This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort.
Offert par
Spécialisation Exploration de données Université de l'Illinois à Urbana-ChampaignÀ propos de ce cours
23,359 consultations récentes
Résultats de carrière des étudiants
27%
ont commencé une nouvelle carrière après avoir terminé ce cours
33%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
10%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 3 sur 6 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Approx. 33 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Vietnamien, Coréen, Allemand, Russe, Turc, Anglais, Espagnol
Compétences que vous acquerrez
Data Clustering AlgorithmsText MiningProbabilistic ModelsSentiment Analysis
Résultats de carrière des étudiants
27%
ont commencé une nouvelle carrière après avoir terminé ce cours
33%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
10%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 3 sur 6 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Approx. 33 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Vietnamien, Coréen, Allemand, Russe, Turc, Anglais, Espagnol
Offert par
Université de l'Illinois à Urbana-Champaign
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
Programme du cours : ce que vous apprendrez dans ce cours
2 heures pour terminer
Orientation
You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.
2 heures pour terminer
2 vidéos (Total 15 min), 5 lectures, 2 quiz
5 lectures
Welcome to Text Mining and Analytics!10 min
Syllabus15 min
About the Discussion Forums15 min
Updating your Profile10 min
Social Media10 min
2 exercices pour s'entraîner
Orientation Quiz15 min
Pre-Quiz30 min
4 heures pour terminer
Week 1
During this module, you will learn the overall course design, an overview of natural language processing techniques and text representation, which are the foundation for all kinds of text-mining applications, and word association mining with a particular focus on mining one of the two basic forms of word associations (i.e., paradigmatic relations).
4 heures pour terminer
9 vidéos (Total 109 min), 1 lecture, 2 quiz
9 vidéos
1.2 Overview Text Mining and Analytics: Part 211 min
1.3 Natural Language Content Analysis: Part 112 min
1.4 Natural Language Content Analysis: Part 24 min
1.5 Text Representation: Part 110 min
1.6 Text Representation: Part 29 min
1.7 Word Association Mining and Analysis15 min
1.8 Paradigmatic Relation Discovery Part 114 min
1.9 Paradigmatic Relation Discovery Part 217 min
1 lecture
Week 1 Overview10 min
2 exercices pour s'entraîner
Week 1 Practice Quiz1 h
Week 1 Quiz1 h
4 heures pour terminer
Week 2
During this module, you will learn more about word association mining with a particular focus on mining the other basic form of word association (i.e., syntagmatic relations), and start learning topic analysis with a focus on techniques for mining one topic from text.
4 heures pour terminer
10 vidéos (Total 116 min), 1 lecture, 2 quiz
10 vidéos
2.2 Syntagmatic Relation Discovery: Conditional Entropy11 min
2.3 Syntagmatic Relation Discovery: Mutual Information: Part 113 min
2.4 Syntagmatic Relation Discovery: Mutual Information: Part 29 min
2.5 Topic Mining and Analysis: Motivation and Task Definition7 min
2.6 Topic Mining and Analysis: Term as Topic11 min
2.7 Topic Mining and Analysis: Probabilistic Topic Models14 min
2.8 Probabilistic Topic Models: Overview of Statistical Language Models: Part 110 min
2.9 Probabilistic Topic Models: Overview of Statistical Language Models: Part 213 min
2.10 Probabilistic Topic Models: Mining One Topic12 min
1 lecture
Week 2 Overview10 min
2 exercices pour s'entraîner
Week 2 Practice Quiz1 h
Week 2 Quiz1 h
10 heures pour terminer
Week 3
During this module, you will learn topic analysis in depth, including mixture models and how they work, Expectation-Maximization (EM) algorithm and how it can be used to estimate parameters of a mixture model, the basic topic model, Probabilistic Latent Semantic Analysis (PLSA), and how Latent Dirichlet Allocation (LDA) extends PLSA.
10 heures pour terminer
10 vidéos (Total 103 min), 2 lectures, 3 quiz
10 vidéos
3.2 Probabilistic Topic Models: Mixture Model Estimation: Part 110 min
3.3 Probabilistic Topic Models: Mixture Model Estimation: Part 28 min
3.4 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 111 min
3.5 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 210 min
3.6 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 36 min
3.7 Probabilistic Latent Semantic Analysis (PLSA): Part 110 min
3.8 Probabilistic Latent Semantic Analysis (PLSA): Part 210 min
3.9 Latent Dirichlet Allocation (LDA): Part 110 min
3.10 Latent Dirichlet Allocation (LDA): Part 212 min
2 lectures
Week 3 Overview10 min
Programming Assignments Overview10 min
2 exercices pour s'entraîner
Week 3 Practice Quiz1 h
Quiz: Week 3 Quiz1 h
5 heures pour terminer
Week 4
During this module, you will learn text clustering, including the basic concepts, main clustering techniques, including probabilistic approaches and similarity-based approaches, and how to evaluate text clustering. You will also start learning text categorization, which is related to text clustering, but with pre-defined categories that can be viewed as pre-defining clusters.
5 heures pour terminer
9 vidéos (Total 141 min), 1 lecture, 2 quiz
9 vidéos
4.2 Text Clustering: Generative Probabilistic Models Part 116 min
4.3 Text Clustering: Generative Probabilistic Models Part 28 min
4.4 Text Clustering: Generative Probabilistic Models Part 314 min
4.5 Text Clustering: Similarity-based Approaches17 min
4.6 Text Clustering: Evaluation10 min
4.7 Text Categorization: Motivation14 min
4.8 Text Categorization: Methods11 min
4.9 Text Categorization: Generative Probabilistic Models31 min
1 lecture
Week 4 Overview10 min
2 exercices pour s'entraîner
Week 4 Practice Quiz1 h
Week 4 Quiz1 h
Avis
Meilleurs avis pour EXPLOITATION DE TEXT ET ANALYTIQUE
par JH9 févr. 2017
Excellent course, the pipeline they propose to help you understand text mining is quite helpful. It has an important introduction to the most key concepts and techniques for text mining and analytics.
par DC24 mars 2018
The content of Text Mining and Analytics is very comprehensive and deep. More practise about how formula works would be better. Quiz could be not tough to be completed after attending every lectures.
par AC11 févr. 2017
This is a very good course. I think it provides a very good foundation of text mining and analytics like PLSA and LDA. More advanced research discussed in the last lecture is also very interesting.
par JS6 juin 2017
The content was very useful, and the preparation of the course denoted much care and preparation by the teacher. I would love to see some modern topics like word embeddings covered in the course!
À propos du Spécialisation Exploration de données
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
Is financial aid available?
Puis-je obtenir des crédits universitaires si je réussis le Cours ?
D'autres questions ? Visitez le Centre d'Aide pour les Etudiants.
