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所在平台: Udemy |
课程主页: https://www.udemy.com/course/an-introduction-to-word-vectorization/
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课程名称：词向量化入门 课程概述：在这门课程中，您将学习如何将文本转换为可以用于各种自然语言处理（NLP）任务的数值向量。课程覆盖了词向量化的理论和实践，这是一种将词转换为数值向量的技术，能够捕捉其意义、用法或上下文的某些方面。您将了解不同类型的词向量化方法，包括基于频率的方法和基于预测的方法，以及它们在假设、优缺点上的差异。此外，您还将学习如何使用Python及流行库（如Gensim和TensorFlow）实现某些词向量化方法，并将其应用于自己的NLP项目。课程还将介绍如何评估和可视化词向量，以及如何将其用于各种NLP任务，如情感分析、文本分类和机器翻译。 课程内容如下： 第一讲：词向量化简介 - 将学习词向量化的基本知识及其在NLP中的重要性，了解两大类词向量化方法：基于频率和基于预测的方法及其高层次的工作原理。 第二讲：基于频率的词向量化方法 - 将学习常见的基于频率的词向量化方法，如独热编码、计数向量器、TF-IDF和n-grams，了解它们的工作原理及其优缺点，并学会如何使用Python和Gensim实现这些方法。 第三讲：基于预测的词向量化方法 - 将学习常见的基于预测的词向量化方法，如word2vec、fastText和GloVe，了解它们的工作原理及其优缺点，并学会如何使用Python和TensorFlow实现这些方法。 第四讲：词向量的评估与可视化 - 将学习如何评估和可视化词向量，以及如何将其应用于各种NLP任务。将了解不同的评估方法（如内在评估和外在评估）及不同的降维技术（如PCA和t-SNE），并学习如何利用词向量进行情感分析、文本分类和机器翻译。
Word Vectorization: Learn how to transform text into numerical vectors that can be used for various natural language processing (NLP) tasks. In this course, you will learn about the theory and practice of word vectorization, a technique that converts words into numerical vectors that capture some aspects of their meaning, usage, or context. You will learn about the different types of word vectorization methods, such as frequency-based and prediction-based methods, and how they differ in their assumptions, advantages, and disadvantages. You will also learn how to implement some of the word vectorization methods using Python and popular libraries, such as Gensim and TensorFlow, and how to use them for your own NLP projects. You will also learn how to evaluate and visualize the word vectors, and how to use them for various NLP tasks, such as sentiment analysis, text classification, and machine translation.The course is divided into the following lectures:Lecture 1: Introduction to Word Vectorization. In this lecture, you will learn about the basics of word vectorization, and why it is important for NLP. You will also learn about the two main categories of word vectorization methods: frequency-based and prediction-based methods, and how they work at a high level.Lecture 2: Frequency-based Methods of Word Vectorization. In this lecture, you will learn about the frequency-based methods of word vectorization, such as one-hot encoding, count vectorizer, TF-IDF, and n-grams. You will see how they work, and what are their advantages and disadvantages. You will also learn how to implement them using Python and Gensim.Lecture 3: Prediction-based Methods of Word Vectorization. In this lecture, you will learn about the prediction-based methods of word vectorization, such as word2vec, fastText, and GloVe. You will see how they work, and what are their advantages and disadvantages. You will also learn how to implement them using Python and TensorFlow.Lecture 4: Evaluation and Visualization of Word Vectors. In this lecture, you will learn how to evaluate and visualize the word vectors, and how to use them for various NLP tasks. You will learn about the different evaluation methods, such as intrinsic and extrinsic evaluation, and the different dimensionality reduction techniques, such as PCA and t-SNE. You will also learn how to use the word vectors for sentiment analysis, text classification, and machine translation.
