NLP in Python - Natural Language Processing
NLP is a field concerned with the ability of a computer to understand, analyze, manipulate and potentially generate human language.In this course we study about NLP and use the NLP toolkit or NLTK in Python.
Natural Language Processing
Explore natural language processing (NLP) concepts, review Text preprocessing and data cleaning, learn forward propagation and back propagation in neural networks, Word Embedding using Keras and deep learning with examples
Natural Language Processing
Learn Python-based NLP techniques including text resource access, word cloud generation, WordNet usage, and word processing methods like stemming and lemmatization.
Natural Language Processing
This course provides an understanding of natural language processing, its tools, techniques, philosophy and principle. It will cover Tokenization, Stop Words and Word Embedding.
Textblob
Explore TextBlob for NLP tasks: preprocessing, sentiment analysis, and text vectorization. Learn to clean text data, handle stopwords, and implement sentiment analysis using Python.
State of the Art Natural Language Understanding at Scale - David Talby
Explore state-of-the-art NLP techniques using Apache Spark, covering core algorithms, distributed pipelines, and performance optimizations for large-scale language understanding applications.
Complete Natural Language Processing Tutorial in Python
Comprehensive Python tutorial covering NLP fundamentals, techniques, and state-of-the-art models. Includes practical examples using popular libraries and hands-on implementation of advanced concepts.
Introduction to NLP and Topic Modeling
Explore NLP basics, advanced techniques like word vectors, and topic modeling. Learn pre-processing, tools, and methodologies to start NLP modeling and understand topic analysis.
Complete Natural Language Processing Machine Learning Tutorial
Comprehensive NLP tutorial covering tokenization, preprocessing, encoding, and word embeddings. Practical implementations and in-depth explanations for beginners and intermediate learners.
Rich Morphological Modeling for Multi-lingual HLT Applications - 2016
Explore advanced morphological modeling techniques for diverse languages, enhancing HLT applications through innovative approaches like neural networks and cross-lingual projection.