Word2Vec Courses and Certifications

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

Neural Breakdown with AVB

Natural Language Processing Evolution: From Word2Vec and RNNs to GPT - 50 Key Concepts

Explore the evolution of Natural Language Processing through 50 key concepts, from fundamental Word2Vec and RNNs to modern transformer architectures and GPT models, with clear technical explanations.

• YouTube
• 18 minutes
• Self-Paced
• Free Video

Natural Language Processing with Probabilistic Models

1790 ratings at Coursera

This is the Course 2 of the Natural Language Processing Specialization, offered by deeplearning.ai

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

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

Packt

Advanced Semantic Processing

Learn advanced semantic processing: entities, arity and reification, WordNet and word sense disambiguation with the Lesk algorithm, co-occurrence matrices, word vectors, LSA, and Word2vec.

• Coursera
• 3 weeks, 2 hours a week
• Self-Paced
• Paid Course

Mastering Generative AI: Model Foundations and NLP

Gain job-ready skills in generative AI and NLP that employers need. Enhance your resume with in-demand expertise that will help you stand out as an AI engineer, AI developer, or data scientist.

• edX
• 2 weeks, 2-3 hours a week
• Self-Paced
• Free Online Course (Audit)

Python Natural Language Processing

Learn core NLP techniques in Python, from preprocessing and Chinese word segmentation to word embeddings, text classification, and neural text generation.

• XuetangX
• Self-Paced
• Free Online Course

NPTEL;  IIT Madras

Applied Natural Language Processing

Explore practical NLP through corpus operations, probabilistic language models, word vectors, recurrent networks, machine translation, and conversation modeling.

• YouTube
• 1 day 2 hours
• Self-Paced
• Free Video

Interactive Word Embeddings using Word2Vec and Plotly

42 ratings at Coursera

Preprocess a recipe text dataset, generate ingredient word embeddings with Word2Vec in Gensim, and build an interactive Plotly and NetworkX tool for item recommendations.

• Coursera
• 2 hours 25 minutes
• No Longer Offered
• Paid Course

Krish Naik

Complete Natural Language Processing Machine Learning Tutorial

1 rating

Comprehensive NLP tutorial covering tokenization, preprocessing, encoding, and word embeddings. Practical implementations and in-depth explanations for beginners and intermediate learners.

• YouTube
• 3 hours 53 minutes
• Self-Paced
• Free Video

James Briggs

Intro to Dense Vectors for NLP and Vision

An introduction to dense vectors for NLP and vision, with Python examples of sentence embeddings, question answering, and image-text matching.

• YouTube
• 44 minutes
• Self-Paced
• Free Video

AI Engineer

Improving Recommendation Systems and Search in the Age of LLMs

Explore how LLMs revolutionize recommendation systems through semantic IDs, synthetic data augmentation, and unified models with real-world case studies from Amazon, Netflix, Spotify, and Etsy.

• YouTube
• 21 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

OpenSource Connections

How Far is the Empire State Building from the Eiffel Tower? Semantic Vectors in Travel Discovery - Haystack US 2023

Explore semantic vector applications in travel marketplaces for improved recommendations, ranking, and ad optimization. Learn about evolving algorithms and practical advice for implementation.

• YouTube
• 45 minutes
• Self-Paced
• Free Video

UofU Data Science

Vector Semantics and Word Embeddings in Natural Language Processing

Dive into vector semantics and word embeddings, exploring representation learning, distributional hypothesis, word2vec implementation, and their practical applications in natural language processing.

• YouTube
• 1 hour 21 minutes
• Self-Paced
• Free Video