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Natural Language Processing (NLP) in Python with 8 Projects

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Natural Language Processing (NLP) in Python with 8 Projects, available at $109.99, has an average rating of 4.67, with 94 lectures, 3 quizzes, based on 697 reviews, and has 7532 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who is interested to learn Natural Language Processing It is particularly useful for Anyone who is interested to learn Natural Language Processing.

Enroll now: Natural Language Processing (NLP) in Python with 8 Projects

Summary

Title: Natural Language Processing (NLP) in Python with 8 Projects

Price: $109.99

Average Rating: 4.67

Number of Lectures: 94

Number of Quizzes: 3

Number of Published Lectures: 94

Number of Published Quizzes: 3

Number of Curriculum Items: 97

Number of Published Curriculum Objects: 97

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

  • Anyone who is interested to learn Natural Language Processing

Target Audiences

  • Anyone who is interested to learn Natural Language Processing

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”

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Update 1.0 :

Fasttext Library for Text classification section added.

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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.

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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

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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-bot

  • Google Now, Apple Siri, Amazon Alexa

  • Machine Translation

  • Sentiment analysis

  • SpeechRecognition and many more.

So, welcome to my course on NLP.

Natural Language Processing (NLP) in Python with 8 Projects

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This course has 10+ Hours of HDQuality 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 & SetupIn this section we will get our online environment Google Colab setup.

3 : Basics of Natural Language ProcessingIn 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 Classification

In 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 AnalysisIn 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 articles

Another one we will work is finding the sentiment from the recently posted tweet about some specific keyword with the help of Twitter API – tweepy library

9 : 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 EmbeddingIn 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 & RNNIn 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 LSTMIn 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.

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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 Course

  • Udemy Certificate of Completion available for download

  • Friendly support in the Q&A section

So 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.

Regards

Ankit & Vijay

Course Curriculum

Chapter 1: Welcome

Lecture 1: Course Overview

Lecture 2: Reviews UPDATE

Lecture 3: Introduction to NLP

Lecture 4: Course FAQ's

Chapter 2: Installation & Setup

Lecture 1: Course Installation

Lecture 2: Local Installation Steps

Lecture 3: Links to Notebooks (As taught in Lectures)

Lecture 4: Links to Notebooks (More explanatory notebook for refrence)

Chapter 3: Basics of Natural Language Processing

Lecture 1: Section : Introduction

Lecture 2: Tokenization Basic Part – 1

Lecture 3: Tokenization Basic Part – 2

Lecture 4: Tokenization Basic Part – 3

Lecture 5: Stemming & Lemmatization – 1

Lecture 6: Stemming & Lemmatization – 2

Lecture 7: Stop Words

Lecture 8: Vocabulary and Matching Part – 1

Lecture 9: Vocabulary and Matching Part – 2 (Rule Based)

Lecture 10: Vocabulary and Matching Part – 3 (Phrase Based)

Lecture 11: Parts of Speech Tagging

Lecture 12: Named Entity Recognition

Lecture 13: Sentence Segmentation

Chapter 4: Project 1 : Spam Message Classification

Lecture 1: Business Problem & Dataset

Lecture 2: Data Exploration & Preprocessing

Lecture 3: Split Data in Training & Testing

Lecture 4: Apply Random Forest

Lecture 5: Apply Support vector Machine (SVM)

Lecture 6: Predict Testing Data both model

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

Lecture 1: Business Problem

Lecture 2: Cleaning Text Data with NLTK – 1

Lecture 3: Cleaning Text Data with NLTK – 2

Lecture 4: Bag of Word Model

Lecture 5: Apply Naive Bayes Algorithm

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

Lecture 1: Review Classification Part -1

Lecture 2: Review Classification Part – 2

Chapter 7: Project 4 : Automated Text Summarization

Lecture 1: Importing the libraries and Dataset

Lecture 2: Create Word Frequency Counter

Lecture 3: Calculate Sentence Score

Lecture 4: Extract summary of document

Chapter 8: Project 5 : Twitter sentiment Analysis

Lecture 1: Setting up Twitter Developer application

Lecture 2: Fetch Tweet from Tweeter server

Lecture 3: Find Setiment from Tweets

Chapter 9: Deep Learning Basics

Lecture 1: The Neuron

Lecture 2: Activation Function

Lecture 3: Cost Function

Lecture 4: Gradient Descent and Back-Propagation

Chapter 10: Word Embeddings

Lecture 1: Introduction to Word Embedding

Lecture 2: Train Model for Embedding – I

Lecture 3: Train Model for Embedding – II

Lecture 4: Embeddings with Pretrained model

Chapter 11: Project 6 : Text Classification with CNN

Lecture 1: Convolutional Neural Network Part 1

Lecture 2: Convolutional Neural Network Part 2

Lecture 3: Spam Detection with CNN – I

Lecture 4: Spam Detection with CNN – II

Chapter 12: Project 7 : Text Classification with RNN

Lecture 1: Introduction to Recurrent Neural Networks

Lecture 2: Vanishing Gradient Problem

Lecture 3: LSTM and GRU

Lecture 4: Spam Detection with RNN

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

Lecture 1: Text Generation Part I

Lecture 2: Text Generation Part II

Chapter 14: FastText Library for Text Classification

Lecture 1: fasttext Installation steps [Video]

Lecture 2: fasttext Installation steps [Text]

Lecture 3: Virtual Box Installation

Lecture 4: Create Linux Virtual Machine

Lecture 5: Install fasttext library

Lecture 6: Text Classification with Fasttext

Chapter 15: Data analysis with Numpy

Lecture 1: Introduction to NumPy

Lecture 2: Numpy Arrays Part 1

Lecture 3: Numpy Arrays Part 2

Lecture 4: Numpy Arrays Part 3

Lecture 5: Numpy Indexing and Selection Part 1

Lecture 6: Numpy Indexing and Selection Part 2

Lecture 7: Numpy Operations

Chapter 16: Data analysis with Pandas

Lecture 1: Pandas Introduction

Lecture 2: Pandas Series

Lecture 3: DataFrames Part 1

Lecture 4: DataFrames Part 2

Lecture 5: DataFrames Part 3

Lecture 6: Missing Data

Lecture 7: Groupby Method

Lecture 8: Merging, Joining and Concatenating DataFrames

Lecture 9: Pandas Operations

Instructors

Rating Distribution

  • 1 stars: 16 votes
  • 2 stars: 17 votes
  • 3 stars: 95 votes
  • 4 stars: 234 votes
  • 5 stars: 335 votes

Frequently Asked Questions


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