Artificial INTELLIGENCE Certification.
The Artificial Intelligence Certification
The Artificial Intelligence Certification
Tools covered
Is the Certification Program for you?
What will you learn?
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 MLC.
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.
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.
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.
Machine Learning 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.
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.
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.
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
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.
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.
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.
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.
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.
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.
Machine Learning 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.
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.
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.
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
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.
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.
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.
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.
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.
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.
What's Next?
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.
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 fields of Data Science and Machine Learning.
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 and Machine Learning. 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
Mohammad Srouji
Enrollment Process
APPLY
Submit your program application with correct and full information
INTERVIEW
Go through general interview and a technical assessment
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
$2,967/ person
- Recorded sessions including lectures and coding labs
- Live instructor-led online workshops
- Learn from others: invited online talks & discussions
- Weekly office hours & round-the-clock advising
- Group capstone project with guided mentorship
- Career development workshops & mentorship
Here's what previous graduates had to say about the program
