Learn Skills to be an AI Developer

While we can't guarantee job placement in remote ai jobs, participating companies are on the lookout for exceptional AI professionals and might offer positions to students who successfully complete the cohort. These companies will selectively choose the most outstanding participants for interviews.

The time commitment varies among participants, but generally most participants commit to 1-2 hours per day on weekdays. Some dedicate additional time on weekends (up to 4-6 hours). The minimum recommended time is about 4 hours per week.

Our AI program curriculum ensures you gain both theoretical knowledge and practical skills:

• Machine Learning Fundamentals

  • Supervised, unsupervised, and reinforcement learning

• • Introduction to MLOps: Containers, Experiment Tracking, Model Registry, and Workflow Orchestration

• Neural Networks and Deep Learning

• • Introduction to Neural Networks

  • Deep dive into advanced neural network concepts

• Computer Vision

• • Image processing and recognition techniques

  • Practical applications using cloud computing (AWS, GCP, Azure)

• Foundation Models and Transfer Learning

• • Understanding and leveraging pre-trained models

  • Fine-tuning techniques for specific applications

• Natural Language Processing (NLP) and Large Language Models (LLMs)

• • Core NLP concepts and techniques

  • Exploration of state-of-the-art LLMs

• Retrieval-Augmented Generation (RAG)

• • Advanced techniques for enhancing AI-generated content with external knowledge

• Cloud Computing for Machine Learning

• • Introduction to cloud platforms (AWS, GCP, Azure)

  • Deploying ML models in cloud environments

• Advanced MLOps

• • Infrastructure as Code (IaC)

  • Continuous Integration and Continuous Deployment (CI/CD) for ML projects

Throughout the program, you’ll apply these concepts in hands-on projects, including building a Flask app for regression/classification, developing a computer vision app on AWS, creating a transfer learning application, and designing a RAG-based Streamlit app.

The program employs a mix of learning methods to cater to different learning styles:

• Hands-on, project-based learning
• Theoretical foundations
• One-on-one mentoring sessions
• Self-paced study with provided resources
• Optional group sessions and discussions

While prior AI experience is beneficial, it's not mandatory. The program is designed to accommodate participants with various levels of AI knowledge, from beginners to those with some exposure to AI concepts.

The work-study model allows you to pay for the program through work. You make an initial deposit of $500 and then work on real AI projects to cover the remaining tuition. This typically takes about 3 months.

Yes, we offer a full upfront payment option. With this option, you can get involved in paid projects during the program if you want.