Natural Language Processing Bootcamp in Python

سرفصل های دوره

Learn the fundamentals of Text Mining and NLP using Text Processing, NLTK, Sentiment Analysis and Neural Networks

1 - Course Introduction

1 - Course Scripts.txt

1 - DareData Website.txt

1 - Introduction

1 - Ivos Github.txt

1 - LinkedIn Ivo.txt

2 - Note Course is being Updated during 2023

3 - IMPORTANT LECTURE Dont skip this one

4 - Course Materials and Speed Up.html

2 - Installing Anaconda and Initial Setup

5 - Link to slides.txt

5 - Slides Setting up the Environment

6 - Anaconda Distribution Download Link.txt

6 - Installing the Anaconda Distribution

7 - Importing an Environment to Anaconda

8 - Creating an Environment from Scratch and Installing Individual Libraries

9 - Alternative Running Notebooks on Google Colab

3 - Optional for Beginners Python Basics MiniCourse

10 - Link to Slides.txt

10 - Slides Python Basics Course Part 1

11 - Link to Slides.txt

11 - Slides Python Basics Course Part 2

12 - Getting Started Jupyter Notebook Overview

13 - Using Python as a Calculator Exploring Integers and Floats

14 - Exploring Python Libraries Modules Using the Math Library

14 - Python Math Library Documentation.txt

15 - Python Strings and Indexes

16 - Python Lists

17 - Discussing Methods and the Mutability Property

18 - Python Sets

19 - Python Dictionaries

20 - Python Tuples

21 - Control Flow If Statements

22 - Control Flow Python Loops

23 - Python Functions

24 - Numpy Cheat Sheet.txt

24 - Numpy Documentation.txt

24 - Numpy Overview

25 - Pandas Cheat Sheet.txt

25 - Pandas Documentation.txt

25 - Pandas Overview

26 - Tutorial How to Complete the Exercises

27 - Exercise Solutions Code Along Lecture

4 - Optional for Beginners Basic Text Processing

3 - Working with Text Quiz.html

28 - Slides Basic Text Processing

29 - Why Computers dont understand words as we do

30 - String Indexing

31 - Combining Strings

32 - Iterating Strings and Format Method

33 - Testing if String is in Sentence and Escaping Characters

34 - String Methods 1 Sentence Length Conversions Casing Methods and IsAlpha

35 - String Methods 2 Split Strip and Join

36 - String Methods 3 Capitalize Replace Count and Find

37 - Exercise Solutions Code Along Lecture String Basics

5 - Exploring NLTK Natural Language Toolkit

38 - Link to Slides.txt

38 - Slides NLTK Intro Tokenizers and Text Normalization

39 - Link to Slides.txt

39 - Slides NLTK POS and NGrams

40 - Natural Language Toolkit Library.txt

40 - Section Introduction

41 - Intro to Tokenization and Sentence Tokenizer

41 - Natural Language Toolkit Library.txt

41 - Punkt Sentence Tokenizer.txt

42 - TreeBank Word Tokenizer.txt

42 - Word Tokenizer Example

43 - Cleaning our Tokens Removing Punctuation and lowercase

44 - FreqDist NLTK Function

45 - Introduction to Stemming Porter Lancaster and Snowball Stemmer

45 - Porter Stemmer Documentation.txt

45 - Stemming Corpus Medium Article.txt

46 - Stemming Application Example

47 - Introduction to Lemmatization

48 - PartofSpeech POS Tagging

48 - Perceptron POS Tagging Original Blog Post.txt

49 - Training our own POS Tagger Part 1

50 - Training our own POS Tagger Part 2 UniGram Tagger

51 - Training our own POS Tagger Part 3 BiGram Tagger

52 - Lemmatization and POS Tagging

53 - Stop Words

54 - NGrams Concept

55 - Exercise Solutions Code Along Lecture NLTK

6 - Project 1 Analyzing IMDB Reviews

56 - Project Description Analyzing IMDB Reviews

57 - Link to the Project Materials.html

7 - Word Vectors Intuition

58 - Slides Word Vectors Intuition

59 - Introduction to Word Vectors and creating OneHot Vectors

59 - Word Vectors Intuition Medium Article.txt

60 - Initializing CoOccurence Matrix

61 - Filling CoOccurence Matrix

62 - Cosine Similarity Explanation.txt

