【2024年】十大自然語言處理課程熱門排行推薦與優惠精選!

 

【2024年】十大自然語言處理課程熱門排行推薦與優惠精選!

本文章推薦「Modern Natural Language Processing in Python」、「NLP – Natural Language Processing with Python」、「Natural Language Processing with Deep Learning in Python」等相關LinkedIn線上課程,讓您滿足學習的慾望。
你是否想透過線上學習得到更多的技能,增加自己的技能樹?現在是學生的您,透過線上學習可以將更多專業知識用在課業學習上更加強所學。還是您是朝九晚五的上班族,尋找可以為工作上帶來更上一層樓的技能?或您是因為興趣或想培養其他興趣?
線上課程不受地理位置影響,不受時間早晚影響,老師來自世界各地,也不受學習程度影響的特色,讓您無時無刻想學都可以,想多看幾次增加熟悉度也可以。不同領域的老師將針對不同主題滿足您的學習目的,推薦的課程項目會陸續更新,絕對提供您最熱門人氣高的線上課程。

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目錄

  1. 自然語言處理課程總覽
  2. 自然語言處理課程介紹
  1. Modern Natural Language Processing in Python
  2. NLP – Natural Language Processing with Python
  3. Natural Language Processing with Deep Learning in Python
  4. Data Science: Natural Language Processing (NLP) in Python
  5. Introduction to Natural Language Processing (NLP)
  6. NLP and Text mining with python(for absolute beginners only)
  7. From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase
  8. Hands On Natural Language Processing (NLP) using Python
  9. 2021 Natural Language Processing in Python for Beginners
  10. Ai工程师-自然语言处理实战(Python版)
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自然語言處理課程總覽

為了節省您的時間,本列表整理每個課程重點資訊,讓您可以快速瀏覽這文章所提供的課程是否是您所需要的,點選您有興趣產品的「名稱」或「圖示」可以進一步跳到文章所屬的介紹區塊瞭解更多細節。

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課程資訊
1
2
3
4
5
6
7
8
9
10
評價 4.0 分
(1,307 個評分)
4.6 分
(8,917 個評分)
4.5 分
(6,548 個評分)
4.6 分
(10,084 個評分)
4.4 分
(2,607 個評分)
4.1 分
(402 個評分)
4.1 分
(893 個評分)
4.5 分
(1,304 個評分)
4.5 分
(354 個評分)
4.5 分
(80 個評分)
學生 46,430 人人 44,847 人人 40,069 人人 38,938 人人 10,393 人人 9,919 人人 8,679 人人 7,601 人人 3,092 人人 507 人人
課程描述Solve Seq2Seq and Classification NLP tasks with Transformer and CNN using Tensorflow 2 in Google ColabLearn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language ProcessingComplete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive netsApplications: decrypting ciphers, spam detection, sentiment analysis, article spinners, and latent semantic analysis.Learn how to analyze text data.Learn Natural Language Processing using Python from experts with hands on examples and practice sessions.A down-to-earth, shy but confident take on machine learning techniques that you can put to work todayLearn Natural Language Processing ( NLP ) & Text Mining by creating text classifier, article summarizer, and many more.Text Cleaning, Spacy, NLTK, Scikit-Learn, Deep Learning, word2vec, GloVe, LSTM for Sentiment, Emotion, Spam & CV ParsingNLP实战系列

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自然語言處理課程列表

您可以從下面資訊進一步瞭解自然語言處理課程的價錢與最優惠的折扣、自然語言處理課程的特色以及自然語言處理課程介紹,發掘其他人同樣有興趣的產品還有哪些,期望您能找到滿意的!
1

Modern Natural Language Processing in Python

Solve Seq2Seq and Classification NLP tasks with Transformer and CNN using Tensorflow 2 in Google Colab

點擊前往 Udemy

課程老師Martin Jocqueviel
課程評價4.0 分(1,307 個評分)
學生人數46,430 人

課程介紹

Modern Natural Language Processing course is designed for anyone who wants to grow or start a new career and gain a strong background in NLP.

