- Course Overview
- Reviews UPDATE
- Introduction to NLP
- Course FAQs
Online
₹ 449 3,499
Quick facts
particular | details | |
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
₹ 449 ₹3,499
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Welcome
Installation & Setup
- Course Installation
- Local Installation Steps
- Links to Notebooks (As taught in Lectures)
- Links to Notebooks (More explanatory notebook for refrence)
Basics of Natural Language Processing
- Section : Introduction
- Tokenization Basic Part - 1
- Tokenization Basic Part - 2
- Tokenization Basic Part - 3
- Stemming & Lemmatization - 1
- Stemming & Lemmatization - 2
- Stop Words
- Vocabulary and Matching Part - 1
- Vocabulary and Matching Part - 2 (Rule Based)
- Vocabulary and Matching Part - 3 (Phrase Based)
- Parts of Speech Tagging
- Named Entity Recognition
- Sentence Segmentation
- NLP Basics
Project 1 : Spam Message Classification
- Business Problem & Dataset
- Data Exploration & Preprocessing
- Split Data in Training & Testing
- Apply Random Forest
- Apply Support vector Machine (SVM)
- Predict Testing Data both model
- Quiz
Project 2 : Restaurant Review Prediction (Good or bad)
- Business Problem
- Cleaning Text Data with NLTK - 1
- Cleaning Text Data with NLTK - 2
- Bag of Word Model
- Apply Naive Bayes Algorithm
Project 3 : IMDB, Amazon and Yelp review Classification
- Review Classification Part -1
- Review Classification Part - 2
Project 4 : Automated Text Summarization
- Importing the libraries and Dataset
- Create Word Frequency Counter
- Calculate Sentence Score
- Extract summary of document
Project 5 : Twitter sentiment Analysis
- Setting up Twitter Developer application
- Fetch Tweet from Tweeter server
- Find Setiment from Tweets
Deep Learning Basics
- The Neuron
- Activation Function
- Cost Function
- Gradient Descent and Back-Propagation
Word Embeddings
- Introduction to Word Embedding
- Train Model for Embedding - I
- Train Model for Embedding - II
- Embeddings with Pretrained model
- Word Embeddings
Project 6 : Text Classification with CNN
- Convolutional Neural Network Part 1
- Convolutional Neural Network Part 2
- Spam Detection with CNN - I
- Spam Detection with CNN - II
Project 7 : Text Classification with RNN
- Introduction to Recurrent Neural Networks
- Vanishing Gradient Problem
- LSTM and GRU
- Spam Detection with RNN
Project 8 : Automatic Text Generation using TensorFlow, Keras and LSTM
- Text Generation Part I
- Text Generation Part II
FastText Library for Text Classification
- fasttext Installation steps [Video]
- fasttext Installation steps [Text]
- Virtual Box Installation
- Create Linux Virtual Machine
- Install fasttext library
- Text Classification with Fasttext
Data analysis with Numpy
- Introduction to NumPy
- Numpy Arrays Part 1
- Numpy Arrays Part 2
- Numpy Arrays Part 3
- Numpy Indexing and Selection Part 1
- Numpy Indexing and Selection Part 2
- Numpy Operations
Data analysis with Pandas
- Pandas Introduction
- Pandas Series
- DataFrames Part 1
- DataFrames Part 2
- DataFrames Part 3
- Missing Data
- Groupby Method
- Merging, Joining and Concatenating DataFrames
- Pandas Operations
- Reading and Writing Files in Pandas
Data Visualization with Matplotlib
- Matplotlib Part 1 - Functional Method
- Matplotlib Part 1 - Object Oriented Method
- Matplotlib Part 2 - Subplots Method
- Matplotlib Part 2 - Figure size, Aspect ratio and DPI
- Matplotlib Part 3
- Matplotlib Part 4
Appendix
- Text File Processing - I
- Text File Processing - II
- Text File Processing - III
- Text File Processing - IV
- Working with PDF File - I