What will you learn in this Natural Language Processing with Sequence Models Course
Train neural networks with word embeddings to perform sentiment analysis of tweets.
Generate synthetic text using Gated Recurrent Unit (GRU) language models.
Implement Named Entity Recognition (NER) using Long Short-Term Memory (LSTM) networks.
Utilize Siamese LSTM networks to identify duplicate questions in datasets.
Program Overview
1. Neural Networks for Sentiment Analysis
⏳ 5 hours
Learn about deep neural networks and build a tweet classifier to determine sentiment polarity
2. Recurrent Neural Networks for Language Modeling
⏳ 5 hours
Understand the limitations of traditional language models and implement RNNs and GRUs to generate text sequences.
3. LSTMs and Named Entity Recognition
⏳ 5 hours
Explore LSTM networks to address the vanishing gradient problem and apply them to extract entities from text.
4. Siamese Networks for Duplicate Question Detection
⏳ 5 hours
Implement Siamese LSTM networks to identify semantically similar questions, enhancing information retrieval systems
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Job Outlook
Prepares learners for roles such as NLP Engineer, Machine Learning Engineer, and Data Scientist.
Applicable in industries like technology, healthcare, finance, and e-commerce.
Enhances employability by providing practical skills in sequence modeling and natural language processing.
Supports career advancement in fields requiring expertise in deep learning and NLP applications.
Specification: Natural Language Processing with Sequence Models
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