Course Overview

“The key to artificial intelligence has always been the representation.”
—Jeff Hawkins

The simulation of human intellect by machines is known as “artificial intelligence”, machines that display human characteristics such as learning, problem solving and decision making. In today’s world, firms that want to gain a competitive advantage employ various kinds of artificial intelligence to better understand their consumers and to increase their knowledge of a customer’s needs. This diploma in Artificial Intelligence delves into Expert System, Machine Learning, Deep learning, Reinforcement learning, Data Mining, and Statistics. Using technologies such as Colab and Python, it walks a business through all the steps, from model training to deploying models into production that are required to begin leveraging the potential of AI.

Course Modules

AI Foundations (3 weeks)

  • History of AI
  • Current state-art-of-the AI tools
  • Expert Systems
  • Application of AI
  • Small live Demo of AI application
  • Crash Course of python
  • Introduction to Explainable AI
  • Future of AI
  • Lab Exercise

Advanced AI Concepts (2 weeks)

  • Theory of Types of Data
  • Exploratory Data Analysis Theory
  • Supervised and Unsupervised Learning techniques
  • Introduction to the tools like Panda’s profiling and D tale
  • Exploratory Data Analysis (Demonstrations)
  • Automated features extractions
  • Lab Exercise

Advance AI Algorithm and Evaluation (2 weeks)

  • Machine learning Algorithms
  • Evaluation of ML project
  • Deployment of the Model
  • Introduction to Deep learning
  • Introduction to reinforcement learning
  • Lab Exercise

Projects (3 weeks)

  • Sentiment Analysis (theory and practical)
  • Image Classification (theory and practical)
  • Intelligent Conversational Chatbot (theory and practical)
  • Predictive Modelling (Classification and Regression (theory and practical)
  • Lab Exercise

Course content is indicative and subject to the student learning rate

Learning Outcomes

Learners will be able to illustrate how artificial intelligence, in the form of applied machine learning and deep learning, can be incorporated into their individual organizations through this curriculum. It will guide them through the process of obtaining data, utilizing it to train models, and incorporating these models into the organization’s infrastructure to reap the benefits of artificial intelligence. This credential is intended for students who come from non-cognate backgrounds and have no technical expertise, as well as those who come from cognate backgrounds but lack technical skills and knowledge.

Course Award

City Colleges Diploma


Dr. Abhishek Kaushik is a post-doctoral research fellow and Senior Industry Consultant (Artificial intelligence). He is currently working with UCD and Industrial partners to build the REG Tech system with machine learning and reinforcement learning. He has lectured in multiple Irish educational institutions.

He is one of the few Ph.D. students who completed in less than four years from Adapt Centre, Dublin City University. He received his master’s degree in Information Technology from Kiel University of Applied Science (Germany) in 2016, and his bachelor’s degree in Computer Science and Engineering from Kurukshetra University (India) in 2012. His research focuses on | NLP | Conversational Search | Bot | Machine Learning | Deep Learning | Information Retrieval | Reinforcement Learning | User Behaviour | Reg Tech | Legal System | Explainable AI |. He has also published some outstanding research work in collaboration with UCD and DCU.

He has gained about five years of experience in Industrial Research while working in Ireland, Germany, and India. He has been awarded multiple grants and scholarships during his research journey by various funding agencies. He has participated in many research talks in esteemed universities all around the globe (Canada, UK, US, India, etc). He is an active reviewer of numerous journals and conferences. He wrote multiple chapters focussing on applications of AI published by springer.


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Live online training with real-time instructor-student interaction.
  • Live & fully online
  • Archived for review
27th September
10 weeks, 1 evening per week from 6.30 to 9.30pm GMT
Study in a classroom environment one evening a week.
  • City centre location
  • Fully interactive
  • Limited class sizes
27th September
10 weeks, 1 evening per week from 6.30 to 9.30pm GMT

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