Artificial Intelligence

Providing cutting-edge AI courses and leveraging necessary tools to break into AI careers with competence 

Application is currently closed.

Course Overview

This course provides a comprehensive overview of machine learning (ML), deep learning (DL), natural language processing (NLP), generative models, and artificial intelligence (AI). The course is designed to equip learners with the fundamental principles, techniques, and practical skills necessary to understand and build advanced AI systems.

Program Eligibility



Job Opportunities

Job opportunities you can apply for at the end of the training includes:


Our Three Pillars

Blended Peer Learning

You will be placed in small groups to work together as a team and complete academic goals in real-time virtual classrooms and during in-person sessions; which has goals at the individual and group level.

Mentor-Based Learning

A mentor will be available at all times during office hours to provide help and evaluate your work. We offer you a supportive and engaging work environment, where you can feel free to make mistakes and learn from your experiences.

Projects-Based Learning

Our learning methodology focuses 100% on the needs of today’s market. You will work on real-world projects similar to those you’ll find on the job and complete them using the same tools used by professionals currently in analytics positions.

Our Admission Process

Step 1 → Apply

Submit your application. Share a bit about yourself and what's driving you to start a career in data science.

Step 2 - Admissions Assessment

Complete a short critical thinking and problem-solving assessment. This allows us to assess your aptitude for data.

Step 3 - Admissions Interview

Speak with an Admissions representative in a non-technical interview. This is an opportunity for us to get to know each other a little better. Nothing technical - just a friendly conversation.

Step 4 - Admissions Decision

Receive your acceptance decision from Admissions. This usually happens within 3 business days.

Step 5 → Prework

If accepted, you'll begin course pre-work to prepare for the first day of class. Our data courses pre-work consists of 20-40 hours of lessons and labs covering the basics of Python (including loops and functions), statistical measures such as central tendency and dispersion, and building data visualizations using matplotlib and seaborn.