Nowadays, the industry is becoming more and more in need of NLP solutions. Chatbots and online automatio

[更多細節]

哪些人適合這堂課?

  • AI amateurs that are eager to learn how we process language nowadays
  • AI students that need to have a deeper and wider knowledge about NLP
  • Business driven people that are eager to know how NLP can be applied to their field to leverage any text data
  • Anyone who wants to start a new career and get a strong background in NLP, adding efficient cases to their portfolio

學習目標

  • Build a Transformer, new model created by Google, for any sequence to sequence task (e.g. a translator)
  • Build a CNN specialized in NLP for any classification task (e.g. sentimental analysis)
  • Write a custom training process for more advanced training methods in NLP
  • Create customs layers and models in TF 2.0 for specific NLP tasks
  • Use Google Colab and Tensorflow 2.0 for your AI implementations
  • Pick the best model for each NLP task
  • Understand how we get computers to give meaning to the human language
  • Create datasets for AI from those data
  • Clean text data
  • Understand why and how each of those models work
  • Understand everything about the attention mechanism, lying behind the newest and most powerful NLP algorithms




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2

NLP – Natural Language Processing with Python

Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language Processing

點擊前往 Udemy

課程老師Jose Portilla
課程評價4.6 分(8,917 個評分)
學生人數44,847 人

課程介紹

Welcome to the best Natural Language Processing course on the internet! This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language.

In the course we will cover

[更多細節]

哪些人適合這堂課?

  • Python developers interested in learning how to use Natural Language Processing.

學習目標

  • Learn to work with Text Files with Python
  • Learn how to work with PDF files in Python
  • Utilize Regular Expressions for pattern searching in text
  • Use Spacy for ultra fast tokenization
  • Learn about Stemming and Lemmatization
  • Understand Vocabulary Matching with Spacy
  • Use Part of Speech Tagging to automatically process raw text files
  • Understand Named Entity Recognition
  • Visualize POS and NER with Spacy
  • Use SciKit-Learn for Text Classification
  • Use Latent Dirichlet Allocation for Topic Modelling
  • Learn about Non-negative Matrix Factorization
  • Use the Word2Vec algorithm
  • Use NLTK for Sentiment Analysis
  • Use Deep Learning to build out your own chat bot




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3

Natural Language Processing with Deep Learning in Python

Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets

點擊前往 Udemy

課程老師Lazy Programmer Team
課程評價4.5 分(6,548 個評分)
學生人數40,069 人

課程介紹

In this course we are going to look at NLP (natural language processing) with deep learning.

Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and si

[更多細節]

哪些人適合這堂課?

  • Students and professionals who want to create word vector representations for various NLP tasks
  • Students and professionals who are interested in state-of-the-art neural network architectures like recursive neural networks
  • SHOULD NOT: Anyone who is not comfortable with the prerequisites.

學習目標

  • Understand and implement word2vec
  • Understand the CBOW method in word2vec
  • Understand the skip-gram method in word2vec
  • Understand the negative sampling optimization in word2vec
  • Understand and implement GloVe using gradient descent and alternating least squares
  • Use recurrent neural networks for parts-of-speech tagging
  • Use recurrent neural networks for named entity recognition
  • Understand and implement recursive neural networks for sentiment analysis
  • Understand and implement recursive neural tensor networks for sentiment analysis
  • Use Gensim to obtain pretrained word vectors and compute similarities and analogies




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4

Data Science: Natural Language Processing (NLP) in Python

Applications: decrypting ciphers, spam detection, sentiment analysis, article spinners, and latent semantic analysis.

點擊前往 Udemy

課程老師Lazy Programmer Inc.
課程評價4.6 分(10,084 個評分)
學生人數38,938 人

課程介紹

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 co

[更多細節]

哪些人適合這堂課?

