Course Overview
Machine learning is a topic which is leading the trend in the transformation of organisations from digitisation to data-driven. This course aims to expose the learner to the code underpinnings of constructing and assessing a subset of the machine learning tasks, namely the classification, time series and clustering/feature extraction tasks. The objective is to target commonly encountered tasks in the space of financial data analysis. Participants will be able to gain familiarity in such tasks and have a deeper understanding of tackling them.
Course Duration
2 days (16 hours)
Course Fee
$866.70/- (GST Inclusive)
Medium of Instruction: English
Mode:
Webinar
Course Outline
Day 1:
Module 1:
To enable learners to have an overall understanding of the landscape of Data Mining and Machine Learning.
- Introduction: Machine Learning and Data Mining
- Supervised and Unsupervised learning techniques
Module 2:
To give the participants the very first steps towards forming hypothesis and doing predictive analytics, learning some of the classification techniques in supervised learning.
- Machine Learning and Classification
- Classification: Logistic Regression, Decision Trees, Random Forest, XGBoost
Module 3:
It aims to equip participants with the basics of how supervised machine learning models work and how evaluation and optimization can be carried out.
- Evaluation and Metrics
- Understanding and comparing model results
- Feature importance
Day 2:
Day 2 will continue the journey for the learners in machine learning with focus on Neural Networks and Unsupervised learning techniques.
Module 4:
This module work with exploring time-based data, here participants will learn about how to assess and play with past data to explain future movements.
- ARIMA model – seasonality
Module 5:
To equip learners in understanding some of the Unsupervised learning techniques which is required for machine learning.
- Clustering and Segmentation
- Principal Component Analysis
- Feature Reduction
- AB Testing
Module 6:
To further equip learners with knowledge on neural networks, this is a flexible method for which new machine learning fields are being pioneer.
- Introduction to neural networks
- Working with a neural network
Learning Outcome
- Construct analytics models/results as part of solutions to address business problems
- Evaluate the performance of analytics models
- Analyse the results or outputs of analytics models
- Evaluate the importance of features which are used
- Understand how to reduce features
Mode of Assessment
Written and Practical
System Requirements
Windows OS or MAC OS installed with anaconda