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
INTRODUCTION TO PROGRAMMING USING PYTHON
Exam Code: 98-381
Duration: 5 days
Perform Operations Using Data Types and Operators
This Module Explains How to Use Python Operators and Data Types to Achieve A Specified Result.
Control Flow with Decisions and Loops
This Module Explains How to Use Control Flow and Looping Operations in Python.
Perform Input and Output Operations
This Module Explains How to Construct Input and Output Operations Using Files or from the console.
Document and Structure Code
This Module Explains How to Structure and Document Well-Written Python Code.
Perform Troubleshooting and Error Handling
This Module Explains How to Perform Troubleshooting and Error Handling Operations in Python.
Perform Operations Using Modules and Tools
This Module Explains How to Use Built-In Modules.
ARTIFICIAL INTELLIGENCE (AI) PRACTITIONER
Exam Code: AIP-110
Duration: 5 days
Exam Certification Body:
Solving Business Problems Using AI and ML
Identify AI and ML solutions for Business Problems
Formulate a Machine Learning Problem
Select Approprriate Tools
Collecting and Refining the Dataset
Collect the Dataset
Analyze the Dataset to Gain Insight
Use Visualizations to Analyze Data
Setting up and Training a Model
Set up a Machine Learning Model
Train the Model
Finalizing a Model
Translate Results into Business Actions
Incorporate a Model into a Long-Term Business Solution
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
Building Classification Models
Train Binary Classification Models
Train Multi-Class Classification Models
Evaluate Classification Models
Tune Classification Models
Building Clustering Models
Build k-Means Clustering Models
Build Hierarchical Clustering Models
Building Advanced Models
Build Decision Tree Models
Build Random Forest Models
Building Support-Vector Machines
Build SVM Models for Classification
Build SVM Models for Regression
Buidling Artificial Neural Networks
Build Multi-Layer Perceptron (MLP)
Build Convolutional Neural Network (CNN)
Promoting Data Privacy and Ethical Practices
Protect Data Privacy
Promote Ethical Practices
Establish Data Privacy and Ethics Policies
09.00am - 05.00pm
09.00am - 05.00pm
Unit C-15-3A, Block C,
Dataran 3 Dua (3 Two Square),
No. 2, Jalan 19/1,
46300 Petaling Jaya, Selangor, Malaysia