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• Understand the concepts of NLP, NLU, NLG, NER and how they are applied
• Understand Artificial Intelligence writing regresions in javascript using Brain and Tensorflow
• Write your own tokenizer and use existing tokenizers from Natural and NLPjs
• Write NLU classifiers in javascript using Brain, Tensorflow, NLPjs Neural, Microsoft LUIS
• Build your own Perceptron in javascript from scratch and your own NLU Classifier, so you will have a deep understand of what is under the hood
• Measure the precision of the different NLPs using a custom corpus and SIGDIAL22
• Understand what is stemming, apply stemmers to the classifiers that you built (Brain, Tensorflow, NLPjs neural, Microsoft LUIS and your own classifier), measure the precision and compare with non-stemmed versions
• Understand Measures in Machine Learning: Confusion Matrix, Accuracy, Precision, Recall, F1-Score
• Different KPIs for NLU: Machine Learning Measures, Confidence, Clarity, TPS, Language Support
• Named Entity Recognition: enum entities, regular expressions, builtins using Microsoft Recognizers and Duckling (extract emails, URLs, dates, numbers...)
• Useful NLP Algorithms: utterance generation from patterns, N-Grams, Language Prediction, Predictive Text, Levenshtein distance, get best similar substring, emoji replacement
• How to build your own chatbot, benchmark it, do it multi-language, publish into console, add a web for talking with the chatbot