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

Page Link: Click here to know more and register

Website: Click here

Contact Number: +65 9106 3805

Name: Avanta Global

Email ID: digital@avanta.com.sg