Courses Description

Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches,but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry.

Course content

• Foundation of AI
  • What is AI?
  • Python for AI
  • Probability & Statistics
  • Visualization Techniques
  • Case Study
• Machine Learning : Supervised Learning
  • Regression (Linear, Multiple and Logistic)
  • Classification (K-NN, Naive Bayes) Techniques
  • Decision Trees
  • Case Study
• Machine Learning : Unsupervised Learning
  • K-means Clustering
  • Hierarchical Clustering
  • High-dimensional Clustering
  • Case Study
• Machine Learning : Ensemble Techniques
  • Boosting
  • Bagging
  • Submenus, Context Menus, Pop-up Menus
  • Case Study
• Machine Learning : Reinforcement Learning
  • Value based methods
  • Q-learning
  • Policy-based methods
• Deep Learning
  • Neural Network Basics
  • Deep Neural Networks
  • TensorFlow using Neural Networks & Deep Learning
  • Case Study
• Natural Language Processing
  • Statistical NLP and text similarity
  • Text Summarization
  • Syntax and Parsing techniques
  • Semantics and Generation
  • Case Study
• Computer Vision
  • Convolutional Neural Networks
  • Keras Library for Deep Learning in Python
  • Pre-processing Image Data
  • Object and face recognition using OpenCV
  • Case Study
• Intelligent Agents
  • Uniform and heuristic-based search techniques
  • Planning and constraint satisfaction techniques
  • dversarial search and its uses
  • Case Study