Word Embeddings Courses and Certifications

Creating Sequence Models and Transformers

Build sequence models from tokenization and embeddings through RNNs, attention, and transformers, culminating in a transformer-based Q&A system.

• Udacity
• 11 hours
• Self-Paced
• Paid Course

Computing With Natural Language

Learn advanced techniques like word embeddings, deep learning attention, and more. Build a machine translation model using recurrent neural network architectures.

• Udacity
• 25 hours
• Self-Paced
• Paid Course

UofU Data Science

Neural Classification with Word Embeddings - PyTorch Tutorial

Master neural classification techniques and word embeddings implementation in PyTorch through hands-on practice with essential deep learning concepts and practical applications.

• YouTube
• 1 hour 23 minutes
• Self-Paced
• Free Video

UofU Data Science

Word Embeddings - Word2Vec Skip-Gram

Dive into word2vec skip-gram architecture to transform words into meaningful vector representations for natural language processing and machine learning applications.

• YouTube
• 1 hour 8 minutes
• Self-Paced
• Free Video

Devoxx

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.

• YouTube
• 43 minutes
• Self-Paced
• Conference Talk

Natural Language Processing in TensorFlow

6538 ratings at Coursera

Build NLP systems in TensorFlow: tokenize text with Tokenizer and pad_sequences, train word embeddings for movie-review sentiment, and generate poetry with RNNs, GRUs and LSTMs.

• Coursera
• 4 weeks, 6 hours a week
• Self-Paced
• Paid Course

Foundations of NLP Data Processing

Build NLP preprocessing foundations through text cleaning, classical vectorization, tokenization, and word embeddings.

• CodeSignal
• 1 hour
• Self-Paced
• Free Certificate

Enhancing the Word Play Game with new functionalities

Enhance a word prediction game with semantic scoring, personalized leaderboards, and Flask-powered server functionality.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Natural Language Processing with Classification and Vector Spaces

4647 ratings at Coursera

This is the Course 1 of the Natural Language Processing Specialization, offered by deeplearning.ai

• Coursera
• 4 weeks, 8 hours a week
• Self-Paced
• Paid Course

Career Certificate

Natural Language Processing

6208 ratings at Coursera

Build NLP applications for sentiment analysis, question answering, machine translation and text summarization using classification, vector spaces, probabilistic models, sequence models and attention models.

• Coursera
• 12 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 4 courses

Applied Unsupervised Learning in Python

Apply unsupervised learning in Python: reduce dimensions with PCA and t-SNE, cluster with k-means and DBSCAN, model topics with LDA, and impute missing data.

• Coursera
• 4 weeks, 8 hours a week
• Self-Paced
• Paid Course

Stanford CS224N - Natural Language Processing with Deep Learning 2023

Master cutting-edge neural networks for NLP, from word vectors to Transformers, with hands-on tutorials covering RNNs, attention mechanisms, and modern language models.

• YouTube
• 1 day 5 hours 12 minutes
• Self-Paced
• Free Video

Stanford CS224N - Natural Language Processing with Deep Learning - Winter 2021

Master neural networks, transformers, and attention mechanisms for text processing, translation, and language generation through Stanford's comprehensive deep learning approach.

• YouTube
• 1 day 7 hours 31 minutes
• Self-Paced
• Free Video

Natural Language Processing with Deep Learning in Python

8590 ratings at Udemy

Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets

• Udemy
• 12 hours 3 minutes
• Self-Paced
• Paid Course

Fundamentals of Natural Language Processing

20 ratings at Coursera

Build N-gram language models, logistic regression text classifiers, and word representations: tokenization, smoothing, stochastic gradient descent, TF-IDF, PMI, LSA, and Word2Vec.

• Coursera
• 4 weeks, 6 hours a week
• Self-Paced
• Paid Course