Online course / Flexible schedule

Artificial INTELLIGENCE Certification.

From basic Python knowledge to complete understanding and implementation of Machine Learning algorithms.

The Artificial Intelligence Certification​

This is a 16-week intensive training program designed to equip participants from the Middle East and North Africa (MENA) region with the necessary knowledge, tools, and know-how for market-ready skills. The program was carefully designed by our team with industry and academic expertise to ensure participants learn the foundational knowledge they need while engaging in real-world projects and challenges that help them attain the skills needed for today’s industry job roles.

The Artificial Intelligence Certification​

This is a 16-week intensive training program designed to equip participants from the Middle East and North Africa (MENA) region with the necessary knowledge, tools, and know-how for market-ready skills. The program was carefully designed by our team with industry and academic expertise to ensure participants learn the foundational knowledge they need while engaging in real-world projects and challenges that help them attain the skills needed for today’s industry job roles.

Tools covered

Is the Certification Program for you?

A fresh graduate who wants to build a career in Machine learning. ​
An entrepreneur looking to launch a new tech venture that builds on AI solutions.
An enthusiast looking to transition into the Machine Learning field early on in your career, and you are wondering where to begin.
A working professional who wants to dive deeper into the world of Artificial Intelligence with the right tools and projects.

Our Partner Network

Interested in hiring our graduates?

3 Different Specializations
you will

Pick your track

A Machine Learning Engineer will put more focus on the modeling-related tasks: training, testing, comparing and fine-tuning models to deploy them into production at scale. Also, the Machine Learning Engineer will be responsible for monitoring these models once in production, and retraining them when necessary to make sure they are performing as expected.

Pick your track

A Data Scientist for instance will put more focus on the data-related tasks: extracting, cleaning, transforming, and visualizing data to derive valuable insights from it. Also, a Data Scientist can use this data to build predictive models for testing purposes.

Pick your track

A Data Engineer’s primary job is to prepare data for analytical or operational uses. They are typically responsible for building data pipelines to bring together information from different source systems. Data engineers often work as part of an analytics team alongside data scientists.

Machine Learning Track

Pick your track

A Machine Learning Engineer will put more focus on the modeling-related tasks: training, testing, comparing and fine-tuning models to deploy them into production at scale. Also, the Machine Learning Engineer will be responsible for monitoring these models once in production, and retraining them when necessary to make sure they are performing as expected.

Neural Networks & Deep Learning

You will learn about neural networks’ mathematical foundations and why neural networks have brought about great advancements to the field of Machine Learning. You will get introduced to deep learning libraries in Python and discover techniques for improving deep learning models’ performance.

Deep Learning in Advanced Data Types

You will learn about new deep learning architectures suitable for different data types. You will understand what makes these data types unique and why there is a need to make use of advanced deep learning designs. You will explore foundations of the Computer Vision, Natural Language Processing, and Time-series signals fields.

Machine Learning in Production

Now that your foundation is solid, you will explore machine learning in practice: what are the challenges commonly faced when these models are deployed in real-world and how to overcome them, what are some best practices to abide by, and what are tools that can help you scale to real-world implementations.

Data Science Track

Pick your track

A Data Scientist for instance will put more focus on the data-related tasks: extracting, cleaning, transforming, and visualizing data to derive valuable insights from it. Also, a Data Scientist can use this data to build predictive models for testing purposes.

Data Preprocessing

You will explore different types of data and what kind of operations can be performed on each in order to better extract useful information. You will also learn how to structure sequential data to conduct your analysis.

Neural Networks & Deep Learning

You will learn about Deep Learning and discover its great advancements. You will get introduced to deep learning libraries in Python and discover different Neural Network architectures that help you build deep learning solutions when dealing with different kinds of data.

Data Science Toolkit

You will learn how you can scrape data from the web using python libraries. You will get introduced to relational and non-relational databases for storing data, and finally, you will learn about some data transformations.

Data Science in Production

Now that your foundation is solid, you will explore data science in practice. You will revisit the data science lifecycle to know more about each of its steps in production. You will learn about the challenges that might occur and the best practices to overcome them, along with tools that help you scale to real-world implementations.

Data Storytelling

You will learn how to effectively communicate insights from your dataset using various types of visualizations. You will discover tools and techniques to create an engaging and informative story.

Detailed Curriculum

Machine Learning Specialization

Orientation & Prepwork

During orientation week, you will get onboarded to the program. This week will focus on providing you with review material on preparatory material you need to be comfortable with before you begin your journey in the AI Certification.

Data Science Foundations

You will discover the Data Science lifecycle and explore its different aspects in detail. You will also learn the basics of databases, the different data types you will encounter in your study, and different treatment methods for data.

Machine Learning Foundations

You will focus on understanding what a machine learning algorithm is composed of, get introduced to the most popular algorithms and explore their mathematical formulations, and be able to select the best model to build and train on different real-world problems.

