Course Info

Length: 1 Week

City: Singapore

Type: In Classroom

Available Dates

  • Mar-17-2025

    Singapore

  • June-16-2025

    Singapore

  • Sep-15-2025

    Singapore

  • Dec-15-2025

    Singapore

Dates in Other Venues

  • Jan-13-2025

    Dubai

  • Feb-10-2025

    London

  • Mar-17-2025

    Istanbul

  • Mar-17-2025

    Kuala Lumpur

  • Mar-17-2025

    Barcelona

  • Mar-17-2025

    Paris

  • Mar-17-2025

    Amsterdam

  • Mar-17-2025

    Dubai

  • Apr-14-2025

    London

  • May-12-2025

    Dubai

  • June-16-2025

    Kuala Lumpur

  • June-16-2025

    Istanbul

  • June-16-2025

    Barcelona

  • June-16-2025

    London

  • June-16-2025

    Amsterdam

  • June-16-2025

    Paris

  • July-14-2025

    Dubai

  • Aug-11-2025

    London

  • Sep-15-2025

    Paris

  • Sep-15-2025

    Amsterdam

  • Sep-15-2025

    Barcelona

  • Sep-15-2025

    Istanbul

  • Sep-15-2025

    Dubai

  • Sep-15-2025

    Kuala Lumpur

  • Oct-13-2025

    London

  • Nov-10-2025

    Dubai

  • Dec-15-2025

    Barcelona

  • Dec-15-2025

    London

  • Dec-15-2025

    Istanbul

  • Dec-15-2025

    Paris

  • Dec-15-2025

    Kuala Lumpur

  • Dec-15-2025

    Amsterdam

Course Details

Course Outline

5 days course

Introduction to Predictive Analytics

 

  • Overview of data analytics and its types:
  •     Descriptive
  •     Diagnostic
  •     Prescriptive
  •     Predictive
  • Understanding predictive analytics and its key concepts
  • The benefits of predictive analytics 
  • Role of machine learning in predictive analysis
  • Preparing data for analysis

Supervised Learning Techniques

 

  • Defining supervised learning
  • Types of supervised learning:

  •      Regression analysis
  •      Classification problems
  • Understanding Regression Analysis algorithms:

  •      Linear regression
  •      Polynomial regression
  •      Logistic regression

  • Understanding Classification problems:
  •      Decision tree and random forests algorithms
  •      Support vector machine (SUM) algorithm 
  • Metrics to evaluate regression and classification models

Unsupervised Learning

 

  • Defining unsupervised learning
  • Types of unsupervised learning

  •      Clustering algorithm
  •      Dimensionality reduction algorithm

  • Understanding Clustering algorithm:

  •      K- means clustering algorithm
  •      Density-based clustering algorithm
  •      Hierarchical clustering algorithm

  • Understanding Dimensionality reduction and its application

  •      Principles component analysis (PCA)
  •      Singular value decomposition (SVD)
  •      t-SNE

  •  Metrics to evaluate unsupervised leaning techniques

Advanced Analytics Techniques

  • Identifying ensemble learning methods:

  •     Bagging
  •     Random forests
  •     Boosting algorithms
  •     Stacking & voting classifiers

  • Utilizing XGBoost and LightGBM for gradient boosting
  • Defining deep learning concepts and the role of artificial neural networks
  • Exploring deep learning framework:

  •     TensorFlow
  •     Keras
  •     PyTorch

  • Building deep learning:
  •     Convolutional neural networks (CNN)
  •     Recurrent neural networks (RNN)

Model Selection & Optimization 

 

  • Determining the importance of selecting the best model
  • Cross-validation technique to select the best model
  • Exploring model bias-variance trade-off

  •        Overfitting
  •        Underfitting

  • Course recap and Q&A
  • Final project presentation