Artificial Intelligence

The domain of machine learning and its implications to the artificial intelligence sector, the advantages of machine learning over other conventional methodologies, introduction to Deep Learning within machine learning. 

How it differs from all others methods of machine learning, training the system with training data, supervised and unsupervised learning, classification and regression supervised learning, clustering and association unsupervised learning, the algorithms used in these types of learning.

Introduction to AI, Introduction to Neural Networks, Supervised Learning with Neural Networks, Concept of Machine Learning, Basics of statistics, probability distributions, hypothesis testing, Hidden Markov Model.

Course Objectives:

• Multi-layered Neural Networks
• Training of neural networks
• Deep Learning Libraries
• Keras API
• TFLearn API for TensorFLow
• DNN: Deep Neural Networks
• CNN: Convolution Neural Networks
• RNN: Recurrent Neural Networks
• GPU in Deep Learning
• Autoencoders & Restricted Boltzmann Machine (RBM)
• Deep learning applications
• Chatbots
• Automated conversation bots using one of the descriptive techniques
• AI Deep Learning Projects