Natural Language Processing

Date:

500/-

Welcome to our NLP Workshop!
In today's digital age, where vast amounts of text data are generated every day, the ability to extract insights and understand the nuances of human language is more crucial than ever. Our NLP course is designed to equip you with the knowledge and skills needed to analyze, interpret, and manipulate natural language data effectively

Syllabus

In our workshop, you'll embark on a journey through the fundamentals of NLP, exploring key concepts such as

Natural Language Processing
  • Basics

    • Python

  • Machine Learning
    • Supervised learning

      • Linear Regression

      • Naive Bayes

      • Support Vector Machines (SVM)

      • Logistic Regression

      • Random Forest

      • Gradient Boosting Machines (GBM)

      • Neural Networks

    • Unsupervised learning

      • K-means Clustering

      • Hierarchical Clustering

      • DBSCAN

      • Principal Component Analysis (PCA)

  • Fundamentals of NLP

    • Intro to NLP

    • Text preprocessing

      • Tokenization

      • Stop word removal

      • Stemming and lemmatization

      • Part-of-speech tagging

  • Text Representation

    • Bag-of-words model

    • TF-IDF (Term Frequency-Inverse Document Frequency)

    • Word embeddings (e.g., Word2Vec, GloVe)

    • Document embeddings (e.g., Doc2Vec)

  • NLP with Machine Learning
    • Named Entity Recognition (NER)

      • Introduction to NER

      • Approaches and techniques for NER

    • Sentiment Analysis

      • Introduction to sentiment analysis

      • Machine learning approaches for sentiment analysis

    • Text Classification

      • Introduction to text classification

      • Naive Bayes classifier for text classification

      • Support Vector Machines (SVM) for text classification

    • Sequence Labeling

      • Introduction to sequence labeling tasks (e.g., POS tagging, chunking)

      • Hidden Markov Models (HMM)

      • Conditional Random Fields (CRF)

  • NLP with Deep Learning
    • Neural Networks for NLP

      • ANN

      • CNN

      • RNN

      • LSTM

      • Transformers and Self attention

    • Projects on Deep Learning

    • Introduction to Speech Processing

    • Introduction to LLMs