{"product_id":"leveraging-deep-learning-for-natural-language-processing-course","title":"Leveraging Deep Learning for Natural Language Processing Course","description":"\u003cdiv\u003e\u003cp\u003eIn this \u003cstrong\u003eNatural Language Processing course\u003c\/strong\u003e, you will learn how to navigate the various text pre-processing techniques and select the best neural network architecture for Natural Language Processing.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eLeveraging Deep Learning for Natural Language Processing Course Benefits\u003c\/h3\u003e\n\u003cul\u003e\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIn this course, you learn how to:\u003c\/strong\u003e \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eUnderstand various pre-processing techniques for deep learning problems.\u003c\/li\u003e\n\u003cli\u003eBuild a vector representation of text using word2vec and GloVe.\u003c\/li\u003e\n\u003cli\u003eCreate a named entity recognizer and parts-of-speech tagger with Apache OpenNLP.\u003c\/li\u003e\n\u003cli\u003eBuild a machine translation model in Keras, a deep learning API.\u003c\/li\u003e\n\u003cli\u003eDevelop a text Career Stages application using Long short-term memory (LSTM).\u003c\/li\u003e\n\u003cli\u003eBuild a trigger word detection application using an attention model.\u003c\/li\u003e\n\u003cli\u003eTest your knowledge in the included end-of-course exam.\u003c\/li\u003e\n\u003cli\u003eContinue learning and face new challenges with after-course one-on-one instructor coaching.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eNatural Language Processing Course Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 1: Introduction to Natural Language Processing\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn about:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe basics of Natural Language Processing and its applications\u003c\/li\u003e\n\u003cli\u003ePopular text pre-processing techniques\u003c\/li\u003e\n\u003cli\u003eWord2vec and Glove word embeddings Sentiment classification\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 2: Applications of Natural Language Processing\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn about: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eNamed Entity Recognition and how to develop it using popular libraries\u003c\/li\u003e\n\u003cli\u003eParts of Speech Tagging\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 3: Introduction to Neural Networks\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn about:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eBasics of Gradient descent and backpropagation.\u003c\/li\u003e\n\u003cli\u003eFundamentals of Deep Learning, Keras and deploying a Model-as-a-Service (MaaS)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 4: Foundations of Convolutional Neural Networks (CNN)\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eIn this module, you will learn about CNN architecture, application areas, and implementation using Keras.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 5: Recurrent Neural Networks (RNN)\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eIn this module, you will learn about RNN architecture, application areas, vanishing gradients, and implementation using Keras.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 6: Gated Recurrent Units (GRU)\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eIn this module, you will learn about GRU architecture, application areas, and implementation using Keras.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 7: Long Short-Term Memory (LSTM)\u003c\/h4\u003e\n\u003cul\u003e\n\u003cli\u003eIn this module, you will learn about LSTM architecture, application areas, and implementation using Keras.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 8: State of the Art in Natural Language Processing\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003ePerform Attention Model and Beam search\u003c\/li\u003e\n\u003cli\u003eUse End to End models for speech processing\u003c\/li\u003e\n\u003cli\u003eUse Dynamic Neural Networks to answer questions\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eModule 9: A Practical NLP Project Workflow in an Organization\u003c\/h4\u003e\n\u003cp\u003eIn this module, you will learn how to:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eAcquire data using free datasets and crowdsourcing\u003c\/li\u003e\n\u003cli\u003eUse cloud infrastructure, such as the Google collab notebook, to train deep learning NLP models\u003c\/li\u003e\n\u003cli\u003eWrite a Flask framework server Rest\u003cabbr title=\"Application Programming Interface\"\u003eAPI\u003c\/abbr\u003e to deploy a model\u003c\/li\u003e\n\u003cli\u003eDeploy the web service on cloud infrastructures such as Amazon Elastic Compute Cloud (Amazon EC2) or Docker Cloud\u003c\/li\u003e\n\u003cli\u003eLeverage the promising techniques in \u003cabbr title=\"Natural Language Processing\"\u003eNLP\u003c\/abbr\u003e, such as Bidirectional Encoder Representations from Transformers (BERT)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e","brand":"Logical Operations","offers":[{"title":"268A54US \/ 2026-08-26T09:00:00 \/ Herndon, VA","offer_id":47534217461979,"sku":"US-1278-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"26BA95US \/ 2026-11-23T09:00:00 \/ Herndon, 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