Natural Language Processing
The Study Of Natural Language Processing Involves Computers Understanding And Interacting With Human Speech. Key Features Include Textual Tokenization, Sentiment Analysis, Machine Translation, And Speech Recognition. What Areas Can Be Used Such As Chatbots, Language Translation Or Content Generation Using Transformer Models. This Course Will Explore NLP Basics, Real World Application And Recent Developments In The Field.
Curriculum
- What Is Nlp?
- Applications Of Nlp
- Nlp Vs. Traditional Text Processing
- Key Concepts In Nlp (Tokens, Lemmas, Stemming)
- Text Preprocessing Techniques (Tokenization, Stopword Removal, Etc.)
- Part-Of-Speech Tagging
- Named Entity Recognition (Ner)
- Syntactic Parsing (Dependency And Constituency Parsing)
- Semantic Analysis (Sentiment Analysis, Word Sense Disambiguation)
- Text Classification
- Bag-Of-Words Model
- Tf-Idf (Term Frequency-Inverse Document Frequency)
- Word Embeddings (Word2vec, Glove)
- Topic Modeling (Lda, Latent Semantic Analysis)
- Recurrent Neural Networks (Rnn) In Nlp
- Simple Rnns
- Long Short-Term Memory (Lstm)
- Gated Recurrent Unit (Gru)
- Convolutional Neural Networks (Cnn) For Text
- Attention Mechanisms And Transformers
- Introduction To Attention
- Sequence-To-Sequence Models
- Transformers (Bert, Gpt, Etc.)
- Transfer Learning In Nlp
- Pretrained Language Models (Bert, Gpt-3)
- Fine-Tuning Pretrained Models
- Named Entity Recognition With Transformers
- Question Answering Systems
- Summarization Techniques
- Language Generation And Conversational Agents
- Machine Translation
- Text Classification And Sentiment Analysis
- Speech Recognition And Synthesis
- Chatbots And Virtual Assistants
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