What is Natural Language Processing - NLP Full Course
Comprehensive introduction to Natural Language Processing, covering key concepts, techniques, and applications using Python, with hands-on projects in sentiment analysis and chatbot development.
Object Recognition
Explore object recognition techniques, from Bag of Words to selective search, covering codebooks, visual synonyms, and object localization for effective image classification.
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
Drop Your Decoder: Pre-training with Bag-of-Word Prediction for Dense Passage Retrieval
Explore innovative pre-training techniques for dense passage retrieval using bag-of-word prediction methods, focusing on decoder-free approaches for improved efficiency.
Using Your Powers for Good - Data Science in the Social Sector
Explore data science applications in the social sector, including disaster relief and education. Learn techniques like logistic regression and ensemble building for impactful machine learning projects.
Introduction to Text Analytics with R - Overview
Explore text analytics with R: tokenization, feature engineering, classification models. Learn data cleaning, analysis, and visualization using a spam dataset. Practical introduction for aspiring data scientists.
Introduction to Language Modeling: From N-grams to Neural Networks
Explore the evolution of language modeling, from n-grams to neural architectures, covering key concepts like embeddings, transformers, and RLHF implementations.
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.
Fake News Classifier with Machine Learning and NLP - Part 1
Learn to build a fake news classifier using machine learning and NLP techniques. Covers data preprocessing, feature extraction, and multinomial classification algorithms for detecting misinformation.
Machine Learning Model Retraining Approach - Incremental and Continuous Model Training
Explore incremental machine learning model training for continuous updates, addressing model drift and improving performance over time.
Neural Nets for NLP 2019 - Convolutional Neural Networks for Language
Explore convolutional neural networks for NLP, covering bag-of-words, context windows, sentence modeling, stacked and dilated convolutions, and structured convolution for sentence pairs.
Neural Nets for NLP 2017 - Convolutional Networks for Text
Comprehensive lecture on convolutional networks for text, covering bag-of-words, n-grams, applications, stacked and dilated convolutions, structured convolution, sentence pair modeling, and CNN visualization techniques.
Neural Nets for NLP 2020 - Convolutional Neural Networks for Text
Comprehensive exploration of convolutional neural networks for text processing, covering key concepts, applications, and advanced techniques in natural language processing.
Building a Naive Bayes Text Classifier with scikit-learn
Learn to build a Naive Bayes text classifier using scikit-learn, exploring preprocessing techniques and feature extraction methods for effective spam detection in YouTube comments.
Word Embeddings from Deep Space Nine using Machine Learning
Explore word embedding techniques using Star Trek Deep Space Nine scripts, from Bag of Words to BERT, with practical insights for production environments.
Application of Unsupervised Learning to AC Susceptibility Measurements of High-Temperature Superconductors
Explore unsupervised learning applications in analyzing AC susceptibility measurements of high-temperature superconductors, focusing on clustering techniques and data representation methods.
Linear Structures in Transformer Embedding Spaces
Explore linear structures in Transformer embedding spaces, examining anisotropy, BERT's next sentence prediction, layer behavior, and multi-head attention modules.