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What you'll learn?

  • In this course you will be learning about Natural Language Processing (NLP) from an experienced professional. 
  • Giving machines the capacity to find meaning in unstructured data pulled from natural language holds notable promise. By 2025, the global NLP market is expected to reach over $34 billion, growing at a CAGR of 21.5%  and there would be high deamand for NLP skills.
  • In this course I cover length and breadth of topics in NLP. I explain NLP concepts in a simple way along with practical implementation in Python using libraries like NLTK, spaCy, TensorFlow.
  • I also discuss various topics like text pre-processing, text classification, text summarization, topic modelling and word embeddings.
  • I also cover NLP applications in various domains like healthcare, finance. I am sure this course should help you in getting started and also become proficient in NLP In this video course you will learn the following about Natural Language Processing: Introduction to NLP Its Applications in domains like finance and healthcare Stemming and Lemmatimzation with NLTK and spaCy TF-IDF, Bag of Words Represntation Named Entity Recognition with spaCy in python Word2Vec model and custom word2vec model in python Exploratory data analysis on text dataset using python LDA topic modelling Text Classification with Neural network using Tensorflow in Python Text Classification with Convolutional Neural Network( CNN) using Tensorflow in Python Text Classification with Long Short Term memory( LSTM) networks using Tensorflow in Python Extractive Text Summarization using gensim and python Abstractive Text Summarization using Google PEGASUS

Course Overview

In this course you will be learning about Natural Language Processing (NLP) from an experienced professional.  Giving machines the capacity to find meaning in unstructured data pulled from natural language holds notable promise. By 2025, the global NLP market is expected to reach over $34 billion, growing at a CAGR of 21.5%  and there would be high deamand for NLP skills.

In this course I cover length and breadth of topics in NLP. I explain NLP concepts in a simple way along with practical implementation in Python using libraries like NLTK, spaCy, TensorFlow. I also discuss various topics like text pre-processing, text classification, text summarization, topic modelling and word embeddings. I also cover NLP applications in various domains like healthcare, finance. I am sure this course should help you in getting started and also become proficient in NLP

In this video course you will learn the following about Natural Language Processing:

  • Introduction to NLP
  • Its Applications in domains like finance and healthcare
  • Stemming and Lemmatimzation with NLTK and spaCy
  • TF-IDF, Bag of Words Represntation
  • Named Entity Recognition with spaCy in python
  • Word2Vec model and custom word2vec model in python
  • Exploratory data analysis on text dataset using python
  • LDA , Top2Vec, BERTopic topic modelling
  • Text Classification with Neural network using Tensorflow in Python
  • Text Classification with Convolutional Neural Network( CNN) using Tensorflow in Python
  • Text Classification with Long Short Term memory( LSTM) networks using Tensorflow in Python
  • Text Classification with BERT using Tensorflow in Python
  • Text Classification with spaCy v3 library
  • Extractive Text Summarization using gensim and python
  • Abstractive Text Summarization using Google PEGASUS

 

For advanced NLP content check out my Youtube Channel

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Pre-requisites

  • Basic knowledge of python
  • Basic Knowledge of Machine Learning

Target Audience

  • Beginners who want to learn about Natural Language Processing in a practical way from an experienced professional
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Curriculum 21 Lectures 00:12:46

  • Section 1 : Introduction to Natural Language Processing and applications in various domains 3 Lectures 00:12:45

    • Lecture 1 :
    • Introduction to NLP Preview
      I give an introduction to Natural Language Processing
    • Lecture 2 :
    • Natural Language Processing In Finance
      I explain applications of NLP in Finance Domain
    • Lecture 3 :
    • Natural Language Processing in Healthcare
      I explain the applications of NLP in Healthcare domain

  • Section 2 : Basics of NLP 3 Lectures 00:00:01

    • Lecture 1 :
    • Text Preprocessing
      In this video I will be explain about Stemming and Lemmatization in the context of Natural Language Processing. I will also explain how you can do stemming and lemmatization using NLTK and spaCy packages in Python.
    • Lecture 2 :
    • Tf-idf, Bag of words
      In this video i will be explaining concepts of Bag of words, Term frequency- Inverse Document Frequency, Cosine similarity in the context of Natural Language Processing. I will also explain how to implement these concepts in python.I have also shared the code on Github
    • Lecture 3 :
    • Text Visualization
      In this video i will be explaining about exploratory data analysis and visualization on text data.I will be using the Amazon fine food reviews dataset from Kaggle(https://www.kaggle.com/snap/amazon-fine-food-reviews). I will be using libraries like seaborn, wordcloud to explore and visualize the text dataset

