Artificial Intelligence Professional Program

Preference Dates Timing Location Registration Fees
Instructor-Led Training

(In-Person and Live Webinars)
June 25, 28, July 2, 5, 9, 12, 16, 19, 2024 Tue. & Fri.: 7:00 PM - 9:30 PM (GMT+4) Dubai Knowledge Park or remotely (online). 1,250 USD
Certification Exam 28 July 2024 Sunday: 3:00 PM - 5:00 PM (GMT+4) Dubai Knowledge Park or remotely (online). Included

Course Description

This program offers a comprehensive learning experience in Python programming, data analysis, data visualization, and machine learning. Designed specifically for individuals new to the field of AI and data science, participants will gain a solid foundation in Python fundamentals, including data manipulation and control flow.

The program covers essential topics such as data structures, file I/O, and object-oriented programming, providing a well-rounded understanding of these key concepts. Participants will also learn data analysis techniques using popular libraries like Numpy and Pandas. 

The program further introduces supervised and unsupervised learning, guiding participants through building predictive models and exploring classification algorithms. Deep learning concepts, including neural networks and image classification, are also covered. Through hands-on exercises and real-world applications, participants will develop the necessary skills to leverage AI and Big Data effectively. This program is designed to empower beginners to embark on a successful journey into the exciting fields of AI and data science.  Upon successful completion of this program, the participants will earn a KHDA-Accredited AI Certificate.

Unit 1 – Python Programming Fundamentals

  • Establishing a Python Environment: Walkthrough of setting up a conducive Python development environment.
  • Creating a Virtual Environment: Process and benefits of segregating projects with separate Python virtual environments.
  • Setting up Your Github Repository: Fundamentals of using Github for version control and sharing code.
  • Overview of Jupyter Notebook IDE: Introduction to Jupyter, a powerful, flexible open-source IDE ideal for Python scripting.
  • Data Types: Exploration of Python’s built-in data types and their manipulation.
  • String Manipulation: Techniques for processing and handling Python strings.
  • Selection Statements: Understanding conditional logic with If, Else, and Elif statements.
  • For and While Loops: Grasp repetitive operations with Python’s For and While loop constructs.

Unit 2 – Data Structures & File I/O

  • Storing Data in Lists: Working with Python’s versatile List data structure.
  • Working with read-only Data using Tuples: Understanding immutable sequence type – Tuples.
  • Creating maps with Dictionaries: Manipulating Python’s built-in hashmap data structure – Dictionary.
  • Removing Duplicates using Sets: Understanding and applying Python’s set data structure.
  • File Input and Output: Basics of reading from and writing to files in Python.

Unit 3 – Object-Oriented Programming

  • Code reuse with Methods: Achieving code reusability with Python methods.
  • Encapsulation using Classes and Objects: Understanding encapsulation concept, creating and manipulating classes and objects.
  • Inheritance and Composition: Applying inheritance and composition to model real-world concepts.
  • Creating your own reusable modules and libraries: Building and managing personal Python modules and libraries.

Unit 4 – Data Analysis with Numpy and Pandas

  • Advantages of using Numpy Arrays: Insights into Numpy’s powerful N-dimensional array object.
  • Numpy Arrays Indexing: Techniques to access and manipulate elements in Numpy arrays.
  • Numpy Operations: Performing mathematical operations on Numpy arrays.
  • Pandas Dataframes: Working with Pandas dataframes, a two-dimensional labeled data structure.
  • Data Preprocessing: Handling missing values and categorical features in datasets.
  • Categorizing data with Groupby: Understanding and applying the Groupby operation in Pandas.
  • Merging and Joining Dataframes: Combining different Pandas dataframes using merge and join operations.
  • Loading Data into Dataframes: Importing data from various sources into Pandas dataframes.

Unit 5 – Data Visualization

  • Data Visualization with Matplotlib: Basics of creating static, animated, and interactive visualizations in Python using Matplotlib.
  • Data Visualization with Seaborn: Understanding Seaborn, a Python data visualization library based on Matplotlib.
  • Scatter, Join, Distribution and Regression Plots: Creating various types of plots to analyze data.
  • Interactive Data Visualization with Plotly: Introduction to Plotly, a Python graphing library that makes interactive, publication-quality graphs.
  • Geographical Data Visualization with Plotly: Exploring geographic data with Plotly.

Unit 6 – Machine Learning – Part I

  • Introduction to Machine Learning: Understanding the fundamentals of machine learning.
  • Supervised, Unsupervised and Reinforcement Learning: Distinguishing between different types of machine learning paradigms.
  • Supervised Learning (Classification, Regression): Basics of predictive models in machine learning: Classification and Regression.
  • Linear Regression: Building a predictive model using Linear Regression.
  • Building a Predictive Model for a Real Estate Firm: Applying machine learning to solve real-world business problems.

Unit 7 – Machine Learning – Part II

  • Building a Classification Model with Logistic Regression: Creating a binary classification model using Logistic Regression.
  • Evaluating Classification Models (Accuracy, Precision, Recall, F1-Score): Assessing the performance of classification models using common metrics.
  • Bias-Variance Tradeoff: Understanding the critical balance in machine learning models to improve generalization.
  • Classification – K-Nearest Neighbors (KNN): Introduction to instance-based learning algorithm K-Nearest Neighbors for classification problems.
  • Tuning a KNN Model: Exploring strategies to optimize the performance of a KNN model.
  • Unsupervised Learning – K-Means Clustering: Fundamentals of K-Means, a popular centroid-based clustering algorithm.

Unit 8 – Deep Learning

  • Neural Network Representation: Understanding the structure and components of neural networks.
  • Forward Propagation: Grasp the feedforward process in a neural network, where information flows from the input layer to the output.
  • Activation Functions: Exploration of various activation functions in neural networks and their role.
  • Cost Functions: Concept of cost functions in optimization of neural networks.
  • Back-Propagation with Gradient Descent: Understanding the learning mechanism in neural networks through backpropagation and gradient descent.
  • Image Classification with Deep Learning: Application of deep learning techniques for the task of image classification.
Python Course Content

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.


  • 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.