  • Students who are comfortable writing Python code, using loops, lists, dictionaries, etc.
  • Students who want to learn more about machine learning but don’t want to do a lot of math
  • Professionals who are interested in applying machine learning and NLP to practical problems like spam detection, Internet marketing, and sentiment analysis
  • This course is NOT for those who find the tasks and methods listed in the curriculum too basic.
  • This course is NOT for those who don’t already have a basic understanding of machine learning and Python coding (but you can learn these from my FREE Numpy course).
  • This course is NOT for those who don’t know (given the section titles) what the purpose of each task is. E.g. if you don’t know what “spam detection” might be useful for, you are too far behind to take this course.

學習目標

  • Write your own cipher decryption algorithm using genetic algorithms and language modeling with Markov models
  • Write your own spam detection code in Python
  • Write your own sentiment analysis code in Python
  • Perform latent semantic analysis or latent semantic indexing in Python
  • Have an idea of how to write your own article spinner in Python




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5

Introduction to Natural Language Processing (NLP)

Learn how to analyze text data.

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課程老師Brian Sacash
課程評價4.4 分(2,607 個評分)
學生人數10,393 人

課程介紹

This course introduces Natural Language Processing through the use of python and the Natural Language Tool Kit. Through a practical approach, you’ll get hands on experience working with and analyzing text.

As a student of this course, you’ll get upd

[更多細節]

哪些人適合這堂課?

  • This course is for anyone who is not familiar with Natural Language Processing and is looking for a way to start.
  • This course is probably not for you if you already have an understanding of Natural Language Processing and the Natural Language Tool Kit.

學習目標

  • Work with text data using the Natural Language Tool Kit.
  • Load and manipulate custom text data.
  • Analyze text to discover, sentiment, important key words, and statistics.




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6

NLP and Text mining with python(for absolute beginners only)

Learn Natural Language Processing using Python from experts with hands on examples and practice sessions.

點擊前往 Udemy

課程老師Statinfer Solutions
課程評價4.1 分(402 個評分)
學生人數9,919 人

課程介紹

Want to know how NLP algorithms work and how people apply it to solve data science problems? You are looking at right course!

This course has been created, designed and assembled by professional Data Scientist who have worked in this field for ne

[更多細節]

哪些人適合這堂課?

  • Data Scientists who are curious about NLP.
  • Data Analysts working on text data and want to get some insight using NLP.
  • Students who want to learn NLP.

學習目標

  • After the completion of this course, you will have good understanding of NLP.
  • You will approach algorthms to solve real world NLP problems.
  • You will be able to implement sentiment analysis
  • You will be able to perform document classification




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7

From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase

A down-to-earth, shy but confident take on machine learning techniques that you can put to work today

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課程老師Loony Corn
課程評價4.1 分(893 個評分)
學生人數8,679 人

課程介紹

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

Taught by a Stanford-educated, ex-G

[更多細節]

哪些人適合這堂課?

  • Yep! Analytics professionals, modelers, big data professionals who haven’t had exposure to machine learning
  • Yep! Engineers who want to understand or learn machine learning and apply it to problems they are solving
  • Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
  • Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing
  • Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role

學習目標

  • Identify situations that call for the use of Machine Learning
  • Understand which type of Machine learning problem you are solving and choose the appropriate solution
  • Use Machine Learning and Natural Language processing to solve problems like text classification, text summarization in Python




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8

Hands On Natural Language Processing (NLP) using Python

Learn Natural Language Processing ( NLP ) & Text Mining by creating text classifier, article summarizer, and many more.

點擊前往 Udemy

課程老師Next Edge Coding
課程評價4.5 分(1,304 個評分)
學生人數7,601 人

課程介紹

In this course you will learn the various concepts of natural language processing by implementing them hands on in python programming language. This course is completely project based and from the start of the course the main objective would be to le

[更多細節]

哪些人適合這堂課?

