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course | AI Development Essentials

Join the AI Development ... course taught in English – Building on AI foundational knowledge, this program moves participants to an intermediate level, equipping them…

SMCS-3267 | AI Development Essentials

Course Sector : Sales, Marketing and Customer Service

Course dates

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Duration	Date from	Date to	Course Venue	Course fees	Book a course
5 Days	18/10/2026	22/10/2026	Online	$2,150	Book now
5 Days	05/04/2027	09/04/2027	Athens	$4,950	Book now
5 Days	09/05/2027	13/05/2027	Riyadh	$4,250	Book now
5 Days	09/08/2027	13/08/2027	Dubai	$4,250	Book now

Course Introduction

Building on AI foundational knowledge, this program moves participants to an intermediate level, equipping them to design, implement, and evaluate AI models. Participants learn practical techniques in machine learning, model evaluation, and optimization, enabling them to contribute to real-world AI projects.

Training Course Methodology

The training course is designed to be interactive and participatory, and includes various learning tools to enable the participants to operate effectively and efficiently in a multifunctional environment. The course will use lectures and presentations, exercises, experiential and exposure to real world problems and policy choices confronting delegates.

Course objective

Build and implement basic machine learning models.
Evaluate and optimize AI models for performance.
Work with structured and unstructured data for AI applications.
Understand key algorithms used in intermediate AI projects.
Apply AI knowledge to real-world problem-solving scenarios.

Course audience

Junior Data Scientists
Product Managers & Innovators
Business Leaders & Decision-Makers

Course Outline | DAY 01

Overview of Intermediate AI Concepts

Recap of AI foundations
Introduction to machine learning workflows
Feature engineering and selection
Understanding model complexity and bias-variance tradeoff
Case studies of intermediate AI applications

Course Outline | Day 02

Supervised Learning Techniques

Advanced regression techniques
Classification methods (e.g., decision trees, SVMs, k-NN)
Model training, testing, and validation
Metrics for evaluating supervised learning models
Common challenges and troubleshooting

Course Outline | Day 03

Unsupervised Learning Techniques

Clustering algorithms (k-means, hierarchical)
Dimensionality reduction (PCA, t-SNE)
Association rule learning
Evaluating unsupervised models
Practical applications and examples

Course Outline | Day 04

Model Optimization and Deployment

Hyperparameter tuning
Overfitting and underfitting management
Cross-validation techniques
Introduction to AI deployment pipelines
Best practices for model maintenance

Course Outline | Day 05

AI Application and Integration

Applying AI to business and technical problems
Using intermediate AI models in projects
Ethical and responsible AI considerations
Hands-on mini-project or scenario analysis
Planning next steps for advanced AI skill development

Course Certificates

BOOST’s Professional Attendance Certificate “BPAC”

BPAC is always given to the delegates after completing the training course,and depends on their attendance of the program at a rate of no less than 80%,besides their active participation and engagement during the program sessions.

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You typically need:Programming skills, especially Python.Mathematics and statistics.Machine learning and deep learning knowledge.Data collection, cleaning, and analysis.AI frameworks such as PyTorch or TensorFlow.Understanding of APIs, databases, and cloud platforms.Model evaluation, deployment, and monitoring skills.

The core foundations are data, algorithms, computing power, machine learning models, mathematics/statistics, and evaluation. For modern generative AI, knowledge of LLMs, prompting, embeddings, vector databases, and AI APIs is also useful.

There isn't one universally accepted seven-type classification. A useful way to categorize AI applications/capabilities is:Machine learningDeep learningNatural language processing (NLP)Computer visionGenerative AIRoboticsExpert systems

If you mean broad categories of tools used to build and work with AI:AI assistants and chatbots – for text, research, and productivity.Machine learning frameworks – such as PyTorch and TensorFlow.Data science tools – such as Python, Jupyter, and pandas.Generative AI platforms/APIs – for building AI-powered applications.MLOps and cloud platforms – for training, deploying, and monitoring models.

Open Training Calendar

Since 2001, we’ve been at the forefront of professional training in the Middle East — shaping the future of learning and development one success story at a time. With a vision rooted in innovation and excellence, we help individuals, teams, and organizations reach their highest potential through integrated, future-ready training solutions. Our comprehensive programs combine global best practices with local insights, empowering people to grow, lead, and make a lasting impact in their industries.

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