Certified Machine Learning Expert (CMLE)

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Classroom Session: 9:30pm to 5:30pm (SGT / UTC +8) 5 Lessons Click on preferred date to register
Online Session: 5:00pm to 9:00pm (SGT / UTC +8) 10 Lessons Click on preferred date to register

Course Information

  • Duration: 5 Day / 40 Hours
  • Who Should Attend: Anyone who are interested in pursuing a career in the areas of Machine Learning and would like the opportunity to learn in a supportive and encouraging environment

Course Objective

Certified Machine Learning Expert (CMLE) is designed to allow participants acquire knowledge on how to use Python to apply powerful machine learning methods and gain insight into real-world applications.


It is recommended that participants have a valid Business Analytics Essential (BAE), or some basic understanding in software development


Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Machine Learning based on the syllabus covered


Participants will be awarded and recognised as a Certified Machine Learning Expert (CMLE) upon meeting the requirements and passing the examination.

Module 1 Introduction to AI and Machine Learning

  • What is Artificial Intelligence (AI)
  • Concepts of machine learning
  • Data and machine learning
  • Real world applications of machine learning
  • How machine learning works

Module 2 Data Structures & Managing Data Using Python

  • Data and Data Types
  • Deep Dive into Python
  • Data Types
  • Variable Operators in Python
  • Data Vectors and Data Frames
  • Reading and Writing Data Files to Python
  • Communicating with Database via Python
  • Executing SQL Using Python
  • Joining Structured & Semi Structured Data with Python
  • Big Data Concepts & Application of Python

Module 3 Exploring Data Using Python

  • Bar Chart
  • Pie Chart
  • Trend Chart
  • Histogram
  • Box Plot
  • Scattered Plot & Correlation
  • Other Chart

Module 4 Basic Classification Models & Techniques

  • Concept of Classification
  • Supervised and Unsupervised Classification
  • Decision Tree Classification
  • Random Forest Classification
  • Naive Bayes Classification
  • Support Vector Machine

Module 5 Regression Methods and Forecasting

  • Concept of Regression Modelling
  • Modelling Stages
  • Simple linear Regression
  • Multiple Linear Regression
  • Refining the Model
  • Model Validation and Prediction
  • Logistic Regression

Module 6 Finding Data Patterns Using Association Rules

  • Concepts of Association Rules
  • Market Basket Analysis (MBA)
  • Support, Confidence & Lift
  • Other Techniques of Association
  • Application of Association

Module 7 K-Means Clustering

  • Cluster Analysis
  • Hierarchical Clustering
  • K-Means Clustering

Module 8 Evaluating and Improving Model Performance

  • Model Evaluation and Comparison
  • Parameters to Evaluate the Model Accuracy
  • Selection of Right Parameters for a Model

Certified Machine Learning Expert (CMLE) involves rigorous usage of real-time case studies, hands-on exercises and group discussion