The Natural Language Processing Specialization is a comprehensive program focused on cutting-edge NLP techniques. This specialization consists of four hands-on courses taught by industry experts, offering participants updated lessons and techniques as of October ’21. Participants will be immersed in the world of NLP and its applications, equipping them with the skills to analyze and manipulate human language effectively.
What You’ll Learn
- Use logistic regression, naïve Bayes, and word vectors for sentiment analysis, analogies implementation, and word translation.
- Implement dynamic programming, hidden Markov models, and word embeddings for autocorrection, autocomplete, and part-of-speech tagging.
- Apply recurrent neural networks, LSTMs, GRUs, and Siamese networks in Trax for sentiment analysis, text generation, and named entity recognition.
- Utilize encoder-decoder, causal, and self-attention for machine translation, text summarization, chatbot development, and question-answering.
[adinserter block=”5″]
Skills You’ll Gain
- Data Science
- Natural Language Processing
- Machine Learning
- Deep Learning
- Transformers
- Sentiment Analysis
- Word Embeddings
- Attention Models
[adinserter block=”10″]
Natural Language Processing (NLP) is a subfield that combines linguistics, computer science, and artificial intelligence to interpret and manipulate human language using algorithms. This specialization is designed to introduce participants to NLP applications that perform sentiment analysis, question-answering, language translation, text summarization, and chatbot development. By the end of the program, participants will have the expertise to build models that analyze speech and language, uncover contextual patterns, and gain insights from text and audio data. The specialization empowers learners to develop an understanding of key NLP concepts, including various models and techniques such as logistic regression, word embeddings, recurrent neural networks, attention models, and more. Participants will also learn about Transformers, sentiment analysis, machine translation, and other essential topics in NLP. **Key Highlights:** – Learn logistic regression, naive Bayes, and word vectors for sentiment analysis and translation. – Gain expertise in dynamic programming, hidden Markov models, and word embeddings for tasks like autocorrection and part-of-speech tagging. – Master recurrent neural networks, LSTMs, GRUs, and Siamese networks for advanced applications like sentiment analysis and text generation. – Explore encoder-decoder, causal, and self-attention mechanisms for tasks such as machine translation, summarization, and chatbot development. This specialization is ideal for individuals interested in pursuing careers in machine learning, deep learning, and NLP, as well as those looking to enhance their skills in analyzing unstructured data and building AI-powered applications.
[adinserter block="3"]
[adinserter block="4"]
