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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-natural-language-processing-in-python/
课程评论：没有评论
课程名称：数据科学：Python中的自然语言处理（NLP） 课程概述：你是否曾经想过OpenAI的ChatGPT、GPT-4、DALL-E、Midjourney和Stable Diffusion等人工智能技术是如何工作的？在本课程中，你将学习这些开创性应用的基础知识。课程将指导你使用自然语言处理（NLP）构建多个实际系统，这一领域涉及文本和语音。课程不包含复杂的数学运算，专注于Python编码，且所有学习材料均为免费。 课程将从简要讨论NLP的概念和应用开始，接着将开始构建有用的项目。第一个项目是实现密码解密算法，这在军事和间谍活动中有实际应用。接下来，我们会使用字符级语言模型（基于马尔可夫原理）和遗传算法来构建NLP工具。 第二个项目是构建垃圾邮件检测器。这项技术使我们能够大幅减少接收到的邮件垃圾。我们还将开发一个情感分析模型，能够对文本块进行评分，以判断其积极或消极的程度，这种技术在Twitter上预测股市走势时尤为有用。 课程还将介绍一些实用的工具和技术，如自然语言工具包（NLTK）和潜在语义分析（LSA）。最后，我们将构建一个文章重写工具，这是一个复杂的任务，当前市场上大多数产品在这方面表现仍不理想。讲座旨在帮助你入门并启发你改进自己的思路。一旦掌握，你就可以将其作为搜索引擎优化（SEO）工具，受到网络营销人员的欢迎。 这门课程注重“如何构建和理解”，而不仅仅是“如何使用”。任何人都能在看完文档后15分钟内学会使用API，但本课程让你通过实验亲自去“看到”模型内部发生的事情。如果你想深入了解机器学习模型而不仅仅是表面，那么这门课程适合你。 正如著名物理学家理查德·费曼所说：“我无法创造的，我就无法理解”。本课程是唯一让你从零开始实施机器学习算法的课程，与其他课程不同，后者只教你如何将数据插入库，而你是否真的需要帮助完成三行代码呢？经过对10个数据集的相似操作，你将意识到你学到的并不是10个东西，而是学到了一件事，只是重复相同的三行代码十次。
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.In this course you will build MULTIPLE practical systems using natural language processing, or NLP - the branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so it doesn't contain any hard math - just straight up coding in Python. All the materials for this course are FREE.After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we'll build is a cipher decryption algorithm. These have applications in warfare and espionage. We will learn how to build and apply several useful NLP tools in this section, namely, character-level language models (using the Markov principle), and genetic algorithms.The second project, where we begin to use more traditional "machine learning", is to build a spam detector. You likely get very little spam these days, compared to say, the early 2000s, because of systems like these.Next we'll build a model for sentiment analysis in Python. This is something that allows us to assign a score to a block of text that tells us how positive or negative it is. People have used sentiment analysis on Twitter to predict the stock market.We'll go over some practical tools and techniques like the NLTK (natural language toolkit) library and latent semantic analysis or LSA.Finally, we end the course by building an article spinner. This is a very hard problem and even the most popular products out there these days don't get it right. These lectures are designed to just get you started and to give you ideas for how you might improve on them yourself. Once mastered, you can use it as an SEO, or search engine optimization tool. Internet marketers everywhere will love you if you can do this for them!This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you."If you can't implement it, you don't understand it"Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratchOther courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
