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Excited to share a new milestone!
I’m thrilled to announce that I have successfully completed the AI & Machine Learning track at AMIT Learning with a grade of 99%! ✨️
Throughout this journey, I’ve focused on bridging the gap between theoretical knowledge and real-world application, applying these concepts directly to hands-on projects like my current Traffic Congestion Analysis using Python, Pandas, and advanced data cleaning techniques.
A big thank you to Maria Roshdy and the entire team at AMIT Learning for the valuable guidance and great learning experience. Looking forward to the next steps into predictive modeling and AI solutions!
#ArtificialIntelligence #MachineLearning #DataScience #AMIT #Python #DataAnalytics
The network for creativity
Join 1.25M professional creatives like you
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Creatives on Contra have earned over $150M and we are just getting started
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Congratulations, Divan 🎉
Hey! I just shared a write-up on something I learned while rebuilding a product with AI agents:)
The main idea is pretty simple:
Don’t put all your product context in one giant agent file.
Keep the main file small, and put the real product knowledge inside structured docs that the AI can navigate when it needs them.
This has helped me a lot when working on a SaaS product with a lot of business logic!
Sharing the full breakdown here:
I'm offering consulting services to set this up! :)
The table-of-contents-in-the-main-file pattern is the key move here - it keeps the agent's default context small while still giving it a map to go fetch the architecture or naming-convention doc only when the task actually touches that domain.
Python-based exploratory data analysis of breast cancer patient data, using Pandas, Matplotlib, and statistical methods to identify patterns in age, BMI, cancer stage, incidence, and mortality.
