Deep Learning for NLP E-Learning
Enroll in the unique Deep Learning for NLP E-Learning Training and enjoy 1 year of 24/7 access to rich interactive videos, expert voice-guided instruction, progress tracking via reports, and chapter-based assessments to test your knowledge instantly.
This course explores how deep learning techniques are revolutionizing Natural Language Processing (NLP). You'll dive into state-of-the-art models like word embeddings, RNNs, transformers, and BERT, and apply them to real-world tasks such as sentiment analysis, text generation, and translation.
Why Choose This Training?
- 1 year of anytime access to a premium e-learning platform
- Learn deep learning's impact on NLP through hands-on and theoretical content
- Test your knowledge with quizzes and track your progress via reports
- Ideal for careers in AI, machine learning, and data science
- Includes a certificate of participation
Who Should Attend?
- AI and data science professionals expanding into NLP
- Software developers and machine learning engineers
- Students in computer science, computational linguistics, or AI
- Professionals applying NLP in business or research
- Anyone with basic Python and ML knowledge
Course content
Deep Learning for NLP: Introduction
Course: 1 Hour, 18 Minutes
- Course Overview
- NLP with Deep Learning
- NLP Use Cases in Deep Learning
- Basic Deep Learning Frameworks
- Intermediate Deep Learning Frameworks
- Advanced Deep Learning Frameworks
- Introduction to Sentiment Data
- Using Deep Learning Pipelines for Sentiment Data
- Sentiment Analysis - Overview & Data
- Sentiment Analysis - EDA
- Sentiment Analysis - Pre-processing
- Sentiment Analysis - Modeling & Evaluation
- Sentiment Analysis - Creating Accuracy & Loss Graphs
- Course Summary
Deep Learning for NLP: Neural Network Architectures
Course: 2 Hours, 30 Minutes
- Course Overview
- Basic Architecture of a Neural Network
- Multilayer Perceptron (MLP)
- Recurrent Neural Network (RNN) Architecture
- Challenges in RNN
- Applications of Neural Network-based Architecture
- Introducing the Product Reviews Data
- Loading Product Reviews Data into Google Colaboratory
- Understanding Product Reviews Data
- Exploring Product Reviews Data
- Pre-processing Product Reviews Data
- Applying Feature Engineering - Word Representation
- Creating Vector Representations Using Word2vec
- Averaging Feature Vectors
- Creating Word Embeddings with Word2Vec
- Constructing a RNN Model with Word2vec Embeddings
- Using GloVe Vectors
- Product Reviews Classification Using RNN
- Course Summary
Deep Learning for NLP: Memory-based Networks
Course: 1 Hour, 27 Minutes
- Course Overview
- Introduction to Memory-based Networks
- Gated Recurrent Unit (GRU) Architecture
- Long Short-term Memory (LSTM) Architecture
- Fall of RNN versus Rise of LSTM
- Variants of LSTM networks
- Product Review Data Preparation for Modeling
- Product Review Data Classification Using GRU
- Product Review Data Classification Using LSTM
- Product Review Data Classification Using Bi-LSTM
- Result Comparison between RNN, GRU, and LSTM
- Course Summary
Deep Learning for NLP: Transfer Learning
Course: 2 Hours, 10 Minutes
- Course Overview
- Introduction to Transfer Learning
- Advantages and Challenges of Transfer Learning
- Role of Language Modeling in Transfer Learning
- Introduction to Basic Transfer Learning Models
- Intermediate Transfer Learning Models
- Advance Transfer Learning Models
- Building ELMo Embedding Layer for Reviews
- Creating ELMo an Model for Product Reviews
- Classifying Product Reviews Using ELMo
- Reshaping Data for the ELMo Embedding Layer
- Building a Language Model Using ULMFiT
- Implementing the Language Model Using ULMFiT
- Classifying Product Reviews Using ULMFIT & FastText
- Performing Result Comparison
- Course Summary
Deep Learning for NLP: GitHub Bug Prediction Analysis
Course: 1 Hour, 56 Minutes
