Artificial Intelligence Professional Program
Preference | Dates | Timing | Location | Registration Fees |
---|---|---|---|---|
Weekdays Program (In-Person and Live Online) | October 6, 8, 13, 15, 20, 22, 27, 29, 2025 | Mon & Wed: 6:30 PM - 9:00 PM (GMT+4) | Dubai Knowledge Park or remotely (online) | USD 1,500 |
Certification Exam (Optional, for credential) | November 5, 2025 | Wed: 6:30 PM - 8:30 PM (GMT+4) | Dubai Knowledge Park or remotely (online) | USD 300 |
About the Program
Artificial Intelligence (AI) remains one of the most in-demand skills in today’s high-tech industry. Global surveys, including McKinsey’s 2025 State of AI report, highlight a persistent shortage of skilled data scientists and AI practitioners, with many organizations struggling to find the talent needed to implement AI initiatives.
Our AI Professional Program equips participants with the technical knowledge, hands-on experience, and certification required to succeed in the field. The curriculum begins with Python programming fundamentals and progresses through data analysis, data visualization, and machine learning—covering supervised, unsupervised, and deep learning. Participants deepen their expertise through real-world projects and gain exposure to best practices for designing, building, and training AI models, guided by experienced faculty and industry mentors.
The program culminates in the Innosoft Certified AI Professional Exam, a performance-based assessment of applied skills in Python, data analysis, and machine learning. Successful graduates earn a KHDA-accredited certificate, affirming their readiness for advanced study and professional roles in AI and data science.

Unit 1 – Python Programming Fundamentals
- Setting up a Python development environment and virtual environments
- Version control with GitHub
- Using Jupyter Notebook for scripting and data analysis
- Data types, string manipulation, conditionals, and loops
- Project: Cryptocurrency Data Scraper using Python and APIs
Unit 2 – Data Structures & File I/O
- Lists, tuples, sets, and dictionaries
- File input and output operations
- Project: MovieLens Dataset Analysis (extracting and processing user demographic data)
Unit 3 – Object-Oriented Programming
- Functions, scope, and lambda expressions
- Classes, objects, inheritance, and composition
- Error handling and modular programming
- Project: Flight Reservation System (applying OOP concepts)
Unit 4 – Data Analysis with NumPy and Pandas
- Data cleaning, preprocessing, and transformation
- Working with structured and unstructured data
- Exploratory Data Analysis (EDA)
- Project: San Francisco Employee Salaries Dataset Analysis
Unit 5 – Data Visualization
- Static visualizations with Matplotlib and Seaborn
- Interactive visualizations with Plotly
- Integrating Python data with Tableau dashboards
- Project: Financial & Cryptocurrency Data Visualization Dashboard
Unit 6 – Machine Learning – Part I
- Introduction to AI and Machine Learning
- Supervised learning: regression and classification
- Unsupervised learning: clustering
- Project: Real Estate Price Prediction using Linear Regression
Unit 7 – Machine Learning – Part II
- Logistic Regression for binary classification
- Model evaluation (accuracy, precision, recall, F1-score)
- K-Nearest Neighbors (KNN) and hyperparameter tuning
- K-Means clustering for segmentation
- Project: Graduate Admissions Prediction & Customer Segmentation
Unit 8 – Deep Learning
- Neural network basics: architecture, forward propagation
- Activation and cost functions
- Backpropagation and gradient descent
- Image classification with neural networks
- Project: Image Classification using a Deep Neural Network
Unit 9 – Certification Exam Preparation
- Comprehensive review of Python, data analysis, visualization, and machine learning
- Exam prep project simulating a real-world AI workflow
- Innosoft Certified AI Professional Exam: a performance-based assessment in Python, data analysis, and machine learning
- Graduates earn a KHDA-accredited certificate, affirming readiness for advanced study and professional roles in AI and data science
Target Audience
- Beginners who want to learn programming and have chosen Python as their first language.
- Professionals looking to upskill by learning Python for data analysis, automation or web development.
- Students who want to strengthen their resume and build projects by learning Python.
- Researchers and Academics who need Python for data manipulation and analysis.
- Data Analysts looking to perform advanced data analysis and visualization using Python libraries.
- Software Developers looking to integrate Python into their development stack.
Prerequisites
- A basic understanding of programming concepts.
- Familiarity with mathematical concepts (optional, but helpful for data analysis).
- Access to a computer with a modern web browser.
- An eagerness to learn and explore new skills!
After the Course
- Gain hands-on skills in data analysis by learning to manipulate, process, clean, and crunch datasets in Python.
- Acquire expertise in data visualization techniques using Python libraries to create interactive and insightful graphs and charts.
- Develop a strong foundation in machine learning algorithms, understanding how they work and how to implement them using Python.
- Enhance your problem-solving abilities by engaging in real-world projects and case studies in data analysis, visualization, and machine learning.
- Be adept at using Python libraries such as Numpy, Pandas, Matplotlib, and Scikit-learn for data science applications.
- Gain the knowledge and practical skills necessary to excel in the AI Certified Professional Exam.
- Be part of a learning community where you can share your insights and learn from the experiences of others in the data science field.
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