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WIZAPE
Apprentice Mode
10 Modules / ~100 pages
Wizard Mode
~25 Modules / ~400 pages

Natural Language Processing and Cognitive AI
( 25 Modules )

Module #1
Introduction to NLP
Overview of Natural Language Processing, its importance, and applications.
Module #2
NLP Fundamentals
Discussing the basics of human language, linguistics, and cognitive psychology.
Module #3
Text Preprocessing
Cleaning, tokenization, stopword removal, stemming, and lemmatization.
Module #4
Text Representation
Bag-of-words, TF-IDF, word embeddings, and sentence embeddings.
Module #5
Language Modeling
Predicting the next word in a sequence, n-gram models, and neural language models.
Module #6
Text Classification
Spam vs. not spam, sentiment analysis, and topic modeling.
Module #7
Named Entity Recognition
Identifying named entities, parts-of-speech tagging, and dependency parsing.
Module #8
Part-of-Speech Tagging
Tagging words with their corresponding parts of speech.
Module #9
Dependency Parsing
Analyzing sentence structure and grammatical dependencies.
Module #10
Sentiment Analysis
Determining the sentiment of text, from positive to negative.
Module #11
Topic Modeling
Uncovering hidden topics in a corpus of text, using LDA and NMF.
Module #12
Machine Translation
Translating text from one language to another, using rule-based and statistical approaches.
Module #13
Introduction to Cognitive AI
Overview of Cognitive AI, its applications, and relationship with NLP.
Module #14
Cognitive Architectures
SOAR, ACT-R, and LIDA:cognitive architectures for AI systems.
Module #15
Cognitive Reasoning
Reasoning with knowledge representations, rule-based systems, and semantic networks.
Module #16
Cognitive Learning
Machine learning, deep learning, and cognitive-inspired learning algorithms.
Module #17
Natural Language Understanding
Comprehending natural language, using semantic role labeling and event extraction.
Module #18
Question Answering
Answering questions based on natural language input, using knowledge graphs and inference.
Module #19
Dialog Systems
Building conversational systems, using intent recognition and response generation.
Module #20
Multimodal Language Processing
Processing language in multiple modalities, including speech, vision, and text.
Module #21
Explainable AI
Interpreting and explaining AI decisions, using techniques like attention and saliency maps.
Module #22
Ethics in NLP and Cognitive AI
Discussing ethical considerations, bias, and fairness in AI systems.
Module #23
NLP for Social Good
Using NLP and Cognitive AI for social good, including healthcare, education, and environmental applications.
Module #24
Advanced NLP Topics
Exploring advanced topics, including transfer learning, attention mechanisms, and transformers.
Module #25
Course Wrap-Up & Conclusion
Planning next steps in Natural Language Processing and Cognitive AI career


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