Epython Lab
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Welcome to Epython Lab, where you can get resources to learn, one-on-one trainings on machine learning, business analytics, and Python, and solutions for business problems.

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๐˜ผ๐™„ ๐™„๐™จ ๐™๐™š๐™ซ๐™ค๐™ก๐™ช๐™ฉ๐™ž๐™ค๐™ฃ๐™–๐™ง๐™ฎ, ๐˜ฝ๐™ช๐™ฉ ๐˜ผ๐™ง๐™š ๐™’๐™š ๐™Š๐™ซ๐™š๐™ง๐™ก๐™ค๐™ค๐™ ๐™ž๐™ฃ๐™œ ๐™Œ๐™ช๐™–๐™ฃ๐™ฉ๐™ช๐™ข ๐˜พ๐™ค๐™ข๐™ฅ๐™ช๐™ฉ๐™ž๐™ฃ๐™œ?
In the tech world, discussions of Artificial Intelligence dominate the stageโ€”and rightly so. AI has transformed industries, revolutionized how we work, and opened the door to possibilities once thought unattainable.
But hereโ€™s a question for the experts: Are we paying enough attention to quantum computing?
Quantum computing isn't just a buzzword; it has the potential to supercharge AI by solving problems that classical computers canโ€™t handle in a practical timeframe. From optimizing complex systems to enabling breakthroughs in drug discovery and cryptography, the synergy between AI and quantum computing could redefine innovation.
Yet, in many discussions about AI, I rarely hear about how weโ€™re preparing for this convergence.
How do we ensure our AI models are ready to harness quantum power?
What are the ethical considerations as we bridge these two transformative technologies?
To those immersed in AI, have you explored the potential of quantum computing in your field? If not, why? Letโ€™s start a conversation about how these technologies can shape the futureโ€”together.

hashtag#AI hashtag#QuantumComputing hashtag#Innovation hashtag#FutureTech https://medium.com/@epythonlab/whats-next-after-ai-the-emerging-frontiers-of-technology-822c73b9c7c9
15 ๐˜ฝ๐™š๐™จ๐™ฉ ๐™‹๐™ฎ๐™ฉ๐™๐™ค๐™ฃ ๐˜ผ๐™„/ ๐™ˆ๐™–๐™˜๐™๐™ž๐™ฃ๐™š ๐™‡๐™š๐™–๐™ง๐™ฃ๐™ž๐™ฃ๐™œ ๐™‹๐™ง๐™ค๐™Ÿ๐™š๐™˜๐™ฉ๐™จ ๐™ฉ๐™ค ๐˜ฝ๐™ค๐™ค๐™จ๐™ฉ ๐™”๐™ค๐™ช๐™ง ๐™Ž๐™ ๐™ž๐™ก๐™ก๐™จ https://medium.com/p/96677345b57d
๐Ÿ‘2
๐Ÿ“ข๐——๐—ฎ๐˜† ๐Ÿฎ๐Ÿญ/๐Ÿญ๐Ÿฌ๐Ÿฌ: ๐—ง๐—ฟ๐—ฎ๐—ถ๐—ป๐—ถ๐—ป๐—ด ๐—”๐—บ๐—ต๐—ฎ๐—ฟ๐—ถ๐—ฐ ๐—ก๐—˜๐—ฅ ๐— ๐—ผ๐—ฑ๐—ฒ๐—น๐˜€

I fine-tuned models on 27,989 labeled examples, optimizing key parameters:

- Learning rate: Experimented to find the sweet spot.

- Batch size: Limited to 16 to manage memory constraints.

- Metrics: Focused on precision, recall, and F1-score.



๐Ÿ’ก Finding: Smaller batches helped balance performance and computational efficiency.

๐Ÿ’ก Question: How do you optimize parameters for low-resource NLP tasks?

#AI #ModelTraining #Ethiopia #NLP
๐Ÿ“ข๐˜ฟ๐™–๐™ฎ 22/100: ๐™๐™๐™š ๐™‘๐™–๐™ก๐™ช๐™š ๐™ค๐™› ๐˜พ๐™š๐™ฃ๐™ฉ๐™ง๐™–๐™ก๐™ž๐™ฏ๐™š๐™™ ๐˜ฟ๐™–๐™ฉ๐™–

Why is centralizing e-commerce data critical for Ethiopia?



- For vendors: Better visibility and reach.

- For customers: Streamlined product discovery.

- For analytics: Real-time insights into market trends.



๐Ÿ’ก Question: What are the key challenges to centralizing data in emerging markets?

#ECommerce #DigitalTransformation #Ethiopia
๐ŸŒŸ ๐˜ฟ๐™–๐™ฎ 23/100: ๐™๐™ง๐™ช๐™ฉ๐™ ๐™ค๐™ง ๐™‡๐™ž๐™š: ๐™‰๐™–๐™ซ๐™ž๐™œ๐™–๐™ฉ๐™ž๐™ฃ๐™œ ๐™…๐™ค๐™— ๐™„๐™ฃ๐™ฉ๐™š๐™ง๐™ซ๐™ž๐™š๐™ฌ๐™จ ๐ŸŒŸ

This morning, I received an exciting email: "Interview Invitation: AI Python and .NET Developer."

