From customer-service chatbots to AI-enabled virtual assistants, the demand for technology that employs natural language processing (NLP) is growing at a phenomenal rate. According to Payscale, technology professionals with the training and skills to implement NLP applications now earn an average annual salary of $109,000.
Natural Language Processing, a 10-week Spain WhatsApp Number online program available through the Executive Education program from Carnegie Mellon University School of Computer Science, provides both a fundamental understanding of NLP and an overview of its applications.
This 10-week online program will give you a foundational understanding of NLP. After completing the program, you will be able to:
- Learn key machine learning concepts Spain WhatsApp Number List and deep learning methods to build cutting-edge NLP systems in any specific domain
- Develop graphical models for lemmatization – a key step in many NLP tasks
- Synthesize n-gram language models and make qualitative/quantitative comparison of simple to complex n-gram models
- Utilize neural networks to label parts of speech (POS) and named entities (NER) in English and other languages
- Train dependency parsers from treebanks and use them to perform NLP tasks
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Module 1:
Introduction to NLP
Examine the what, why, and how of NLP, its key applications, and associated challenges. You will:
- Learn the definition of NLP
- Review current and future applications
Module 6:
Sequence Labeling—Speech Tagging and Named Entity Recognition
Study the formal grammars and the languages they generate to determine which kind of language applies to a particular NLP task. You will:
- Explore the process of mapping strings of words to strings of tags
- Implement POS tagging with eight foreign languages
Module 2:
Linguistic Morphology
Explore the basics of linguistics and morphology and the importance of morphology as both a problem and resource in NLP. Plus, learn to distinguish prefixes, suffixes, and infixes and how to construct a simple FST for lemmatization. You will:
- Define identifying characteristics of morphemesx
- Evaluate tools and resources related to morphological analysis
Module 7:
Lexical Semantics
Explore the various building blocks of deep learning for NLP components, and learn how to build and train a deep neural network. You will:
- Determine the ideal lexical approach to ascertain word meaning
- Predict word meanings using cosine similarities
Module 3:
Language Models and Smoothing
Learn language modeling and its application in NLP and how to use different language models for estimating the probability distribution of various linguistic units. You will:
- Discover how computational language models are used for prediction, scoring, and correction
- Evaluate tools and resources for language model construction
Module 8:
Word Embeddings
Learn the basics of lexical semantics and different ways of looking at a word’s meaning as well as how to compute co-occurrence matrices. You will:
- Evaluate reasons and motivations for using word embeddings
- Explore improvement techniques for word embeddings
