Natural Language Processing (NLP) in Python with 8 Projects

Work on 8 Projects, Learn Natural Language Processing Python, Machine Learning, Deep Learning, SpaCy, NLTK, Sklearn, CNN

What you'll learn
The Complete understanding of Natural Language Processing
Implement NLP related task with Scikit-learn, NLTK and SpaCy
Apply Machine Learning Model to Classify Text Data
Text Classification (Spam Detection, Amazon product Review Classification)
Text Summarization (Turn 5000 word article into 200 Words)
Calculate Sentiment Score from Recently Posted Tweet (Tweeter API)
Refresh your Deep Learning Concepts (ANN, CNN & RNN)
Build your own Word Embedding (Word2vec) Model with Keras
Word Embeddings application with Google Pretrained Model
Spam Message Detection with Neural Network Based CNN and RNN Model
Automatic Text Generation using TensorFlow, Keras and LSTM
Working with Text Files & PDF in Python (PyPDF2 module)
Tokenization, Stemming and Lemmatization
Stop Words, Parts of Speech (POS) Tagging with NLTK
Vocabulary, Matching, Named Entity Recognition (NER)
Data Analysis with Numpy and Pandas
Data Visualization with Matplotlib library

Requirements
Basic understanding of Python Programming

Description
Recent reviews: "Thorough explanation, going great so far. A very simplistic and straightforward introduction to Natural Language Processing. I will recommend this class to any one looking towards Data Science""This course so far is breaking down the content into smart bite-size pieces and the professor explains everything patiently and gives just enough background so that I do not feel lost.""This course is really good for me. it is easy to understand and it covers a wide range of NLP topics from the basics, machine learning to Deep Learning.The codes used is practical and useful.I definitely satisfy with the content and surely recommend to everyone who is interested in Natural Language Processing"------------------------------------------------------------------------------------------------------------------------------------------------------Update 1.0 :Fasttext Library for Text classification section added.------------------------------------------------------------------------------------------------------------------------------------------------------Hi Data Lovers,Do you have idea about Which Artificial Intelligence field is going to get big in upcoming year?According to statista dot com which field of AI is predicted to reach $43 billion by 2025?If answer is 'Natural Language Processing', You are at right place. -----------------------------------------------------------------------------------------------------------------------------------------------------Do you want to know How Google News classify millions of news article into hundreds of different category.How Android speech recognition recognize your voice with such high accuracy.How Google Translate actually translate hundreds of pairs of different languages into one another.If answer is "Yes", You are on right track.and to help yourself, me and my friend Vijay have created comprehensive course For Students and Professionals to learn Natural Language Processing from very Beginning-----------------------------------------------------------------------------------------------------------------------------------------------------NLP - "Natural Language Processing" has found space in every aspect of our daily life.Cell phone internet are the integral part of our life. Any most application you will find the use of NLP methods, from search engine of Google to recommendation system of Amazon & Netflix.Chat-botGoogle Now, Apple Siri, Amazon AlexaMachine TranslationSentiment analysisSpeech Recognition and many more.So, welcome to my course on NLP.Natural Language Processing (NLP) in Python with 8 Projects-----------------------------------------------------------------------------------------------------------------------------------------------------This course has 10+ Hours of HD Quality video, and following content.Course Outline : 1 : Welcome In this section we will get complete idea about what we are going to learn in the whole course and understanding related to natural language processing.2 : Installation & Setup In this section we will get our online environment Google Colab setup.3 : Basics of Natural Language Processing In this section we will dive into all basic NLP task like Tokenization, Lemmatization, stop word removal, name entity recognition, part of speech tagging, and see how to apply with different functions available in a Spacy and NLTK library.4, 5, 6 : Spam Message Classification, Restaurant Review Prediction (Good or bad), IMDB, Amazon and Yelp review ClassificationIn the next 3 section we will get dive into a real world data set for text classification, spam detection, restaurant review classification, Amazon IMDb reviews. We will see how to do Pre-Processing and make your data suitable for machine learning algorithm and apply different Machine Learning estimator (Logistic Regression, SVM, Decision Tree) for classifying text.7, 8 : Automated Text Summarization, Twitter sentiment Analysis In this 2 section we will work upon real world application of NLP.Automatic text summarisation, Which compress your text to find the summary of big articlesAnother one we will work is finding the sentiment from the recently posted tweet about some specific keyword with the help of Twitter API - tweepy library9 : Deep Learning Basics In This Section we will get a basic idea about Deep learning concept, like artificial neural network activation function and how ANN works.10 : Word Embedding In This Section, we will see How to implement word2vec on our custom datasets, as well as using Pretrained Google Model.11, 12 : Text Classification with CNN & RNN In this section we will see how to apply advanced deep learning model like convolution neural networks and recurrent neural networks for text classification.13 : Automatic Text Generation using TensorFlow, Keras and LSTM In this section we will apply neural network based LSTM model to automatically generate text.14, 15, 16, 17 : Numpy, Pandas, Matplotlib + File Processing In this section, for all of you who want refresh concept related to data analysis with Numpy and Pandas library, Data Visualization with Matplotlib library, and Text File processing and PDF File processing.-----------------------------------------------------------------------------------------------------------------------------------------------------So, This is the one of the most comprehensive course on natural language processing,And I am expecting you to know basic knowledge of python and your curiosity to learn Different techniques in NLP world.YOU'LL ALSO GET:Lifetime access to Natural Language Processing (NLP) with Python CourseUdemy Certificate of Completion available for downloadFriendly support in the Q&A sectionSo What Are You Waiting For ? Enroll today! and Empower Your Career !I can't wait for you to get started on mastering NLP with Python.Start analyzing your text data & I will see you inside a class.RegardsAnkit & Vijay

