Natural Language Processing: NLP In Python with Projects
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- Curriculum
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Interested in Learning Natural Language Processing?
This course is a perfect fit for you.
This course will take you to step by step into the world of Natural Language Processing.
NLP is a subfield of linguistic, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data.
It will cover all common and important algorithms and will give you the experience of working on some real-world projects.
This course will cover the following topics:-
1. Introduction to NLP.
2. Feature Engineering for NLP.
3. Data Cleaning for NLP.
4. Feature Extraction for NLP.
5. Data Visualization for NLP.
6. Text Classification.
We have covered each and every topic in detail and also learned to apply them to real-world problems.
There are lots and lots of exercises for you to practice and also 2 bonus NLP Projects “Sentiment analyzer” and “Drugs Prescription using Reviews“.
In this Sentiment analyzer project, you will learn how to Extract and Scrap Data from Social Media Websites and Extract out Beneficial Information from these Data for Driving Huge Business Insights.
In this Drugs Prescription using Reviews project, you will learn how to Deal with Data having Textual Features, you will also learn NLP Techniques to transform and Process the Data to find out Important Insights.
You will make use of all the topics read in this course.
You will also have access to all the resources used in this course.
Enroll now and become a master in machine learning.
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1What is NLP?Video lesson
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2Why should you learn NLPVideo lesson
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3Applications of NLPVideo lesson
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4Steps to solve NLP ProblemsVideo lesson
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5Introduction to Text ProcessingVideo lesson
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6Popular Libraries used for NLPVideo lesson
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7Quiz on Introduction to NLPQuiz
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8Quiz SolutionVideo lesson
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9Introduction to Feature EngineeringVideo lesson
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10Reading and Summarizing the Text DataVideo lesson
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11Finding the Length, Polarity and SubjectivityVideo lesson
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12Finding the Words, Characters, and Punctuation CountVideo lesson
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13Counting Nouns and Verbs in the TextVideo lesson
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14Counting Adjectives, Adverb, and PronounsVideo lesson
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15Quiz on Feature Engineering for NLPQuiz
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16Quiz SolutionVideo lesson
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17Why Is it so Necessary to Clean the Data?Video lesson
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18Removing Punctuations and NumbersVideo lesson
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19Performing TokenizationVideo lesson
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20Removing Special and accented CharactersVideo lesson
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21Introduction to Stop wordsVideo lesson
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22Stemming and LemmatizationVideo lesson
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23Quiz on Data Cleaning for NLPQuiz
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24Quiz SolutionVideo lesson
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25What is Feature Extraction?Video lesson
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26Introduction to Bag of WordsVideo lesson
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27Introduction to TFIDFVideo lesson
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28Implementing bag of Words and TFIDFVideo lesson
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29Introduction to N Grams AnalysisVideo lesson
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30Implementing N Grams AnalysisVideo lesson
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31Quiz on Feature Extraction for NLPQuiz
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32Quiz SolutionVideo lesson
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33Importance of Data Visualization in NLPVideo lesson
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34Visualizing Polarity and SubjectivityVideo lesson
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35Part-of-Speech TaggingVideo lesson
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36Visualizing Most Frequent WordsVideo lesson
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37Visualizing N-GramsVideo lesson
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38Introduction to Words CloudVideo lesson
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39Quiz on Data Visualization for NLPQuiz
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40Quiz SolutionVideo lesson
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41What is Text Classification?Video lesson
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42Applications for Text ClassificationVideo lesson
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43Best Models for Text ClassificationVideo lesson
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44Implementing a Naive Bayes ClassifierVideo lesson
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45Implementing a SVM ClassifierVideo lesson
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46More Things to TryVideo lesson
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47Quiz on Text Classification using MLQuiz
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48Quiz SolutionVideo lesson
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49Setting up the EnvironmentVideo lesson
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50Understanding the problem statementVideo lesson
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51Scraping Data from Social Media WebsitesVideo lesson
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52Cleaning the dataVideo lesson
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53Creating a Sentiment Analyzer EngineVideo lesson
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54Visualizing resultsVideo lesson
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55Major TakeawaysVideo lesson
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56Quiz on Sentiment AnalysisQuiz
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57Setting up the EnvironmentVideo lesson
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58Understanding the DatasetVideo lesson
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59Understanding the Problem StatementVideo lesson
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60Summarizing the DatasetVideo lesson
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61Unveiling Hidden Patterns from the DatasetVideo lesson
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62Cleaning the ReviewsVideo lesson
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63Calculating Sentiment from ReviewsVideo lesson
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64Calculating Effectiveness and Usefulness of DrugsVideo lesson
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65Analysing the Medical ConditionsVideo lesson
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66Finding Most Useful and Useful Drugs for each ConditionVideo lesson
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67Quiz on Drug PrescriptionQuiz
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