Course Information
Course Overview
Learn NLP concepts with practical implementation using Python, TensorFlow, PyTorch. Learn about LLMs
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 demand 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 and PyTorch. 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 Representation
Named Entity Recognition with spaCy in python
Custom Named Entity Recognition using spaCy v3 library
Word2Vec model and custom word2vec model in python
Exploratory data analysis on text dataset using python
Text Clustering
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 using PyTorch library
Text Classification using BERT Transformers
Text Classification using spaCy v3 library
Zero shot text classification using HuggingFace
LDA topic modelling
Top2Vec Topic Modelling
BERTopic Topic Modelling
Extractive Text Summarization using gensim and python
Abstractive Text Summarization using Google PEGASUS
Extractive Question Answering with HuggingFace
Aspect Based Sentiment Analysis
HayStack Question Answering Demo
ChatGPT
ChatGPT use cases
How to fine tune LLMs
RAG
RAG based Chatbots
For advanced NLP content check out my Youtube Channel
Content will be constantly updated
Course Content
- 9 section(s)
- 39 lecture(s)
- Section 1 Introduction to Natural Language Processing and applications in various domains
- Section 2 Basics of Natural Language Processing
- Section 3 Named Entity Recognition
- Section 4 Text Clustering and Classification
- Section 5 Topic Modelling Techniques
- Section 6 Text Summarization, Extractive Question Answering
- Section 7 Large Language Models
- Section 8 Fine tuning LLMs
- Section 9 Agents
What You’ll Learn
- Concepts of Natural Language Processing and its Applications across various domains
- Pratical implementation of Natural Language Processing Technqiues using Python, TensorFlow, PyTorch, Transformers, spaCy and gensim libraries
- Understand how to approach and solve NLP problems
- Understand how to use advanced NLP models like BERT
- Understand LLM's and how they can be used for various use cases
Skills covered in this course
Reviews
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GGopalakrishnan Kothandam
Hidden Gem. Great course and content. would love to learn more project based videos in the future.
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PPaola Ghione
Spectacular and very well explained!
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LLuciano Ferrara
Course content is even fine but the person does not explain the fundamentals of each topic and even when he goes through the code, he does not explain all the code. I won't advice this course to novices. Indeed, I didn't even complete it and bought another one (much much better). Make sure you spend more time on each topic with easier example, otherwise the course is useless or not understandable!
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MMuhamad Arif bin Hashim
Very good intro