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所在平台: Udemy |
课程主页: https://www.udemy.com/course/natural-language-processing-in-python/
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课程名称：Python中的机器学习：自然语言处理（版本2） 课程概述： 在这个课程中，你将了解先进的人工智能技术如OpenAI的ChatGPT、GPT-4、DALL-E、Midjourney和Stable Diffusion的基础知识。本课程是一个综合性的4合1课程，涵盖了以下内容： 1) 向量模型和文本预处理方法 2) 概率模型和马尔可夫模型 3) 机器学习方法 4) 深度学习和神经网络方法 第一部分涉及向量模型和文本预处理方法，重点讲解向量在数据科学和人工智能中的重要性。你将学习如何将文本转化为向量，使用诸如CountVectorizer和TF-IDF等技术，同时还将了解word2vec和GloVe等神经嵌入方法。学习成果将应用于文本分类、文档检索和文本摘要等任务，同时掌握令牌化、词干提取和词形还原等文本预处理步骤。 第二部分讲解概率模型和马尔可夫模型，这些是过去100年间在数据科学和机器学习领域中的重要模型。课程内容将展示如何利用这些模型进行文本分类、文章重写和文本生成（如诗歌创作）。这些概念也是理解最新Transformer模型（例如BERT和GPT-3）的基础。 第三部分讨论机器学习方法，涵盖了经典的自然语言处理任务，如垃圾邮件检测、情感分析、潜在语义分析和主题建模。本部分将侧重于应用，而非理论，学习的算法包括朴素贝叶斯、逻辑回归、主成分分析（PCA）、奇异值分解（SVD）和潜在狄利克雷分配（LDA），这些都是自然语言处理中的基本算法。 第四部分聚焦深度学习方法，讲解现代神经网络架构如何用于解决自然语言处理任务。课程内容将覆盖前馈人工神经网络（ANNs）、嵌入、卷积神经网络（CNNs）和递归神经网络（RNNs），特别是LSTM和GRU等现代架构，这些在语言翻译、语音识别和文本转语音等任务中被广泛应用。 独特特点： - 每一行代码都有详细解释。 - 无需浪费时间进行“打字”，让你专注于学习重要内容。 - 担心大学级数学的同学也能理解重要的算法细节。 感谢你的阅读，期待在课程中见到你！
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.Hello friends!Welcome to Machine Learning: Natural Language Processing in Python (Version 2).This is a massive 4-in-1 course covering:1) Vector models and text preprocessing methods2) Probability models and Markov models3) Machine learning methods4) Deep learning and neural network methodsIn part 1, which covers vector models and text preprocessing methods, you will learn about why vectors are so essential in data science and artificial intelligence. You will learn about various techniques for converting text into vectors, such as the CountVectorizer and TF-IDF, and you'll learn the basics of neural embedding methods like word2vec, and GloVe.You'll then apply what you learned for various tasks, such as:Text classificationDocument retrieval / search engineText summarizationAlong the way, you'll also learn important text preprocessing steps, such as tokenization, stemming, and lemmatization.You'll be introduced briefly to classic NLP tasks such as parts-of-speech tagging.In part 2, which covers probability models and Markov models, you'll learn about one of the most important models in all of data science and machine learning in the past 100 years. It has been applied in many areas in addition to NLP, such as finance, bioinformatics, and reinforcement learning.In this course, you'll see how such probability models can be used in various ways, such as:Building a text classifierArticle spinningText generation (generating poetry)Importantly, these methods are an essential prerequisite for understanding how the latest Transformer (attention) models such as BERT and GPT-3 work. Specifically, we'll learn about 2 important tasks which correspond with the pre-training objectives for BERT and GPT.In part 3, which covers machine learning methods, you'll learn about more of the classic NLP tasks, such as:Spam detectionSentiment analysisLatent semantic analysis (also known as latent semantic indexing)Topic modelingThis section will be application-focused rather than theory-focused, meaning that instead of spending most of our effort learning about the details of various ML algorithms, you'll be focusing on how they can be applied to the above tasks.Of course, you'll still need to learn something about those algorithms in order to understand what's going on. The following algorithms will be used:Naive BayesLogistic RegressionPrincipal Components Analysis (PCA) / Singular Value Decomposition (SVD)Latent Dirichlet Allocation (LDA)These are not just "any" machine learning / artificial intelligence algorithms but rather, ones that have been staples in NLP and are thus an essential part of any NLP course.In part 4, which covers deep learning methods, you'll learn about modern neural network architectures that can be applied to solve NLP tasks. Thanks to their great power and flexibility, neural networks can be used to solve any of the aforementioned tasks in the course.You'll learn about:Feedforward Artificial Neural Networks (ANNs)EmbeddingsConvolutional Neural Networks (CNNs)Recurrent Neural Networks (RNNs)The study of RNNs will involve modern architectures such as the LSTM and GRU which have been widely used by Google, Amazon, Apple, Facebook, etc. for difficult tasks such as language translation, speech recognition, and text-to-speech.Obviously, as the latest Transformers (such as BERT and GPT-3) are examples of deep neural networks, this part of the course is an essential prerequisite for understanding Transformers.UNIQUE FEATURESEvery line of code explained in detail - email me any time if you disagreeNo wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratchNot afraid of university-level math - get important details about algorithms that other courses leave outThank you for reading and I hope to see you soon!
