Certified Machine Learning Expert (CMLE)

Machine Learning is revolutionizing the world by allowing computers to learn as they progress forward with large data sets, overwriting overcoming all programming pitfalls and impasses. Machine Learning builds algorithms, which when exposed to high volumes of data, can self-teach and evolve.

When this technology powers Artificial Intelligence (AI) applications, the combination can be powerful. Smart robots can already be found around us doing all our jobs with more speed and accuracy, and continuously improving themselves at every step.

Upcoming Batches

  • Singapore: 17 to 21 Sept 2018, 12 to 16 Nov 2018 | Book Now
  • Bangkok, Thailand: 8 to 12 Oct 2018, 10 to 14 Dec 2018 | Book Now
  • Johor, Malaysia: 29 Oct to 2 Nov 2018, 3 to 7 Dec 2018 | Book Now
  • Kuala Lumpur, Malaysia: 1 to 5 Oct 2018, 26 to 30 Nov 2018 | Book Now
  • Penang, Malaysia: 12 to 16 Nov 2018 | Book Now
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.
Course Duration
40 hours / 5-Day
Course Outline
MODULE 1: Introduction to Machine Learning  

  • 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
  • MODULE 9: Case Studies and Discussion
    MODULE 10: Hands-On Exercise
    Pre-Requisite
    It is recommended that participants have a valid Certification of Competency in Machine Learning Essentials (MLE), 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.
    Who Will Benefit from the Course
    Certified Machine Learning Expert (CMLE) is designed for participants 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.

    This course will equip you with a set of skills that you can draw on to implement the technology in your organisation.

    Class is limited to 20 participants as hands-on sessions and real-time demonstration is expected.