Online Course – Certified Professional Internship in Natural Language Processing by DeepLearning.AI

Expertise – 4-part course series

Natural Language Processing (NLP)

Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language. This technology is one of the most widely used areas of machine learning, and is critical for efficiently analyzing vast amounts of unstructured data, especially textual data. As the field of artificial intelligence evolves, so will the demand for experts skilled in building speech and language analysis models, discovering contextual patterns, and extracting insights from text and audio.

What will you learn?

At the end of this specialization, you will be ready to design NLP applications that will:

• Question-answer analyses
• Emotion analysis
• Language translation tools
• Text summary
• Building chatbots

These NLP applications and management will be at the forefront of the exciting future transformation driven by AI.

Guides

This expertise was designed and taught by two experts in NLP, machine learning, and deep learning:

• Younes Ben-Souda Mori – Instructor in artificial intelligence at Stanford University and also helped develop the expertise in deep learning.
• Lukasz Kaiser – Senior Research Scientist on the Google Brain team, and works with the article on Tensorflow, Tensor2Tensor, and Trax libraries.

Hands-on Learning Project

This specialization will provide you with the basic knowledge of machine learning and the advanced techniques required to build advanced NLP systems:

• Use logistic regression, naive Bayes, and word vectors to implement sentiment analysis, complete analogies, translate words, and use location-sensitive smoothing to detect nearest neighbors.
• Use dynamic programming, hidden Markov models, and word embedding to correct misspellings, complete partial sentences, and identify part-of-speech labels for words.
• Use dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks in TensorFlow and Trax to perform advanced sentiment analysis, text generation, named entity recognition, and duplicate question detection.
• Use encoder-decoder, causality, and attention to perform advanced machine translation of entire sentences, text summarization, question answering, and building chatbots. Learn T5, BERT, Transformer, Reformer, and more with Transformers!