Certified Machine Learning Expert (CMLE)

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 R-tool to apply powerful machine learning methods and gain insight into real-world applications.

Pre-Requisite

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

Examination

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

Certification

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 Understanding R – Data Structures & Managing Data

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

Module 3 Exploring Data Using R

  • 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