Certnexus Training in Kenya

Artificial intelligence (AI) and machine learning (ML) have become indispensable tools for many organizations. When utilized effectively, these technologies provide actionable insights that drive critical decisions and enable the creation of exciting, innovative new products and services. This course demonstrates how to apply various approaches and algorithms to solve business problems using AI and ML, follow a systematic workflow to develop robust solutions, leverage open-source and off-the-shelf tools to build, test, and deploy those solutions, and ensure user privacy is protected. Each topic area includes hands-on activities.

Course Objectives: By the end of this course, you will be able to implement AI techniques to solve business problems. You will:

• Define a general approach to address a specific business problem using applied AI and ML.
• Collect and refine datasets to prepare them for training and testing.
• Train and tune machine learning models.
• Finalize a machine learning model and present results to the relevant stakeholders.
• Develop linear regression models.
• Develop classification models.
• Develop clustering models.
• Develop decision trees and random forests.
• Develop support-vector machines (SVMs).
• Develop artificial neural networks (ANNs).
• Promote data privacy and ethical practices within AI and ML projects.

Target Audience: The skills covered in this course converge on three areas—software development, applied math and statistics, and business analysis. Ideal students may be strong in one or two of these areas and seek to expand their skills in the others, enabling them to apply artificial intelligence (AI) systems, particularly machine learning models, to business problems.

Consequently, the target student may be a programmer looking to develop additional skills to apply machine learning algorithms to business problems, or a data analyst who already possesses strong skills in applying math and statistics to business problems but aims to develop technology skills related to machine learning. A typical student should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-110) certification.

Breakthroughs in computing and engineering are propelling technological advancement, spanning from blockchain and artificial intelligence to gene editing and the Internet of Things. These innovations present significant opportunities for enhancing productivity and improving human well-being. However, as recent high-profile incidents demonstrate, they also introduce novel risks. Technology professionals are under growing pressure to address ethical challenges, striving to balance privacy, accuracy, fairness, and safety. This course equips learners with practical tools to manage ethical risks associated with emerging data-driven technologies, integrating theoretical foundations, regulatory frameworks, and industry best practices. Participants will develop the capabilities needed to navigate ethical dilemmas within their professional roles and organizations.