Natural Language Processing (Nlp) With Python And Nltk 2020

[LeeAndro]

Last updated 1/2020MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: 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 EeeringTraining 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 Str 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 Eeering

Lecture 18 Feature Eeering - 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 eeers and Software eeers.

HomePage:

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