Introduction to Natural Language Processing
( 25 Modules )

Module #1
Introduction to NLP
Overview of natural language processing, its applications, and importance in AI

Module #2
NLP vs Machine Learning vs Deep Learning
Understanding the differences and relationships between NLP, ML, and DL

Module #3
Text Preprocessing
Basic text preprocessing techniques:tokenization, stop words, stemming, and lemmatization

Module #4
Regular Expressions
Using regular expressions for text pattern matching and extraction

Module #5
Text Representation
Introduction to text representation methods:bag-of-words, TF-IDF, and word embeddings

Module #6
Word Embeddings
In-depth look at word2vec, GloVe, and other word embedding techniques

Module #7
Language Models
Introduction to language models:n-grams, Markov chains, and neural language models

Module #8
Text Classification
Text classification using machine learning algorithms:Naive Bayes, Logistic Regression, and SVM

Module #9
Sentiment Analysis
Sentiment analysis using machine learning and deep learning techniques

Module #10
Named Entity Recognition
Introduction to named entity recognition:concepts, techniques, and tools

Module #11
Part-of-Speech Tagging
Part-of-speech tagging:concepts, techniques, and tools

Module #12
Dependency Parsing
Dependency parsing:concepts, techniques, and tools

Module #13
Machine Translation
Introduction to machine translation:statistical, rule-based, and neural approaches

Module #14
Natural Language Understanding
Natural language understanding:semantic role labeling, event extraction, and question answering

Module #15
Dialogue Systems
Introduction to dialogue systems:chatbots, conversational agents, and voice assistants

Module #16
Speech Recognition
Introduction to speech recognition:acoustic models, language models, and speech-to-text systems

Module #17
NLP for Social Media
NLP for social media:text analysis, sentiment analysis, and topic modeling

Module #18
NLP for Healthcare
NLP for healthcare:medical text analysis, clinical decision support, and patient outcomes

Module #19
NLP for Information Retrieval
NLP for information retrieval:search engines, query processing, and document ranking

Module #20
Ethics in NLP
Ethical considerations in NLP:bias, fairness, and transparency

Module #21
NLP Tools and Frameworks
Overview of popular NLP tools and frameworks:NLTK, spaCy, Stanford CoreNLP, and TensorFlow

Module #22
NLP Project Development
Guided project development:choosing a project idea, designing a project plan, and implementing an NLP project

Module #23
NLP Project Evaluation
Evaluating NLP projects:metrics, methods, and best practices

Module #24
Advanced NLP Topics
Advanced NLP topics:multimodal NLP, transfer learning, and attention mechanisms

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Introduction to Natural Language Processing career

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Introduction to Natural Language Processing ( 25 Modules )

Introduction to Natural Language Processing
( 25 Modules )