A collection of all the assignments and projects I completed during Technion's AI Accelerator program (see Education section), including forecasting, NLP, data analysis, and computer vision pipelines.

AI Accelerator Portfolio | Daniel Dekhtyar
Project Case Study

Technion AI Accelerator Portfolio

A collection of all the assignments and projects I completed during Technion’s AI Accelerator program (see Education section), including forecasting, NLP, data analysis, and computer vision pipelines.

Technion logo for the AI Accelerator program
September 2024 – April 2025 Role: Lead ML Engineer (Technion Cohort) Institution: Technion – Israel Institute of Technology Stack: Python, TensorFlow, Pandas, Airflow

Overview

The rigorous 8-month program, offered by the Technion – a global leader in AI research and education – develops high-caliber machine learning engineers equipped to tackle complex industry challenges. The course combines academic excellence with hands-on practice, fostering critical thinking, analytical abilities, and problem-solving skills. Students gain expertise in the latest AI tools, techniques, and technologies leading professionals use worldwide.

The program includes 670 academic hours with 11 modules.

Students develop a practical end-to-end AI application using Python, leveraging datasets (SQL and NoSQL) and applying ML and Deep learning (DL) algorithms to generate actionable insights. This project-oriented approach ensures graduates are prepared to integrate into dynamic AI roles.

The program culminates in a practical end-to-end project, where students develop AI solutions using Python and work with SQL and NoSQL databases, applying insights with various AI/ML/DL algorithms.

Things I learned

TensorFlow PyTorch scikit-learn matplotlib Pandas PostgreSQL Computer Vision Convolutional Neural Networks Recurrent Neural Networks

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