A R T I F I C I A L - I N T E L L I G E N C E

Artificial intelligence (AI) leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind and very popular in today’s world. It is the simulation of natural intelligence in machines that are programmed to learn and mimic the actions of humans. Problem solving, particularly in artificial intelligence, may be characterised as a systematic search through a range of possible actions in order to reach some predefined goal or solution from past experience.

  • Deep earning
  • Neural Network
  • Machine Learning
  • TensorFlow
I M R SMART @ Artificial Intelligence

Artificial Intelligence @ Objectives

  • To promote exchanges between A I and information processing.
  • To exploiting multidisciplinary knowledge within the organisations.
  • To foster the development and understanding of Artificial Intelligence.
  • To bring KM research and technology to meet the organisational challenges.
  • To obtain a understanding of Computational Intelligence and its Applications.

I M R SMART Training @ Artificial Intelligence

Artificial Intelligence course provides essential coverage of the primary parts of AI, including learning approaches, functional areas that AI systems are used for and a thorough introduction to neural networks. AI systems and neural networks are used for, and then maps individual practices, learning approaches, functionalities and neural network and  establishes a step-by-step process for assembling an AI system.

Artificial Intelligence (AI) is the best AI training, you will master various aspects of artificial neural networks, supervised and unsupervised learning, logistic regression with a neural network mindset, binary classification, vectorization, Python for scripting Machine Learning applications, and much more. The main goal of this course is to familiarize you with all aspects of AI so that you can start your career as an artificial intelligence engineer. 

  • Module 1: Deep Learning techniques
  • Module 2: Artificial Neural Networks
  • Module 3: ANN using the Training Data
  • Module 4: Neural Networks and its Applications
  • Module 5: TensorFlow and Tensor Processing Units
  • Module 6: Supervised and Unsupervised Learning Methods
  • Module 7: Machine Learning using Python
  • Module 8: Capstone Projects