62 - Exploring Cosine Similarity

63 - Visualizing Word Vectors

64 - Exercise Solutions Code Along Lecture 1 Word Vectors

65 - Exercise Solutions Code Along Lecture 2 Word Vectors

8 - Optional Reading Text Data into Python

66 - Link to slides.txt

66 - Slides Reading Text Data into Python

67 - Disaster Tweets Source Code.txt

67 - Read Data from a CSV File Using Pandas

68 - Read Data from a CSV File Using Python CSV

69 - Paper where the data is based on.txt

69 - Read Data from a TXT File

70 - 1984 Wikipedia Page.txt

70 - Scraping a Web Page using Requests and BeautifulSoup Wikipedia Example

71 - Scraping a Web Page using Requests and BeautifulSoup Yahoo Finance Example

72 - Scraping a Web Page Errors in Request

73 - Scraping a Web Page using Specific Libraries

9 - Continuous Bag of Words Implementation and Word2Vec

74 - Link to slides.txt

74 - Slides Neural Network Definition and Word2Vec

75 - Continuous Bag of Words Model CBOW Introduction

75 - Word2Vec Original Paper.txt

76 - CBOW Creating Vocab and OneHot Vectors

77 - CBOW Building Features X and Target Variable y

78 - Neural Network Introduction and Diagram

79 - A cool intuition on Neural Networks.txt

79 - CBOW Training the Neural Network

80 - CBOW Obtaining Word Vectors Embeddings

81 - Extracting Wikipedia Data for CBOW Model

82 - Building Context from Wikipedia Data

83 - Turning Word and Context Into Mathematical Vectors

84 - Fitting Neural Network on Wikipedia Data

85 - Accuracy of a Model Intuition.txt

85 - Performance of Neural Network and Predicting a Word Given a Context

86 - Retrieving Word Embeddings and Word Similarities

87 - Gensim Library.txt

87 - Loading the Word2VecModel

88 - Word2Vec Operations with Vectors Analogies

89 - Word2Vec Visualizing Vectors using PCA

90 - Exercise Solutions Code Along Lecture Word Vectors using Neural Networks

10 - Project 2 The Python Archaelogist

91 - Project Description The Python Archaelogist

92 - Link to Project Materials.html

11 - Text Representation

93 - Slides Text Representation

93 - Text Representation Medium Article.txt

94 - Disaster Tweets Page.txt

94 - Reading the Tweets File

95 - Binary Vectorizer

95 - Count Vectorizer Document.txt

96 - Count Vectorizer

97 - TFIDF Vectorizer

98 - Creating Document Vectors via Word Embeddings

99 - Exercise Solutions Text Representation Part 1

100 - Exercise Solutions Text Representation Part 2

12 - Text Classification

101 - Slides Text Classification

102 - Intro to Text Classification and Loading Positive Negative Reviews into Python

103 - PreProcessing Text for Text Classification

104 - Exploratory Data Analysis Log Ratio and Word Influence

105 - Stemming and Vectorizing the Reviews

106 - Creating WordVec Features and Target Array

107 - Logistic Regression Intuition and Manually Tweaking Weights

108 - Train and Test Split

109 - Fitting and Evaluating Model

110 - Obtaining the WeightsCoefficients of Regression Influence of Tokens

111 - Training Model Using Word2Vec Features

112 - Confusion Matrix Example

113 - Naive Bayes Training

114 - Predicting New Reviews Sentiment

115 - Exercise Solutions Text Classification

13 - Text Generation Markov Chain Models

116 - Slides Text Classification

117 - Introduction to Text Generation Tokenizing Sentence

118 - Text Generation Building the Transition Matrix

119 - First Attempt at Generating Text Using Transition Matrix

120 - Creating the Transition Matrix for Wikipedia Data

121 - Generating Text from Wiki Data Sampling from Top N Words

122 - Exercise Solutions Text Generation

14 - Course Ending

123 - Bonus Lecture Other Courses.html

124 - Course Feedback.html

125 - Thank you

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تولید کننده: Udemy