  • Anyone willing to start a career in data science and natural language processing
  • Anyone willing to learn the concepts of natural language processing by implementing them
  • Anyone willing to learn Sentiment Analysis

學習目標

  • Understand the various concepts of natural language processing along with their implementation
  • Build natural language processing based applications
  • Learn about the different modules available in Python for NLP
  • Create personal spam filter or sentiment predictor
  • Create personal text summarizer




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9

2021 Natural Language Processing in Python for Beginners

Text Cleaning, Spacy, NLTK, Scikit-Learn, Deep Learning, word2vec, GloVe, LSTM for Sentiment, Emotion, Spam & CV Parsing

點擊前往 Udemy

課程老師Laxmi Kant
課程評價4.5 分(354 個評分)
學生人數3,092 人

課程介紹

Welcome to KGP Talkie’s Natural Language Processing (NLP) course. It is designed to give you a complete understanding of Text Processing and Mining with the use of State-of-the-Art NLP algorithms in Python.

We will learn Spacy in detail and we will

[更多細節]

哪些人適合這堂課?

  • Beginners in Natural Language Processing
  • Data Scientist curious to learn NLP

學習目標

  • Learn complete text processing with Python
  • Learn how to extract text from PDF files
  • Use Regular Expressions for search in text
  • Use SpaCy and NLTK to extract complete text features from raw text
  • Use Latent Dirichlet Allocation for Topic Modelling
  • Use Scikit-Learn and Deep Learning for Text Classification
  • Learn Multi-Class and Multi-Label Text Classification
  • Use Spacy and NLTK for Sentiment Analysis
  • Understand and Build word2vec and GloVe based ML models
  • Use Gensim to obtain pretrained word vectors and compute similarities and analogies
  • Learn Text Summarization and Text Generation using LSTM and GRU




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10

Ai工程师-自然语言处理实战(Python版)

NLP实战系列

點擊前往 Udemy

課程老師唐宇迪 唐
課程評價4.5 分(80 個評分)
學生人數507 人

課程介紹

AI工程师-自然语言处理实战课程旨在用最接地气的方式讲解复杂的算法原理,基于真实数据集,通过实际案例进行项目实战。整个体系内容包括200+课时,20个项目实战,完美覆盖当下热门技术与经典框架实战。学习路线主要包括三大阶段:1.掌握Python在自然语言处理领域必备工具包使用方法 2.机器学习与深度学习在NLP领域常用算法原理与应用实践 3.基于经典框架展开项目实战(Tensorflow,Keras)。

[更多細節]

哪些人適合這堂課?

  • 自然语言处理方向的同学们
  • 数据科学领域的同学们

學習目標

  • 掌握自然语言处理必备经典算法
  • 掌握Python自然语言处理常用工具包
  • 掌握当下NLP在深度学习领域的应用与实践方法
  • 熟练使用深度学习框架搭建NLP项目
  • 熟悉Python文本处理
  • 熟练使用Python工具包进行数据预处理
  • 掌握贝叶斯算法
  • 掌握隐马尔科夫模型算法
  • 熟练使用Python进行文本分析与挖掘
  • 掌握神经网络算法
  • 掌握卷积神经网络原理
  • 掌握递归神经网络原理
  • 掌握词向量模型算法
  • 熟练使用Gensim工具包构建词向量模型
  • 熟练应用word2vec到各大自然语言处理项目
  • 掌握情感分析与分类原理及实践方法
  • 熟练使用Tensorflow框架进行文本处理
  • 熟练应用Tensorflow进行建模实战
  • 掌握多种文本分类实践方法
  • 掌握LSTM网络架构原理与应用
  • 掌握序列网络模型原理与应用
  • 搭建自己的聊天机器人
  • 构建自己的专属输入法
  • 构建生成模型进行文本生成
  • 熟练使用Keras工具包构建NLP项目




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從老師查找更多自然語言處理課程

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【2024年】十大自然語言處理課程熱門排行推薦與優惠精選!

參考其他資料科學線上課程

除了本文介紹的課程種類以外,想要瞭解資料科學領域還有哪些不同類型的課程值得一探究竟嗎?讓您可以從不同面向更紮實的學習,點擊參考以下其他熱門主題文章。絕對提供您最優惠人氣滿檔的課程,歡迎繼續延伸閱讀。


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