  • Section 3 : Text Classification, Named Entity Recognition 9 Lectures 00:00:00

    • Lecture 1 :
    • Text Classification with Neural Network
      In this video I will demonstrate how we can implement text classification using Neural Network with TensorFlow 2.1 in python. I am specifically training a simple neural network based text classification model to classify sentiment of tweets . I will be using the tensorflow gpu version. The dataset is from the Tweet Sentiment Extraction challenge from Kaggle(https://www.kaggle.com/c/tweet-sentiment-extraction/overview) We would implement text classification using a simple neural network developed using Tensorflow on tweet data to classify tweets as "positive", "negative" or "neutral"
    • Lecture 2 :
    • Text Classification with CNN
      In this video I will demonstrate how we can implement text classification using Convolutional Neural Network with TensorFlow 2.1 in python. I am specifically training a simple convolutional neural network based text classification model to classify sentiment of tweets . I will be using the tensorflow gpu version The dataset is from the Tweet Sentiment Extraction challenge from Kaggle(https://www.kaggle.com/c/tweet-sentiment-extraction/overview) We would implement text classification using a simple neural network developed using Tensorflow on tweet data to classify tweets as "positive", "negative" or "neutral" The code is present here: https://github.com/rsreetech/TextClassificationTensorFlowCNN
    • Lecture 3 :
    • Text Classification with LSTM
      In this video I will demonstrate how we can implement text classification using LSTM using TensorFlow 2.1. I am specifically training a simple LSTM based text classification model to classify sentiment of Amazon Reviews . The dataset is from Amazon Review Data (2018) https://nijianmo.github.io/amazon/index.html. I here look at Cell Phones and Accessories review dataset from "Small" subsets for experimentation. I have pre-processed this dataset. The script for pre-processing is present here: https://github.com/rsreetech/TextClassificationLSTMTensorFlow/blob/master/AmazonReviewsPreprocessing.ipynb Let us perform sentiment classification by considering reviews which have rating 3(3,4,5) and above as having positive sentiment and reviews having ratings below 3(1,2) as negative sentiment. Let us develop a LSTM based classification model for sentiment classification on review data. The code is present here: https://github.com/rsreetech/TextClassificationLSTMTensorFlow/
    • Lecture 4 :
    • Word2Vec Detailed Explanation and Train your Custom Word2Vec Model using Genism
      In this video I will be explaining about Word2Vec and how to train your custom Word2Vec Model using genism in Python.
    • Lecture 5 :
    • ZeroShotTextClassification
      In this video I explain about Zero Shot Text Classification and Textual Entailment. Traditionally, zero-shot learning (ZSL) most often referred to a fairly specific type of task: learn a classifier on one set of labels and then evaluate on a different set of labels that the classifier has never seen before. Recently, especially in NLP, it's been used much more broadly to mean get a model to do something that it wasn't explicitly trained to do. A well-known example of this is in the GPT-2 paper where the authors evaluate a language model on downstream tasks like machine translation without fine-tuning on these tasks directly. I explain about how textual entailment is used in the context of zero shot learning. I also explain about Hugging face zero shot text classification pipeline with a demo
    • Lecture 6 :
    • TextClassificationBERT
      In this video I will be explaining my solution to Kaggle Natural Language Processing with Disaster Tweets (Getting Started Competition). I will be using BERT(Tensorflow) in the solution The main purpose of the competition is Twitter has become an important communication channel in times of emergency. The ubiquitousness of smartphones enables people to announce an emergency they’re observing in real-time. Because of this, more agencies are interested in programmatically monitoring Twitter (i.e. disaster relief organizations and news agencies).
    • Lecture 7 :
    • Spacyv3TextClassification
      Text Classification using spaCy v3.0 transformers in Python | Natural Language Processing Tutorial | #NLProc In this video I will be explaining how we can perfom text classification with spaCy v3.0 transformers library in Python. spaCy v3.0 features all new transformer-based pipelines that bring spaCy’s accuracy right up to the current state-of-the-art. The dataset is from the Tweet Sentiment Extraction challenge from Kaggle(https://www.kaggle.com/c/tweet-sentiment-extraction/overview) We would perform text classification using spaCy on tweet data to classify tweets as "positive", "negative" or "neutral" The code is present here: https://github.com/rsreetech/TextClassificationWithSpacyV3.0
    • Lecture 8 :
    • SpacyV3CustomNamedEntityRecognition
      In this video I will be explaining how we perform Custom Named Entity (Disease) Recognition in clinical text with spaCy v3 transformers in Python|. I am using the latest version of spaCy v3.0 here. There are differences on the training method when compared to spaCy v2.0 Here I will be creating a clinical named entity recognition transformers based model which can recognize the disease names from clinical text For this I have extracted annotated clinical text from the following github repo: https://github.com/dmis-lab/biobert They provide annotated clinical text here: Named Entity Recognition: (17.3 MB), 8 datasets on biomedical named entity recognition(https://drive.google.com/open?id=1OletxmPYNkz2ltOr9pyT0b0iBtUWxslh) This custom name entity(disease) recognition model achieves around 90% f1-score on the dataset with 10 epochs of training. The code is present here: https://github.com/rsreetech/CustomeNERspaCyv3.0
    • Lecture 9 :
    • Named Entity Recognition
      In this video I will be explaining what is Named Entity Recognition(NER) in the context of Natural Language Processing. We also look into how we can use spaCy library in python to perform named entity recognition.

