Overview

Deep Learning and Natural Language Processing

Deep Learning and Natural Language Processing (NLP) have revolutionized the way we interact with and analyze text data. This four-day course, tailored for beginners, provides a comprehensive introduction to the foundations of deep learning and its application in NLP. Participants will gain a strong understanding of neural networks, language processing, and hands-on experience in building NLP models.

Skills Covered

By the end of this course, participants will:

  • Understand the fundamentals of deep learning and its relevance in NLP.
  • Be proficient in building and training neural networks.
  • Gain practical experience in preprocessing and analyzing text data.
  • Learn to develop NLP applications, including sentiment analysis, text classification, and language generation.
  • Develop the skills to apply deep learning and NLP to real-world text data.
  • Feel confident to continue exploring more advanced deep learning and NLP techniques.

Prerequisites

There are no prerequisites required to attend this course.

Target Audience

  • Beginner level courses for aspiring Data Scientist.
  • Anyone who is interested in data science.

Course Curriculum

Module 1: Introduction to Deep Learning

  • What is deep learning and its significance in machine learning.
  • Introduction to neural networks.
  • Data preprocessing and text tokenization.
  • Building a neural network using Python and TensorFlow/Keras.
  • Model training, optimization, and loss functions.
  • Practical exercises and projects with basic neural networks.

Module 2: Natural Language Processing (NLP) Fundamentals

  • Introduction to NLP and its applications.
  • Text data preprocessing: Tokenization, stemming, and lemmatization.
  • Text vectorization and feature extraction.
  • Sentiment analysis using NLP.
  • Text classification and document classification.
  • Hands-on exercises with NLP techniques.

Module 3: Advanced NLP Techniques and Applications

  • Word embeddings and Word2Vec.
  • Named Entity Recognition (NER) and Part-of-Speech (POS) tagging.
  • Text generation and language modeling.
  • Building a chatbot with deep learning and NLP.
  • Practical projects and applications in NLP.
  • Model evaluation for NLP tasks

Module 4: Real-World NLP Projects and Course Conclusion

  • Real-world NLP projects and case studies.
  • Challenges and best practices in NLP.
  • Final project assignment and guidelines.
  • Final project presentations.
  • Course wrap-up, Q&A, and next steps in deep learning and NLP journey.
  • Certificate distribution

Dates & Locations

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Exam & Certification

This course is not associated with any Certification.

Training & Certification Guide

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