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Machine Learning using Python Course

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


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

This course expects that learners come in a basic understanding of python, from there we will build upon their understanding and teach from a data point of view.


Weekday Class (Day 1-2)

09:00 AM – 18:00 PM

No Schedules Available

Weekend Class (Day 1-2)

09:00 AM – 18:00 PM

Proceed to Register(Step 2)
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Important Note:
1. [Company Sponsored]: Company shall ensure there is employer-employee relationship and CPF contribution for employer-sponsored trainees. Should SDF grant disbursement be rejected due to wrong company / UEN provided to Avanta, the company shall be liable to pay the full course fees with out grant.
2. Registration is valid only if, each participant Mobile Number & Email address is provided (Shouldn't provide same contact details of Person in charge)

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