處理，情感分析，信息檢索，聊天機器人等基礎知識。 如果您不熟悉編程或對NLP完全一無所知，我強烈建議您參加本課程。

2.深度學習中的自然語言處理(斯坦福大學) (2. Natural Language Processing with Deep Learning (Stanford University))

This course is also from Stanford but it is a little more advanced. You’re expected to be proficient in Python and have a good understanding of basic calculus, statistics, and machine learning. This course is more math-heavy, so make sure that you have a good understanding of vectors and matrices.

該課程也是斯坦福大學的課程，但要高級一些。 您應該精通Python，并且對基本演算，統計數據和機器學習有很好的了解。 本課程比較重數學，因此請確保您對向量和矩陣有充分的了解。

Keep in mind that a big portion of the course focuses on vectors, matrix calculus, and neural networks because these concepts make up the foundation of a lot of NLP concepts. So if you don’t feel like you have the mathematical skills required, I recommend that you start with the first course above.

請記住，本課程的很大一部分重點是矢量，矩陣演算和神經網絡，因為這些概念構成了許多NLP概念的基礎。 因此，如果您不具備所需的數學技能，建議您從上述第一門課程開始。

3.用于自然語言處理的深度學習(牛津大學) (3. Deep Learning for Natural Language Processing (University of Oxford))

Quoted by them, this is an advanced course on NLP.

由他們引用，這是一門有關NLP的高級課程。

“This will be an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks. We will introduce the mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms. The course will cover a range of applications of neural networks in NLP including analysing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions.”

“這將是一門應用課程，重點是使用遞歸神經網絡分析和生成語音和文本的最新進展。 我們將介紹相關機器學習模型的數學定義，并推導它們相關的優化算法。 該課程將涵蓋NLP中神經網絡的一系列應用，包括分析文本的潛在維度，將語音轉錄為文本，在語言之間進行翻譯以及回答問題。”

Similar to the second course, this course has a heavy emphasis on neural networks, and so, it is highly recommended that you understanding fundamental linear algebra, continuous mathematics, and probability concepts. This course is also very practical and application-heavy, so you should also be a proficient programmer.

與第二門課程類似，本課程著重于神經網絡，因此強烈建議您了解基本的線性代數，連續數學和概率概念。 本課程也是非常實用且需要大量應用程序的課程，因此您也應該是一個熟練的程序員。

4.自然語言處理(華盛頓大學) (4. Natural Language Processing (University of Washington))

This is a unique course that initially focuses on things that aren’t normally focused on, like Hidden Markov Models, Probabilistic Context-Free Grammars, and more. The latter half of the course primarily focuses on vectors and neural networks.

這是一門獨特的課程，最初重點關注通常不關注的事物，例如隱馬爾可夫模型，概率上下文無關文法，等等。 課程的后半部分主要關注向量和神經網絡。

Personally, I feel like the course material provides nice summaries for certain topics, like neural networks. However, with a like of assignments/practicals, I feel like this is more of a resource that you could use to skim or refresh your memory.

我個人覺得課程材料為某些主題(如神經網絡)提供了很好的總結。 但是，通過類似的作業/實踐，我覺得這更多地是您可以用來瀏覽或刷新記憶的資源。

5.應用自然語言處理(加州大學伯克利分校) (5. Applied Natural Language Processing (UC Berkeley))

This is a graduate course that is quite extensive. It emphasizes the use of scikit-learn, keras, gensim, and spacy. In terms of raw theory, this course has top-tier slides and extra readings to further your knowledge. It also covers several topics that some of the courses above don’t.

這是一門非常廣泛的研究生課程。 它強調使用scikit-learn，keras，gensim和spacy。 在原始理論方面，本課程包含頂級幻燈片和更多閱讀材料，以進一步提高您的知識水平。 它還涵蓋了一些上述課程沒有的主題。

The only unfortunate thing is that they don’t share any of their assignments/practicals or quizzes, so there aren’t any opportunities for you to put your knowledge to the test.

唯一不幸的是，他們沒有分享他們的任何作業/實踐或測驗，因此您沒有任何機會進行知識測驗。

Quoted from them, “Topics include text-driven forecasting and prediction (using text for problems involving classification or regression); experimental design; the representation of text, including features derived from linguistic structure (such as parts of speech, named entities, syntax, and coreference) and features derived from low-dimensional representations of words, sentences and documents; exploring textual similarity for the purpose of clustering; information extraction (extracting relations between entities mentioned in text); and human-in-the-loop interactive NLP.”

引述他們的話， “主題包括文本驅動的預測和預測(將文本用于涉及分類或回歸的問題)； 實驗設計; 文本表示，包括從語言結構衍生的特征(例如詞性，命名實體，語法和共指)，以及從單詞，句子和文檔的低維表示衍生的特征； 探索文本相似性以進行聚類； 信息提取(提取文本中提到的實體之間的關系)； 以及環人互動NLP。”

謝謝閱讀！ (Thanks for Reading!)

I hope that you find a course that suits your needs and I wish you the best in your data science journey.

我希望您找到適合您需求的課程，并祝您在數據科學之旅中一切順利。

特倫斯·辛 (Terence Shin)

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