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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FAQs
- Basic Python and machine learning knowledge recommended.
- Gradually teaches sequence modeling and NLP techniques.
- Covers RNNs, GRUs, LSTMs, and Siamese networks.
- Hands-on projects reinforce practical skills.
- Suitable for learners aiming to deepen NLP expertise.
- Develop skills for NLP Engineer, ML Engineer, or Data Scientist roles.
- Learn sentiment analysis, NER, text generation, and duplicate detection.
- Gain experience with real-world NLP applications.
- Applicable in tech, healthcare, finance, and e-commerce.
- Supports career growth in AI and data-driven industries.
- Self-paced with lifetime access.
- Modules are 5 hours each, manageable for busy schedules.
- Hands-on projects enhance applied learning.
- Lessons can be revisited for reinforcement.
- Suitable for professionals and students seeking practical NLP skills.
- Build neural networks for sentiment analysis of tweets.
- Generate text using GRU-based language models.
- Implement NER with LSTM networks.
- Identify duplicate questions using Siamese LSTM networks.
- Apply sequence modeling skills to real-world NLP tasks.
- Certificate awarded upon course completion.
- Shareable on LinkedIn and professional networks.
- Validates practical skills in NLP and sequence modeling.
- Recognized by employers in AI, data science, and tech industries.
- Enhances credibility for professional or academic opportunities.
