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Working in a hands-on learning environment, led by our Natural Language Processing expert instructor, students will learn about and explore: You’ll learn the applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries—all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before.

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Last Updated

February 4, 2021

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Certification

Course Description

Natural Language Processing is your guide to building machines that can read and interpret human language. In it, you’ll use readily available Python packages to capture the meaning in text and react accordingly. The course expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions.

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Course Curriculum

    • Natural language vs. programming language 00:00:00
    • The magic 00:00:00
    • Practical applications 00:00:00
    • Language through a computer’s “eyes” 00:00:00
    • A brief overflight of hyperspace 00:00:00
    • Word order and grammar 00:00:00
    • A chatbot natural language pipeline 00:00:00
    • Processing in depth 00:00:00
    • Natural language IQ 00:00:00
    • Challenges (a preview of stemming) 00:00:00
    • Building your vocabulary with a tokenizer 00:00:00
    • Sentiment 00:00:00
    • Bag of words 00:00:00
    • Vectorizing 00:00:00
    • Zipf’s Law 00:00:00
    • Topic modeling 00:00:00
    • From word counts to topic scores 00:00:00
    • Latent semantic analysis 00:00:00
    • Singular value decomposition 00:00:00
    • Principal component analysis 00:00:00
    • Latent Dirichlet allocation (LDiA) 00:00:00
    • Distance and similarity 00:00:00
    • Steering with feedback 00:00:00
    • Topic vector power 00:00:00
    • Neural networks, the ingredient list 00:00:00
    • Semantic queries and analogies 00:00:00
    • Word vectors 00:00:00
    • Learning meaning 00:00:00
    • Toolkit 00:00:00
    • Convolutional neural nets 00:00:00
    • Narrow windows indeed 00:00:00
    • Remembering with recurrent networks 00:00:00
    • Putting things together 00:00:00
    • Let’s get to learning our past selves 00:00:00
    • Hyperparameters 00:00:00
    • Predicting 00:00:00
    • LSTM 00:00:00
    • Encoder-decoder architecture 00:00:00
    • Assembling a sequence-to-sequence pipeline 00:00:00
    • Training the sequence-to-sequence network 00:00:00
    • Building a chatbot using sequence-to-sequence networks 00:00:00
    • Enhancements 00:00:00
    • In the real world 00:00:00
    • Named entities and relations 00:00:00
    • Regular patterns 00:00:00
    • Information worth extracting 00:00:00
    • Extracting relationships (relations) 00:00:00
    • In the real world 00:00:00
    • Pattern-matching approach 00:00:00
    • Grounding 00:00:00
    • Retrieval (search) 00:00:00
    • Generative models 00:00:00
    • Four-wheel drive 00:00:00
    • Design process 00:00:00
    • Trickery 00:00:00
    • In the real world 00:00:00
    • Too much of a good thing (data) 00:00:00
    • Optimizing NLP algorithms 00:00:00
    • Constant RAM algorithms 00:00:00
    • Parallelizing your NLP computations 00:00:00
    • Reducing the memory footprint during model training 00:00:00
    • Gaining model insights with Tensor Board 00:00:00

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