Participants will be equipped with practical skills to implement classification models and regression models using various supervised learning algorithms such as linear and logistic regression models, k-nearest neighbours, neural networks, support vector machine, decision trees and ensemble methods such as random forest. Participants will also acquire the skills related to the implementation of unsupervised learning algorithms such as k-means clustering and other analytical methods such as correlation analysis and association rules to derive insights from their data. 

Learning Outcome

At the end of this course, participants will:
  • Acquire knowledge of AI and machine learning and its impact on enterprises with several use cases
  • Acquire knowledge on machine learning techniques: Supervised, Unsupervised & Reinforcement Learning
  • Understand the usage of ReLU as a deep learning-activation function and learning rate
  • Gain a solid understanding of discriminative and generative algorithms
  • Gain a solid understanding of key concepts like Principal Component Analysis (PCA), Hyperparameter tuning with Grid Search, Clustering, Classification, Regression & Neural Network

Who should Attend?

  • IT Executives/Managers
  • Risk Analyst/Management
  • Business Analyst
  • Data Analyst
  • Banking Executives/Managers
  • Software Engineers
  • System Engineers

Eligibility Criteria

Recommended to have some basic programming knowledge in any languages (preferred Python).

This course is endorsed under Critical Infocomm Technology Resource Programme Plus (CITREP+) Programme.
To find out more about CITREP+ Funding, please refer to Programme Support under CITREP+ page

Information as accurate as of 20 May 2021