Statistical Model Validation & Testing

You will discover common mistakes and mishaps in evaluation of machine learning methods. You will learn different evaluation criteria as well as statistical methods that guarantee your model comparisons are accurate and statistically supported.

Career Development Week

You will discover the soft skills needed for a successful career such as communication, leadership and networking skills. You will also get the chance to receive advice from an HR expert on designing your CV and building your portfolio.

Neural Networks & Deep Learning

You will learn about neural networks’ mathematical foundations and why neural networks have brought about great advancements to the field of Machine Learning. You will get introduced to deep learning libraries in Python and discover techniques for improving deep learning models’ performance.

Deep Learning in Advanced Data Types

You will learn about new deep learning architectures suitable for different data types. You will understand what makes these data types unique and why there is a need to make use of advanced deep learning designs. You will explore foundations of the Computer Vision, Natural Language Processing, and Time-series signals fields.

Machine Learning in Production

Now that your foundation is solid, you will explore machine learning in practice: what are the challenges commonly faced when these models are deployed in real-world and how to overcome them, what are some best practices to abide by, and what are tools that can help you scale to real-world implementations.

Career Development Week

You will discover the soft skills needed for a successful career such as communication, leadership and networking skills. You will also get the chance to receive advice from an HR expert on designing your CV and building your portfolio.

Research & Hot Topics

You will develop the skills needed to conduct successful research in machine learning, from identifying top references to stay up to date with the latest advancements to contributing back to the research community with your own advancements. You will also get introduced to various foundational and hot research topics in the field.

Capstone Projects

You will dedicate these three weeks to finalizing your capstone project solution with the support of guided, one-on-one mentor-ship from practitioners in the field. At the end, you will present your project solution to a panel who will assess your work as well as to recruiting firms working in the field of Machine Learning.

Career Fair

You will present your capstone projects and engage directly with recruiting firms in the Middle East and North African region looking to hire talent in the fields of Data Science, Machine Learning and Data Engineering. As part of the Career Fair, you will also get access to exclusive career training workshops that will help you better prepare your work portfolio (resume, LinkedIn, etc.) and your interviewing skills for Data Science and Machine Learning roles.

Data Science Specialization

Orientation & Prepwork

During orientation week, you will get onboarded to the program. This week will focus on providing you with review material on preparatory material you need to be comfortable with before you begin your journey in the AI Certification.

Data Science Foundations

You will discover the Data Science lifecycle and explore its different aspects in detail. You will also learn the basics of databases, the different data types you will encounter in your study, and different treatment methods for data.

Machine Learning Foundations

You will focus on understanding what a machine learning algorithm is composed of, get introduced to the most popular algorithms and explore their mathematical formulations, and be able to select the best model to build and train on different real-world problems.

Statistical Model Validation & Testing

You will discover common mistakes and mishaps in evaluation of machine learning methods. You will learn different evaluation criteria as well as statistical methods that guarantee your model comparisons are accurate and statistically supported.

Career Development Week

You will discover the soft skills needed for a successful career such as communication, leadership and networking. You will also get the chance to receive advice from an HR expert on designing your CV and building your portfolio.

Advanced Data Types

You will explore different types of data and what kind of operations can be performed on each in order to better extract useful information. You will also learn how to structure sequential data to conduct your analysis.

Neural Networks & Deep Learning

You will learn about Deep Learning and discover its great advancements. You will get introduced to deep learning libraries in Python and discover different Neural Network architectures that help you build deep learning solutions when dealing with different kinds of data.

Data Science Toolkit

You will learn how you can scrape data from the web using python libraries. You will get introduced to relational and non-relational databases for storing data, and finally, you will learn about some data transformations.

Data Science in Production

Now that your foundation is solid, you will explore data science in practice. You will revisit the data science lifecycle to know more about each of its steps in production. You will learn about the challenges that might occur and the best practices to overcome them, along with tools that help you scale to real-world implementations.

Data Storytelling

You will learn how to effectively communicate insights from your dataset using various types of visualizations. You will discover tools and techniques to create an engaging and informative story.

Career Development Week

You will discover the soft skills needed for a successful career such as communication, leadership and networking skills. You will also get the chance to receive advice from an HR expert on designing your CV and building your portfolio.

Research & Hot Topics

You will develop the skills needed to conduct successful research in machine learning, from identifying top references to stay up to date with the latest advancements to contributing back to the research community with your own advancements. You will also get introduced to various foundational and hot research topics in the field.

Capstone Projects

You will dedicate these three weeks to finalizing your capstone project solution with the support of guided, one-on-one mentor-ship from practitioners in the field. At the end, you will present your project solution to a panel who will assess your work as well as to recruiting firms working in the field of Data Science.

Career Fair

You will present your capstone projects and engage directly with recruiting firms in the Middle East and North African region looking to hire talent in the fields of Data Science, Machine Learning and Data Engineering. As part of the Career Fair, you will also get access to exclusive career training workshops that will help you better prepare your work portfolio (resume, LinkedIn, etc.) and your interviewing skills for Data Science and Machine Learning roles.

