Applications of Machine Learning in Text Analysis
( 25 Modules )

Module #1
Introduction to Machine Learning in Text Analysis
Overview of machine learning and its applications in text analysis

Module #2
Text Preprocessing Techniques
Understanding text preprocessing techniques such as tokenization, stemming, and lemmatization

Module #3
Feature Extraction Methods
Introduction to feature extraction methods such as bag-of-words, TF-IDF, and word embeddings

Module #4
Text Classification Fundamentals
Understanding text classification, its types, and evaluation metrics

Module #5
Naive Bayes Text Classification
Implementing Naive Bayes for text classification

Module #6
Support Vector Machines for Text Classification
Implementing SVM for text classification

Module #7
Random Forests for Text Classification
Implementing Random Forests for text classification

Module #8
Deep Learning for Text Classification
Introduction to deep learning for text classification using CNN and RNN

Module #9
Named Entity Recognition (NER)
Understanding NER, its importance, and implementing it using spaCy

Module #10
Part-of-Speech (POS) Tagging
Understanding POS tagging, its importance, and implementing it using spaCy

Module #11
Dependency Parsing
Understanding dependency parsing, its importance, and implementing it using spaCy

Module #12
Sentiment Analysis Fundamentals
Understanding sentiment analysis, its types, and evaluation metrics

Module #13
Sentiment Analysis using Machine Learning
Implementing sentiment analysis using machine learning algorithms

Module #14
Aspect-Based Sentiment Analysis
Understanding aspect-based sentiment analysis and its applications

Module #15
Topic Modeling using Latent Dirichlet Allocation (LDA)
Understanding topic modeling, its importance, and implementing LDA using Gensim

Module #16
Non-Negative Matrix Factorization (NMF) for Topic Modeling
Implementing NMF for topic modeling using scikit-learn

Module #17
Text Summarization Fundamentals
Understanding text summarization, its types, and evaluation metrics

Module #18
Supervised Text Summarization
Implementing supervised text summarization using machine learning algorithms

Module #19
Unsupervised Text Summarization
Implementing unsupervised text summarization using clustering and ranking methods

Module #20
Text Analysis using Graph-Based Methods
Understanding graph-based methods for text analysis and their applications

Module #21
Cross-Lingual Text Analysis
Understanding cross-lingual text analysis, its importance, and its applications

Module #22
Dealing with Imbalanced Datasets in Text Analysis
Understanding the challenges of imbalanced datasets and techniques to handle them

Module #23
Explainable AI in Text Analysis
Understanding explainable AI, its importance, and techniques for model interpretability

Module #24
Case Studies in Text Analysis
Real-world case studies and applications of machine learning in text analysis

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Applications of Machine Learning in Text Analysis career

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Applications of Machine Learning in Text Analysis ( 25 Modules )

Applications of Machine Learning in Text Analysis
( 25 Modules )