What you'll Learn
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Learn to build practical NLP models for real-world applications.
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Dive deep into self-attention and multi-head attention techniques.
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Understand how Transformers work in modern NLP.
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Explore about RNN and advanced RNN architecture such as GRU and LSTM.
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Apply your knowledge through hands-on exercises that mimic real-world challenges like text classification, text generation and translation.
Who Should Enroll?
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Individuals looking to expand their skill set and apply NLP across different industries.
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For those setting out on their journey to mastering text data analysis and making a mark in the tech world.
About the Instructor
Apoorv Vishnoi Head Training Vertical, Analytics Vidhya
Apoorv is a seasoned AI professional with over 14 years of experience. He has founded companies, worked at start-ups and mentored start-ups at incubation cells.
FAQ's
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What is Natural Language Processing (NLP)?
NLP is the field of computer science focused on enabling machines to understand, interpret, and generate human language. It powers applications like chatbots, translation services, and sentiment analysis.
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What are Recurrent Neural Networks (RNN)?
RNNs are neural networks designed to work with sequences. They maintain a form of memory of previous inputs, which is useful for processing language where the order of words matters.
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Will I receive a certificate upon completion?
Yes, you will receive a certificate of completion after successfully finishing the course and assessments.
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What is self-attention and how does it work?
Self-attention is a mechanism that helps a model determine the relevance of each word in a sentence relative to others. It allows the model to weigh different words based on their importance, capturing context and relationships effectively.
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What are Transformers in NLP?
Transformers are a modern neural network architecture that uses self-attention mechanisms to process words in a sentence simultaneously instead of one at a time. This approach allows them to handle long-range dependencies more efficiently and is the backbone of many state-of-the-art NLP models.
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What is multi-head attention?
Multi-head attention extends the self-attention mechanism by running several attention processes in parallel. This enables the model to learn various relationships and features from the data simultaneously, improving performance and robustness.