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آموزش پردازش زبان طبیعی در زبان Python

دانلود Udemy Natural Language Processing (NLP) Fundamentals in Python

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عنوان اصلی : Natural Language Processing (NLP) Fundamentals in Python

این مجموعه آموزش ویدیویی محصول موسسه آموزشی Udemy است که بر روی 2 حلقه دیسک ارائه شده و به مدت زمان 10 ساعت و 16 دقیقه در اختیار علاقه مندان قرار می گیرد.

در ادامه با برخی از سرفصل های درسی این مجموعه آموزش آشنا می شویم :


Course Introduction :
Introduction
IMPORTANT LECTURE - Don't skip this one
Course Materials and Speed Up

Installing Anaconda and Initial Setup :
[Slides] - Setting up the Environment
Installing the Anaconda Distribution
3 Alternatives to Setup your Environment
[1] - Creating an Environment and Installing Libraries via Anaconda
[2] - Creating an Environment by Importing the YML File
Launching a Jupyter Notebook via Anaconda Navigator
[3] - Creating an Environment via Conda
Installing Libraries via Conda
Launching a Jupyter Notebook via Conda
Testing if your environment is OK
Summary on Environment Setup
Setting up Environment - Quiz

Python Basics Mini-Course :
[Slides] - Python Data Types and Libraries
[Slides] - Objects and Control Flow
[Slides] - Functions, Pandas and Numpy
Jupyter Notebook Overview
Python Integers, Floats and Strings
Python Libraries
Python Lists and Sets
Python Dictionaries and Tuples
Python Control Flow
Python Functions
Numpy Overview
Pandas Overview
Tutorial - How to Complete the Exercises
Quiz - Python Quick Course
Python Quick Course - Exercises

Basic Text Processing :
[Slides] - Basic Text Processing
Manipulating Text Objects
Combining Strings
Iterating Strings and Format Method
Testing if String is in Sentence
Escaping Characters
Sentence Length, Conversions and Casing Methods
Is Alpha, Strip and Split
Join and Capitalize
Replace, Count and Find
Working with Text - Quiz
Working with Text - Exercises

Exploring NLTK (Natural Language Toolkit) :
[Slides] - NLTK Intro and Tokenizers
[Slides] - Text Normalization Techniques
[Slides] - Part-of-Speech Tag and N-Grams
Natural Language Toolkit Introduction and Sentence Tokenizer
Word Tokenizer
Tokenizer Application and Cleaning Tokens
Counting Frequency of Digits in Sentence
FreqDist NLTK Function
Porter, Snowball and Lancaster Stemmers
Stemming Sentences
WordNet Lemmatizer
Part-of-Speech (POS) Tagging
Training a POS Tagger from Scratch - Accessing Tagged Data from Brown Corpus
Training a POS Tagger from Scratch - Unigram Tagger
Training a POS Tagger from Scratch - Bigram Tagger
Plotting the Frequency of Tags in a Sentence
Lemmatization and POS Tagging
Stop Words
N-Grams
Natural Language Toolkit - Quiz
Natural Language Toolkit - Exercises

Reading Text Data into Python :
[Slides] - Reading Text Data into Python
Read Data from a CSV File - Using Pandas
Read Data from a CSV File - Using Python CSV
Read Data from a TXT File
Scraping a Web Page using Requests and BeautifulSoup - Wikipedia Example
Scraping a Web Page using Requests and BeautifulSoup - Yahoo Finance Example
Scraping a Web Page - Errors in Request
Scraping a Web Page using Specific Libraries
Reading Text Data - Quiz
Reading Text Data - Exercises

Word Vectors Intuition :
[Slides] - Word Vectors Intuition - One-Hot Vectors Approach
[Slides] - Word Vectors Intuition - Co-Occurence Matrices
Introduction to Word Vectors
Binary Word Vectors (One-Hot Vectors)
Word Co-Occurence Matrices
Filling Co-Occurence Matrix
Visualizing Word Vectors
Similarity between Words - Cosine
Word Similarities from Co-Occurence Matrix
Word as Vectors - Quiz
Word Vectors - Exercises

Continuous Bag of Words Implementation and Word2Vec :
[Slides] - CBOW Introduction
[Slides] - Neural Network Definition and Word2Vec
Continuous Bag of Words Model (CBOW) Introduction
CBOW - Creating Vocab and Binary Word Arrays
CBOW - Building Features and Target Variable
CBOW - Accuracy of Random Model and Training Process
CBOW - Training the Neural Network
CBOW - Obtaining Word Vectors (Embeddings)
Pre-Processing Wikipedia Data for CBOW Model
Building Features and Target for Wikipedia Data
Fitting Neural Network on Wikipedia Data
Performance of the Neural Network
Predicting a Word Given a Context
Retrieving Word Embeddings and Word Similarities
Word2Vec
Word2Vec - Operations with Vectors
Word2Vec - Word Clustering
Continuous Bag of Words Implementation and Word2Vec - Quiz
Continuous Bag of Words Implementation - Exercises

Text Representation :
[Slides] - Text Representation
Binary Vectorizer
Count Vectorizer
TF-IDF
Text Representation - Quiz
Text Representation - Exercises

Text Classification :
[Slides] - Text Classification and Logistic Regression Intuition
[Slides] - Text Classification Training and Testing Pipeline
Intro to Text Classification
Loading Positive and Negative Movie Reviews
Pre-Processing Text for Text Classification
Log Ratio Intuition and Word Influence
Stemming and Vectorizing the Reviews
Logistic Regression Intuition and Training Process
Sigmoid Function and One Feature Prediction
Gradient Descent Intuition by Adjusting Weights
Train and Test Split
Fitting and Evaluating Model
Model Regularization
Obtaining the Weights/Coefficients of Regression
Predicting New Sentences Sentiment
Text Classification - Quiz

Course Ending :
Course Feedback
Thank you!

مشخصات این مجموعه :
زبان آموزش ها انگلیسی روان و ساده
دارای آموزشهای ویدیویی و دسته بندی شده
ارائه شده بر روی 2 حلقه دیسک
مدت زمان آموزش 10 ساعت و 16 دقیقه !
محصول موسسه آموزشی Udemy