Natural Language Processing with AI
( 25 Modules )

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

Module #2
NLP Fundamentals
Basic concepts of NLP, including tokenization, stemming, and lemmatization

Module #3
Text Preprocessing
Techniques for cleaning, normalizing, and transforming text data

Module #4
Text Representation
Introduction to text representation methods, including bag-of-words and word embeddings

Module #5
Word Embeddings
In-depth exploration of word embeddings, including Word2Vec and GloVe

Module #6
Language Models
Introduction to language models, including Markov chains and n-gram models

Module #7
Deep Learning for NLP
Overview of deep learning techniques for NLP, including recurrent neural networks (RNNs) and long short-term memory (LSTM) networks

Module #8
Convolutional Neural Networks (CNNs) for NLP
Application of CNNs to NLP tasks, including text classification and sentiment analysis

Module #9
Recurrent Neural Networks (RNNs) for NLP
In-depth exploration of RNNs for NLP tasks, including language modeling and machine translation

Module #10
Long Short-Term Memory (LSTM) Networks for NLP
Application of LSTMs to NLP tasks, including sentiment analysis and question answering

Module #11
Sequence-to-Sequence Models
Introduction to sequence-to-sequence models for NLP tasks, including machine translation and text summarization

Module #12
Attention Mechanisms
In-depth exploration of attention mechanisms for NLP tasks, including machine translation and question answering

Module #13
Named Entity Recognition (NER)
Introduction to NER, including rule-based and machine learning approaches

Module #14
Part-of-Speech (POS) Tagging
Introduction to POS tagging, including rule-based and machine learning approaches

Module #15
Dependency Parsing
Introduction to dependency parsing, including constituency parsing and dependency grammar

Module #16
Sentiment Analysis
In-depth exploration of sentiment analysis, including machine learning and deep learning approaches

Module #17
Topic Modeling
Introduction to topic modeling, including Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF)

Module #18
Text Classification
In-depth exploration of text classification, including binary and multi-class classification

Module #19
Information Extraction
Introduction to information extraction, including named entity recognition, event extraction, and relationship extraction

Module #20
Question Answering
In-depth exploration of question answering, including rule-based and machine learning approaches

Module #21
Machine Translation
Introduction to machine translation, including statistical machine translation and neural machine translation

Module #22
Dialog Systems
Introduction to dialog systems, including chatbots and conversational agents

Module #23
NLP for Social Media Analysis
Application of NLP to social media analysis, including sentiment analysis and topic modeling

Module #24
NLP for Healthcare
Application of NLP to healthcare, including clinical text analysis and medical information extraction

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

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Natural Language Processing with AI ( 25 Modules )

Natural Language Processing with AI
( 25 Modules )