Tutorials > Natural Language Processing (Nlp) With Python And Nltk 2020

Natural Language Processing (Nlp) With Python And Nltk 2020


Date: 14 August 2022 | Posted by: voska89
Natural Language Processing (Nlp) With Python And Nltk 2020
Last updated 1/2020
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.27 GB | Duration: 3h 33m
Master Natural Language with Python and NLP using Spam Filter detection


What you'll learn
Natural Language Processing using Python
Requirements
Basic programming skills in Python. Familiarity with numPy, Pandas and matplotlib would be helpful but not required.
Description
Natural Language Processing or NLP is a very popular field and has lots of applications in our daily life. From typing a message to auto-classification of mails as Spam or not-spam NLP is everywhere.NLP is a field concerned with the ability of a computer to understand, analyze, manipulate and potentially generate human language. In this course we study about NLP and use the NLP toolkit or NLTK in Python.The course contains following:Introduction to NLP and NLTKNLP PipelineReading raw dataCleaning and Pre-processingTokenizationVectorizationFeature EngineeringTraining ML Algorithm for Classifying Spam and non-spam messagesThis course would be very useful for Applied Machine Learning Scientists and Data Scientists who are working on NLP/NLU.
Overview
Section 1: Introduction
Lecture 1 Introduction to NLP
Lecture 2 NLTK Introduction
Section 2: Reading and Cleaning Data
Lecture 3 Structured vs Unstructured Data
Lecture 4 Reading Text data
Lecture 5 Exploring the Data
Lecture 6 NLP Pipeline for Text Data
Lecture 7 Removing Punctuation | Cleaning | Pre-processing
Lecture 8 Tokenization
Lecture 9 Removing Stop Words
Lecture 10 Stemming
Lecture 11 Porter Stemmer in NLTK
Lecture 12 Lemmatization
Lecture 13 WordNet Lemmatizer in NLTK
Section 3: Vectorizing Data
Lecture 14 Vectorization
Lecture 15 Count Vectorization
Lecture 16 N-Grams Vectorization
Lecture 17 TF-IDF Vectorization (Term Frequency Inverse Document Frequency)
Section 4: Feature Engineering
Lecture 18 Feature Engineering - Introduction
Lecture 19 Feature Creation
Lecture 20 Feature Evaluation
Lecture 21 Power Transformations - Box Cox Transformation
Section 5: Building Machine Learning Classifier
Lecture 22 Evaluation Metrics - Accuracy, Precision and Recall
Lecture 23 K-Fold Cross-Validation
Lecture 24 Random Forest - Introduction
Lecture 25 Building a basic Random Forest model
Lecture 26 Random Forest with holdout test
Data scientists, Applied Machine Learning engineers and Software engineers.
Homepage
https://www.udemy.com/course/natural-language-processingnlp-with-python-and-nltk/


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