Natural Language Processing With Cutting Edge Models

About Natural Language Processing With Cutting Edge Models

Hi everyone,

This is a massive 3-in-1 course covering the following:

1. Text Preprocessing and Text Vectorization

2. Machine Learning and Statistical Methods

3. Deep Learning for NLP and Generative AI for text.

This course covers all the aspects of performing different Natural Language processing using Machine Learning Models, Statistical Models and State of the art Deep Learning Models such as LSTM and Transformers.

This course will set the foundation for learning the most recent and groundbreaking topics in AI related Natural processing tasks such as Large Language Models, Diffusion models etc.

This course includes the practical oriented explanations for all Natural Language Processing tasks with implementation in Python

Sections of the Course

· Introduction of the Course

· Introduction to Google Colab

· Introduction to Natural Language Processing

· Text Preprocessing

· Text Vectorization

· Text Classification with Machine Learning Models

· Sentiment Analysis

· Spam Detection

· Dirichlet Distribution

· Topic Modeling

· Neural Networks

· Neural Networks for Text Classification

· Word Embeddings

· Neural Word Embeddings

· Generative AI for NLP

· Markov Model for Text Generation

· Recurrent Neural Networks ( RNN )

· Sequence to sequence (Seq2Seq) Networks

. Seq2Seq Networks for Text Generation

. Seq2Seq Networks for Language Translation

· Transformers

· Bidirectional LSTM

· Python Refresher

Who this course is for:

· Students enrolled in Natural Language processing course.

· Beginners who want to learn Natural Language Processing from fundamentals to advanced level

· Researchers in Artificial Intelligence and Natural Language Processing.

· Students and Researchers who want to develop Python Programming skills while solving different NLP tasks.

· Want to switch from Matlab and Other Programming Languages to Python.