Concept
Gensim
Related lectures (20)
Binary Sentiment Classifier Training
Covers the training of a binary sentiment classifier using an RNN.
Word Embeddings: Models and Learning
Explores word embeddings, context importance, and learning algorithms for creating new representations.
Handling Text: Document Retrieval, Classification, Sentiment Analysis
Explores document retrieval, classification, sentiment analysis, TF-IDF matrices, nearest-neighbor methods, matrix factorization, regularization, LDA, contextualized word vectors, and BERT.
Text Processing: Large Digital Text Collections Analysis
Delves into the processing of large digital text collections, exploring hidden regularities, text reuse, and TF-IDF analysis.
Text Data Processing: Basics & Techniques
Introduces the basics of text data processing, covering document retrieval, classification, sentiment analysis, and topic detection.
Text Data Analysis: Basics and Techniques
Introduces the basics of text data analysis, covering document retrieval, classification, sentiment analysis, and topic detection using preprocessing techniques and machine learning models.
Text Data Analysis: Techniques and Applications
Explores handling text data, deriving clean datasets from unstructured text, and various text analysis techniques.
Handling Text Data: Document Retrieval and Classification
Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors.
Text Handling: Matrix, Documents, Topics
Explores text handling, focusing on matrices, documents, and topics, including challenges in document classification and advanced models like BERT.
Document Retrieval and Classification
Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors like BERT.