Data Engineering Specialization

Orientation & Prepwork

During orientation week, you will get onboarded to the program. This week will focus on providing you with review material on preparatory material you need to be comfortable with before you begin your journey in the AI Certification.

Introduction to Data Engineering

You will discover the Data Lifecycle and get familiar with Data Engineering related terminologies. You will also see the big picture of the process and get introduced to Big Data architectures and platforms and what defines data to be “big”. 

Programming Skills for Data Engineering

You will hone your skills in Python by understanding its basics and how data engineers use it to extract, transform, and load data. You will also learn how to deal with Linux environments, and run shell scripts in Linux. And for application’s fast deployment, you will learn how to use Docker to run different tools and applications .

Introduction to Databases

You will learn the different data models and the basics of SQL and NoSQL Databases. You will get familiar with common database management systems and build client sessions to access databases programmatically with Python.

Data Pipelines: Extract, Transform, & Load

You will learn the common data stacks and get introduced to some tools for each part of the pipeline. You will also explore each step of the process starting from extracting data from different sources, passing through data transformation and ending with the different options for loading data. At the end of this week, you will combine all of the previous steps to build a full pipeline.

Data Warehousing & Pipeline Automation

You will learn how to construct Data Warehouses, query data and create views in them. However, now that you have become familiar with the big picture of data engineering and data warehousing, and you know how to build data pipelines, you will learn how to automate, and orchestrate its execution.

Career Development Week

You will discover the soft skills needed for a successful career such as communication, leadership and networking. You will also get the chance to receive advice from an HR expert on designing your CV and building your portfolio.

Big Data: Hadoop & Spark Ecosystem

You will understand how to deal with Hadoop storage and processing tools, like HDFS and Spark. We will also dive deeper into Spark and how to program it with Python programming language to start building big data

Big Data Streaming and Automation

ou will learn about other integrated tools within Hadoop ecosystem, starting with dealing with streaming data sources with Apache Kafka, and how to use Kafka to produce/ consume data. Also, you will learn how to migrate relational data into your data warehouse using Apache Sqoop. Another tools Apache Oozie/ Airflow will be used to automate and orchestrate data pipelines.

Data Warehouses in Hadoop Ecosystem

You will learn how to deal with big data warehouses and how to set up Apache Hive on a Hadoop cluster. You will dive deep into how to load data into Hive from local file systems and HDFS programmatically. At the end of this week, you will be able to understand the relationships between data and business intelligence engineers.

Big Data Collection & Storage in Cloud

Beside working with data on-premises, you will dive into collecting and storing data in the cloud. This week, you will explore different services offered by cloud providers such as AWS for big data processing.

Big Data Processing & Analysis in Cloud

After exploring how to collect and store data in the cloud, you will learn how to process it, analyze it, and use AWS tools to create visuals. 

Career Development Week

You will discover the soft skills needed for a successful career such as communication, leadership and networking. You will also get the chance to receive advice from an HR expert on designing your CV and building your portfolio.

Capstone Projects

You will dedicate these three weeks to finalizing your capstone project solution with the support of guided, one-on-one mentor-ship from practitioners in the field. At the end, you will present your project solution to a panel who will assess your work as well as to recruiting firms working in the field of Data Engineering.

Career Fair

You will present your capstone projects and engage directly with recruiting firms in the Middle East and North African region looking to hire talent in the fields of Data Science, Machine Learning and Data Engineering. As part of the Career Fair, you will also get access to exclusive career training workshops that will help you better prepare your work portfolio (resume, LinkedIn, etc.) and your interviewing skills for Data Science and Machine Learning roles.

Alumni

Get Inspired and find your own path!
Whether you are a student, entrepreneur, freelancer, or career shifter, with the right certification, you can reach your goals in Artificial Intelligence.

Mohammad Srouji

Here’s how Mohamad was able to use the knowledge he acquired to develop his start-up

Jana Kabrit

Just like Jana start your journey in AI from zero!

Ali Wehbe

From Control Engineering to a Master degree in AI!

Enrollment Process

APPLY

Complete your program application by taking a technical assessment 

INTERVIEW

Go through a general interview to make sure that the program is the right fit for you

ACCEPTANCE

Receive an official acceptance for enrollment

How does the Certification Program Prepare you for the job market?

  • Master fundamental concepts and the language of Data Science and Machine Learning
  • Learn how to best represent yourself to recruiters in the Machine Learning space
  • Work in a team to solve a real-world, research-oriented challenge
  • Gain the skills to become a sufficient self-learner and investigator in the field of Machine Learning
  • Learn the best practices for Machine Learning in development and in research settings
  • Be guided by AI instructors and engineers to learn from real-life practitioners
  • Gain the soft skills to become a full-stack fit in a diverse Data Science team in the job market
Fees
$2,967/ person

Different financing options available

Here's what previous graduates had to say about the program

Hear it from our graduates