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.

Human intelligence is much more differentiated, but the focus on five key cognitive capacities resulted in a great leap forward in ai research. Instead of attempting to program “General Intelligence,” as found in humans, the focus shifted to precisely defined tasks.


The skills covered in this course converge on 3 areas—software development, applied math and statistics, and business analysis.

Candidates gain their skills in the other areas so they can apply artificial intelligence (AI) systems, particularly machine learning models to business problems.


10 days training comprising of the following


Exam Code: 98-381
Duration: 5 days

Module 01

Perform Operations Using Data Types and Operators

This Module Explains How to Use Python Operators and Data Types to Achieve A Specified Result.

Module 02

Control Flow with Decisions and Loops

This Module Explains How to Use Control Flow and Looping Operations in Python.

Module 03

Perform Input and Output Operations

This Module Explains How to Construct Input and Output Operations Using Files or from the console.

Module 04

Document and Structure Code

This Module Explains How to Structure and Document Well-Written Python Code.

Module 05

Perform Troubleshooting and Error Handling

This Module Explains How to Perform Troubleshooting and Error Handling Operations in Python.

Module 06

Perform Operations Using Modules and Tools

This Module Explains How to Use Built-In Modules.


Exam Code: AIP-110
Duration: 5 days
Exam Certification Body:

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


Week 01

10th-14th August

09.00am - 05.00pm

Week 02

17th-21st August

09.00am - 05.00pm

Contact Us

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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