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所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-nlp-dl-from-text-to-neural-networks-12-hours/
课程评论：没有评论
**课程名称：** 实用自然语言处理与深度学习：从文本到神经网络 (12+ 小时) **课程概述：** 本课程专为渴望深入了解自然语言处理 (NLP) 及深度学习这两个人工智能领域快速发展和高需求领域的人士设计。无论您是学生、希望提升技能的职场人士，还是有志于成为数据科学家，本课程都将为您提供必备的工具和知识，帮助您理解机器如何阅读、解析和学习人类语言。 课程将从 NLP 的基础知识入手，涵盖文本预处理技术，如分词、词干提取、词形还原、停用词移除、词性标注和命名实体识别。这些技术对于准备非结构化文本数据至关重要，并广泛应用于聊天机器人、翻译系统和推荐引擎等真实世界的人工智能应用中。 随后，您将学习如何使用词袋模型 (Bag of Words)、TF-IDF、独热编码 (One-Hot Encoding)、N-grams 以及 Word2Vec 等词嵌入技术将文本表示为数值形式。这些表示是连接原始文本和机器学习模型的桥梁。 随着课程的深入，您将获得神经网络的实践经验，理解感知器、激活函数、反向传播和多层网络等概念。同时，我们还将探讨用于空间数据的卷积神经网络 (CNN) 和用于文本等序列数据的循环神经网络 (RNN)。 本课程以 Python 为主要编程语言，对初学者友好，无需任何 NLP 或深度学习的先验知识。学完后，您将能够构建端到端的模型，并有信心将所学技能应用于真实的人工智能项目，或在机器学习、数据科学、人工智能工程等领域开创职业生涯。
This course is designed for anyone eager to dive into the exciting world of Natural Language Processing (NLP) and Deep Learning, two of the most rapidly growing and in-demand domains in the artificial intelligence industry. Whether you're a student, a working professional looking to upskill, or an aspiring data scientist, this course equips you with the essential tools and knowledge to understand how machines read, interpret, and learn from human language.We begin with the foundations of NLP, starting from scratch with text preprocessing techniques such as tokenization, stemming, lemmatization, stopword removal, POS tagging, and named entity recognition. These techniques are critical for preparing unstructured text data and are used in real-world AI applications like chatbots, translators, and recommendation engines.Next, you will learn how to represent text in numerical form using Bag of Words, TF-IDF, One-Hot Encoding, N-Grams, and Word Embeddings like Word2Vec. These representations are a bridge between raw text and machine learning models.As the course progresses, you will gain hands-on experience with Neural Networks, understanding concepts such as perceptrons, activation functions, backpropagation, and multilayer networks. We'll also explore CNNs (Convolutional Neural Networks) for spatial data and RNNs (Recurrent Neural Networks) for sequential data like text.The course uses Python as the primary programming language and is beginner-friendly, with no prior experience in NLP or deep learning required. By the end, you'll have practical experience building end-to-end models and the confidence to apply your skills in real-world AI projects or pursue careers in machine learning, data science, AI engineering, and more.
