Course Overview

Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services.

This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users.


You should have experience working with databases and a high-level programming language such as Python, Java, or C/C++. You can obtain this level of skills and knowledge by taking the following:

Logical Operations or comparable course:

> Database Design: A Modern Approach

> Python® Programming: Introduction

> Python® Programming: Advanced


Module 01

Solving Business Problems Using AI and ML

Identify AI and ML solutions for Business Problems

Formulate a Machine Learning Problem

Select Approprriate Tools

Module 02

Collecting and Refining the Dataset

Collect the Dataset

Analyze the Dataset to Gain Insight

Use Visualizations to Analyze Data

Prepare Data

Module 03

Setting up and Training a Model

Set up a Machine Learning Model

Train the Model

Module 04

Finalizing a Model

Translate Results into Business Actions

Incorporate a Model into a Long-Term Business Solution

Module 05

Sapiente Building Linear Regression Models

Build a Regression Model Using Linear Algebra

Build a Regularized Regression Model Using Linear Algebra

Build an Iterative Linear Regression Model

Module 06

Building Classification Models

Train Binary Classification Models

Train Multi-Class Classification Models

Evaluate Classification Models

Tune Classification Models

Module 07

Building Clustering Models

Build k-Means Clustering Models

Build Hierarchical Clustering Models

Module 08

Building Advanced Models

Build Decision Tree Models

Build Random Forest Models

Module 09

Building Support-Vector Machines

Build SVM Models for Classification

Build SVM Models for Regression

Module 10

Buidling Artificial Neural Networks

Build Multi-Layer Perceptron (MLP)

Build Convolutional Neural Network (CNN)

Module 11

Promoting Data Privacy and Ethical Practices

Protect Data Privacy

Promote Ethical Practices

Establish Data Privacy and Ethics Policies


Course Duration

5 days

Exam Code



Course Fees

RM 4,240

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Our Address

Unit C-15-3A, Block C,
Dataran 3 Dua (3 Two Square),
No. 2, Jalan 19/1,
46300 Petaling Jaya, Selangor, Malaysia

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+603-7960 1922

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