  • Section 4 : Topic Modelling 3 Lectures 00:00:00

    • Lecture 1 :
    • LDA Topic Modelling
      In this video I will be explaining about LDA Topic Modelling Explained and how to train build LDA topic model using genism in Python.
    • Lecture 2 :
    • Top2Vec
      In this video I demonstrate using a Kaggle notebook how topic modelling and semantic search can be done using Top2Vec python library. Top2Vec is an algorithm for topic modeling and semantic search. It automatically detects topics present in text and generates jointly embedded topic, document and word vectors. Once you train the Top2Vec model you can: • Get number of detected topics. • Get topics. • Get topic sizes. • Get hierarchichal topics. • Search topics by keywords. • Search documents by topic. • Search documents by keywords. • Find similar words. • Find similar documents. • Expose model with RESTful-Top2Vec
    • Lecture 3 :
    • BERTopic
      In this video I discuss about BERTopic. BERTopic is a topic modelling technique that leverages huggingface transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions. I explain the functionality of BERTopic with a python demo on arxiv dataset

  • Section 5 : Advanced Topics 3 Lectures 00:00:00

    • Lecture 1 :
    • Text Summarization
      In this video I will explain about text summarization. I will be explaining about Text Rank Extractive Summarization Algorithm and I will also demonstrate how you can perform text summarization using spaCy (pytextrank) and genism in Python
    • Lecture 2 :
    • Abstractive Text Summarization
      In this video I will explain about abstractive text summarization. I will be explaining about Google PEGASUS deep learning model and also demonstrate how you can perform abstractive text summarization using Hugging Face Transformers library in Python. This video will have 3 sections . The first section deals with abstractive text summarization concepts, few pre-requisites like transformers, masked language model and rougeF1 metrics. The second section explains the PEGASUS sequence to sequence model for abstractive text summarization and the third section explains how you can use Pegasus on your text for abstractive text summarization using Hugging Face Transformers library in python
    • Lecture 3 :
    • HayStack
      In this video I demonstrate how Haystack can be used to build search systems. I use Haystack on MIMIC clinical data to build a simple search and question answering engine

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Instructor

Rithesh Sreenivasan,

896 Course Views

1 Courses

Experienced and pragmatic researcher with around 16 years of proven experience in developing software solutions based on machine learning, natural language processing and deep learning techniques to solve business problems across various domains like healthcare, finance , real estate and customer service . I have multiple filed patents. I have exposure to wide variety of programming languages, machine learning packages and agile based software development methodologies. I have mentored many junior researchers/engineers throughout my corporate career across various MNCs Specialties: -Natural language processing -Applied machine learning -Deep learning for natural language processing -Deep Learning for Computer Vision -Python -Machine learning libraries like Tensorflow, PyTorch
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