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Course Details –:
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What you’ll learn:-
- What are various text processing techniques and their implementation in python.
- Case Study: Role of Hashing in Spam Filter compared to Countvectorizer.
- Implementation: Spam Filter, Article Summarization, Article Classification
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Description:-
Description:-
This course provides a basic understanding of NLP. Anyone can opt for this course. Prior understanding of Machine Learning is required. However, for those who don;t know Machine Learning, I have added Crash Course for Machine Learning in the last Appendix Section. Text Processing like Tokenization, Stop Words Removal, Stemming, different types of Vectorizers, WSD, etc are explained in detail with python code. Also difference between CountVectorizer and Hashing in Spam Filter.
Below Topics are covered till now.
Chapter – Text Preprocessing
Below Text Preprocessing Techniques
– Tokenization, Stop Words Removal, N-Grams, Stemming, Word Sense Disambiguation
– Count Vectorizer, Tfidf Vectorizer. Hashing Vector
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Chapter – Text Preprocessing – Python Code
Below Text Preprocessing Techniques with Python code
– Tokenization, Stop Words Removal, N-Grams, Stemming, Word Sense Disambiguation
– Count Vectorizer, Tfidf Vectorizer. Hashing Vector
Chapter – Spam Filter
– Concept with Python Code
Chapter – Summarizing Article
– Concept with Python Code
Appendix – Machine Learning Crash Course
– Machine Learning and Its Types
– Supervised Regression – Simple LR, Performance Measurement of Regression
– Supervised Classification – Logistic Regression, K-Nearest Neighbours with Python, Naive Bayes
– UnSupervised Learning – K-Means Clustering with Python
Who this course is for:
- People willing to learn NLP and looking forward to build career in Machine Learning.
- People who like coding as this course include Bit Heavy Python Coding in some sections.
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