Overview
Section 1: Welcome

Lecture 1 Course Overview

Lecture 2 Reviews UPDATE

Lecture 3 Introduction to NLP

Lecture 4 Course FAQ's

Section 2: Installation & Setup

Lecture 5 Course Installation

Lecture 6 Local Installation Steps

Lecture 7 Links to Notebooks (As taught in Lectures)

Lecture 8 Links to Notebooks (More explanatory notebook for refrence)

Section 3: Basics of Natural Language Processing

Lecture 9 Section : Introduction

Lecture 10 Tokenization Basic Part - 1

Lecture 11 Tokenization Basic Part - 2

Lecture 12 Tokenization Basic Part - 3

Lecture 13 Stemming & Lemmatization - 1

Lecture 14 Stemming & Lemmatization - 2

Lecture 15 Stop Words

Lecture 16 Vocabulary and Matching Part - 1

Lecture 17 Vocabulary and Matching Part - 2 (Rule Based)

Lecture 18 Vocabulary and Matching Part - 3 (Phrase Based)

Lecture 19 Parts of Speech Tagging

Lecture 20 Named Entity Recognition

Lecture 21 Sentence Segmentation

Section 4: Project 1 : Spam Message Classification

Lecture 22 Business Problem & Dataset

Lecture 23 Data Exploration & Preprocessing

Lecture 24 Split Data in Training & Testing

Lecture 25 Apply Random Forest

Lecture 26 Apply Support vector Machine (SVM)

Lecture 27 Predict Testing Data both model

Section 5: Project 2 : Restaurant Review Prediction (Good or bad)

Lecture 28 Business Problem

Lecture 29 Cleaning Text Data with NLTK - 1

Lecture 30 Cleaning Text Data with NLTK - 2

Lecture 31 Bag of Word Model

Lecture 32 Apply Naive Bayes Algorithm

Section 6: Project 3 : IMDB, Amazon and Yelp review Classification

Lecture 33 Review Classification Part -1

Lecture 34 Review Classification Part - 2

Section 7: Project 4 : Automated Text Summarization

Lecture 35 Importing the libraries and Dataset

Lecture 36 Create Word Frequency Counter

Lecture 37 Calculate Sentence Score

Lecture 38 Extract summary of document

Section 8: Project 5 : Twitter sentiment Analysis

Lecture 39 Setting up Twitter Developer application

Lecture 40 Fetch Tweet from Tweeter server

Lecture 41 Find Setiment from Tweets

Section 9: Deep Learning Basics

Lecture 42 The Neuron

Lecture 43 Activation Function

Lecture 44 Cost Function

Lecture 45 Gradient Descent and Back-Propagation

Section 10: Word Embeddings

Lecture 46 Introduction to Word Embedding

Lecture 47 Train Model for Embedding - I

Lecture 48 Train Model for Embedding - II

Lecture 49 Embeddings with Pretrained model

Section 11: Project 6 : Text Classification with CNN

Lecture 50 Convolutional Neural Network Part 1

Lecture 51 Convolutional Neural Network Part 2

Lecture 52 Spam Detection with CNN - I

Lecture 53 Spam Detection with CNN - II

Section 12: Project 7 : Text Classification with RNN

Lecture 54 Introduction to Recurrent Neural Networks

Lecture 55 Vanishing Gradient Problem

Lecture 56 LSTM and GRU

Lecture 57 Spam Detection with RNN

Section 13: Project 8 : Automatic Text Generation using TensorFlow, Keras and LSTM

Lecture 58 Text Generation Part I

Lecture 59 Text Generation Part II

Section 14: FastText Library for Text Classification

Lecture 60 fasttext Installation steps[Video]

Lecture 61 fasttext Installation steps[Text]

Lecture 62 Virtual Box Installation

Lecture 63 Create Linux Virtual Machine

Lecture 64 Install fasttext library

Lecture 65 Text Classification with Fasttext

Section 15: Data analysis with Numpy

Lecture 66 Introduction to NumPy

Lecture 67 Numpy Arrays Part 1

Lecture 68 Numpy Arrays Part 2

Lecture 69 Numpy Arrays Part 3

Lecture 70 Numpy Indexing and Selection Part 1

Lecture 71 Numpy Indexing and Selection Part 2

Lecture 72 Numpy Operations

Section 16: Data analysis with Pandas

Lecture 73 Pandas Introduction

Lecture 74 Pandas Series

Lecture 75 DataFrames Part 1

Lecture 76 DataFrames Part 2

Lecture 77 DataFrames Part 3

Lecture 78 Missing Data

Lecture 79 Groupby Method

Lecture 80 Merging, Joining and Concatenating DataFrames

Lecture 81 Pandas Operations

Lecture 82 Reading and Writing Files in Pandas

Section 17: Data Visualization with Matplotlib

Lecture 83 Matplotlib Part 1 - Functional Method

Lecture 84 Matplotlib Part 1 - Object Oriented Method

Lecture 85 Matplotlib Part 2 - Subplots Method

Lecture 86 Matplotlib Part 2 - Figure size, Aspect ratio and DPI

Lecture 87 Matplotlib Part 3

Lecture 88 Matplotlib Part 4

Section 18: Appendix

Lecture 89 Text File Processing - I

Lecture 90 Text File Processing - II

Lecture 91 Text File Processing - III

Lecture 92 Text File Processing - IV

Lecture 93 Working with PDF File - I

Anyone who is interested to learn Natural Language Processing

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