Acerca de este Curso

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Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
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Nivel intermedio
Aprox. 24 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Japonés

Habilidades que obtendrás

Machine TranslationWord EmbeddingsLocality-Sensitive HashingSentiment AnalysisVector Space Models
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 24 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Japonés

ofrecido por

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deeplearning.ai

Programa - Qué aprenderás en este curso

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Semana
1

Semana 1

7 horas para completar

Sentiment Analysis with Logistic Regression

7 horas para completar
12 videos (Total 37 minutos), 3 lecturas, 1 cuestionario
12 videos
Welcome to Course 11m
Supervised ML & Sentiment Analysis2m
Vocabulary & Feature Extraction2m
Negative and Positive Frequencies2m
Feature Extraction with Frequencies2m
Preprocessing3m
Putting it All Together2m
Logistic Regression Overview3m
Logistic Regression: Training1m
Logistic Regression: Testing4m
Logistic Regression: Cost Function5m
3 lecturas
Connect with your mentors and fellow learners on Slack!10m
Acknowledgement - Ken Church10m
How to refresh your workspace10m
Semana
2

Semana 2

5 horas para completar

Sentiment Analysis with Naïve Bayes

5 horas para completar
11 videos (Total 40 minutos)
11 videos
Bayes’ Rule3m
Naïve Bayes Introduction5m
Laplacian Smoothing2m
Log Likelihood, Part 15m
Log Likelihood, Part 21m
Training Naïve Bayes3m
Testing Naïve Bayes4m
Applications of Naïve Bayes3m
Naïve Bayes Assumptions3m
Error Analysis3m
Semana
3

Semana 3

6 horas para completar

Vector Space Models

6 horas para completar
8 videos (Total 26 minutos)
8 videos
Word by Word and Word by Doc. 4m
Euclidean Distance3m
Cosine Similarity: Intuition2m
Cosine Similarity3m
Manipulating Words in Vector Spaces3m
Visualization and PCA3m
PCA Algorithm3m
Semana
4

Semana 4

6 horas para completar

Machine Translation and Document Search

6 horas para completar
8 videos (Total 29 minutos), 2 lecturas, 1 cuestionario
8 videos
Transforming word vectors6m
K-nearest neighbors3m
Hash tables and hash functions3m
Locality sensitive hashing5m
Multiple Planes3m
Approximate nearest neighbors3m
Searching documents1m
2 lecturas
Acknowledgements10m
Bibliography10m

Reseñas

Principales reseñas sobre NATURAL LANGUAGE PROCESSING WITH CLASSIFICATION AND VECTOR SPACES

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Acerca de Programa especializado: Procesamiento de lenguajes naturales

Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio. By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper....
Procesamiento de lenguajes naturales

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