While Iโ€™m proficient in AI Python and have tackled many projects, .NET isnโ€™t in my skill set. I faced a dilemma:

Exaggerate my expertise?
Or be honest about my strengths and gaps?
I chose truth. I emphasized my Python expertise and willingness to learn .NET.

๐Ÿ’ก Lesson: Honesty builds trust and keeps doors open for the right opportunities.

Have you faced a similar situation? Letโ€™s discuss in the comments! ๐Ÿ™Œ
Forwarded from Epython Lab
I am excited to share with you the Python Programming for Beginners roadmap

Basic Python Programming: https://youtu.be/ISv6XIl1hn0

Data Structures with Projects full tutorial for beginners
https://www.youtube.com/watch?v=lbdKQI8Jsok

OOP in Python - beginners Crash Course https://www.youtube.com/watch?v=I7z6i1QTdsw

Join #epythonlab https://t.me/epythonlab

Join https://t.me/epythonlab for more learning resources
โค6
๐ŸŒŸ๐˜ฟ๐™–๐™ฎ 24/100: ๐™‰๐™š๐™ญ๐™ฉ ๐™Ž๐™ฉ๐™š๐™ฅ๐™จ ๐™›๐™ค๐™ง ๐˜พ๐™š๐™ฃ๐™ฉ๐™ง๐™–๐™ก๐™ž๐™ฏ๐™š๐™™ ๐™€-๐™˜๐™ค๐™ข๐™ข๐™š๐™ง๐™˜๐™š๐ŸŒŸ



I'm moving closer to deploying a centralized e-commerce platform for Ethiopia.



Next steps:

1๏ธโƒฃ Integrating XLM-Roberta for real-time entity extraction.

2๏ธโƒฃ Expanding the dataset for even better performance.

3๏ธโƒฃ Collaborating with vendors to enrich product listings.



๐Ÿ’ก Takeaway: NLP-driven platforms like central e-commerce can redefine how e-commerce works in Ethiopia.



๐Ÿ’ก Discussion: How can we scale similar platforms for other underrepresented markets?

#AI #ECommerce #FintechAfrica #Amharic
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๐ŸŒŸ๐˜ฟ๐™–๐™ฎ 25/100: ๐™๐™ฃ๐™™๐™š๐™ง๐™จ๐™ฉ๐™–๐™ฃ๐™™๐™ž๐™ฃ๐™œ ๐™€๐™ฉ๐™๐™ž๐™ค๐™ฅ๐™ž๐™–๐™ฃ ๐™๐™ž๐™ฃ๐™ฉ๐™š๐™˜๐™ ๐ŸŒŸ



Ethiopia's fintech ecosystem is a mix of challenges and opportunities. ๐Ÿ“ˆ๐ŸŒ

From low formal banking penetration to an increasingly digital population, itโ€™s clear that innovation in financial services is critical.



Key insights from my research today:

- Low banking penetration but high mobile adoption: Over 75% of transactions are cash-based, yet mobile payment systems like Telebirr are gaining traction.

- Regulatory frameworks: Ethiopiaโ€™s regulatory approach emphasizes financial inclusion but poses innovation challenges, especially for Buy-Now-Pay-Later services.

- Unique consumer behaviors: The dominance of informal financial systems and cash reliance shapes how Ethiopians engage with digital financial services.



๐Ÿ’ก Question of the day: How can fintech drive financial literacy in Ethiopia to accelerate digital adoption?



#FintechAfrica #Ethiopia #Buy-Now-Pay-Later #FinancialLiteracy #DigitalTransformation
HAPPY NEW YEARS! MAY ALL YOUR DREAMS COME TRUE FOR 2025!
โค6๐Ÿ‘4
Forwarded from Epython Lab
๐Ÿ POP QUIZ | Day 5: What type of module is the OS in Python?

Learn about handling files using the OS module in Python https://youtu.be/aWB3Ubb1UV0

@epythonlab #popquiz
Forwarded from Epython Lab
๐Ÿ The most essential skill you must have as a Python Developer/Data Analyst/Data Scientist is membership operators.

In this short tutorial, you will learn about how to use membership operators in real world application.

๐Ÿ‘‰๐Ÿ‘‰https://youtu.be/Us-iQ_HbIrk

Subscribe, like, share ๐Ÿ™
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Forwarded from Epython Lab
๐Ÿ As we know Python is dynamically typed programming language. We do not need to statically define the type of data before we use it like other programming language such as c++ and java.

However, we could also statically indicate type hint for variables. You can learn here ๐Ÿ‘‰๐Ÿ‘‰https://youtu.be/x5wM5M6MJbU