Advanced Machine Learning

Module 11: Natural Language Processing (NLP)

Lesson 1: Introduction to NLP

  • Basics of NLP
  • NLP Applications
  • Challenges in NLP

Lesson 2: Text Preprocessing

  • Tokenization
  • Stop Words Removal
  • Stemming and Lemmatization

Lesson 3: Sentiment Analysis

  • Basics of Sentiment Analysis
  • Techniques and Tools
  • Applications

Lesson 4: Topic Modeling

  • Introduction to Topic Modeling
  • Latent Dirichlet Allocation (LDA)
  • Applications in Text Analysis

Module 12: Deep Learning

Lesson 1: Introduction to Deep Learning

  • Basics of Deep Learning
  • Neural Networks Overview
  • Applications

Lesson 2: Neural Networks Basics

  • Neural Network Architecture
  • Activation Functions
  • Training and Optimization

Lesson 3: Convolutional Neural Networks (CNNs)

  • Basics of CNNs
  • Applications in Image Processing
  • CNN Architecture and Layers

Lesson 4: Recurrent Neural Networks (RNNs)

  • Basics of RNNs
  • Applications in Sequential Data
  • LSTM and GRU