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

📢Day 7/100: Tackling Data Availability in Ethiopia

Building a credit scoring model in Ethiopia has challenges, especially regarding data. 📊

Key hurdles I’m exploring:

1️⃣ Data scarcity: Formal credit histories are rare, but eCommerce and mobile money data offer untapped potential.

2️⃣ Local partnerships: Collaborating with fintechs to access anonymized transaction data.

3️⃣ Privacy compliance: Ensuring data protection laws are adhered to while innovating responsibly.

💡 Question of the day: Are there alternative sources of financial data that have worked well in other emerging markets?

#DataChallenges #Ethiopia #FintechInnovation #AlternativeData #PrivacyByDesign

📢Day 8/100: Sketching My Credit Scoring Workflow

Today, I outlined the workflow for my credit scoring model. Here’s what it looks like:

1️⃣ Data collection: Leveraging transaction histories, behavioral metrics, and alternative data sources.

2️⃣ Feature engineering: Creating features like transaction recency, frequency, and value tailored to BNPL behavior.

3️⃣ Model selection: Comparing Gradient Boosting, Random Forest, and Logistic Regression.

4️⃣ Evaluation: Balancing precision, recall, and ROC-AUC to ensure the model is reliable in the Ethiopian context.

💡 Tips needed: What’s your go-to feature engineering strategy for financial datasets?

#AI #CreditScoring #ModelDevelopment #BNPL #DataScienceEthiopia

📢Day 9/100: Feature Engineering Deep Dive
Feature engineering is where raw data turns into actionable insights! 🛠
In my credit scoring project, key features include:
1️⃣ Recency, Frequency, Monetary (RFM): Critical for understanding customer behavior.
2️⃣ Fraud indicators: High-value transactions flagged based on outlier analysis.
3️⃣ Categorical encodings: Using Weight of Evidence (WoE) to transform qualitative data like product categories.
💡 Takeaway: Good features are the foundation of any successful model. They ensure the patterns we observe are meaningful and actionable.
💡 Discussion point: What’s your go-to method for handling highly skewed data in financial datasets?
#FeatureEngineering #DataScience #CreditScoring #FintechEthiopia

📢Day 10/100: Class Imbalance Challenges
Class imbalance is a persistent issue in fraud detection and credit scoring. 🚨

In my dataset:

Fraudulent transactions are rare (<5%), making prediction tricky.
Techniques like SMOTE (Synthetic Minority Oversampling Technique) helped balance the dataset.
💡 Key Insight: Balancing the data improved model precision for rare classes like fraud detection.

💡 Question: What other methods do you use to address class imbalance without oversampling?

#DataChallenges #FraudDetection #CreditScoring #FintechInnovation

📢Day 11/100: Integrating AI and ML in Credit Scoring

AI and machine learning are at the heart of my credit scoring model, but they require careful application. 🤖

Today’s focus:

1️⃣ Modeling approaches: Exploring supervised learning techniques like Gradient Boosting for risk prediction.

2️⃣ Bias mitigation: Addressing imbalances in transactional data to ensure fair outcomes.

3️⃣ Explainability: Building a model that’s transparent and interpretable to meet regulatory standards.

💡 Coming soon: Detailed performance metrics and insights from my initial experiments with AI-powered credit scoring!

#AI #MachineLearning #CreditScoring #ExplainableAI #FintechEthiopia

📢Day 12/100: Comparing Machine Learning Models

Today, I compared the performance of multiple machine learning models for credit scoring:

1️⃣ Logistic Regression: Simple and interpretable but less effective with complex data.

2️⃣ Random Forest: Excellent for feature importance but slower for large datasets.

3️⃣ Gradient Boosting: Best overall performance with high accuracy and recall.

💡 Finding: Gradient Boosting stood out with an ROC-AUC of 0.97.

💡 Question: Do you prioritize interpretability or accuracy when selecting a model for financial applications?

#MachineLearning #ModelSelection #CreditScoring #FintechEthiopia

Build a self-evolving Genetic Algorithm
https://youtu.be/9M4ETVngWy4

📢Day 13/100: Real-World Prototype Deployment

The prototype for my credit scoring model is live! 🚀

Features:

1️⃣ Web dashboard: Enter customer details and get real-time risk classifications.

2️⃣ API integration: Seamless communication between the frontend and back end.

3️⃣ Explainable results: Each score is accompanied by a breakdown of contributing factors.

💡 Takeaway: Deploying a functional prototype provides valuable feedback for real-world usability.

💡 Question: How do you ensure user-friendly designs for fintech tools in emerging markets?

#Prototype #AI #FintechEthiopia #CreditScoring

📢Day 14/100: Next Steps for the Credit Scoring Model

With the prototype complete, here’s what’s next:

1️⃣ Testing with real-world data: Partnering with fintechs to validate the model.

2️⃣ Incorporating mobile money data: Adding another dimension to the scoring process.

3️⃣ Monitoring and retraining: Ensuring the model stays relevant as new data comes in.

💡 Takeaway: A successful model is never truly done—it evolves with the market.

💡 Question: What’s your approach to maintaining machine learning models in production?

#CreditScoring #MachineLearning #FintechEthiopia #AI

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📢Day 15/100: The Rise of Telegram E-Commerce in Ethiopia

Telegram is transforming e-commerce in Ethiopia, but its fragmented nature poses challenges. Vendors operate in silos, and customers struggle to navigate multiple channels.



EthioMart's Vision:



We aim to create a centralized platform aggregating data from Telegram channels, simplifying product discovery for customers and enhancing visibility for vendors.



💡 Question of the day: How can centralized platforms improve Ethiopia’s digital shopping experience?





#Ethiopia #ECommerce #DigitalTransformation #Telegram #FintechInnovation

📢Day 16/100: Tackling Amharic NLP Challenges

Amharic presents unique challenges in natural language processing (NLP), from its complex script to a lack of annotated datasets.



My approach: Fine-tune Large Language Models (LLMs) for Amharic Named Entity Recognition (NER) to extract product names, prices, and locations from Telegram messages.



💡 Discussion: What strategies can we adopt to make NLP more accessible for low-resource languages like Amharic?

#NLP #AI #Amharic #FintechEthiopia

📢Day 17/100: From Data to Insights



My journey started with collecting and cleaning data from Telegram channels, a hub for Ethiopian e-commerce.



Key steps:

1️⃣ Scraping Telegram messages to capture product details.

2️⃣ Preprocessing Amharic text to handle non-text characters and normalize content.

3️⃣ Tokenizing text for labeling.



💡 Takeaway: High-quality data preparation is the backbone of effective machine learning models.


#DataScience #AmharicNLP #FintechEthiopia

📢Day 18/100: Labeling Amharic Text for NER

Labeling Amharic text for Named Entity Recognition is no small task.

Our algorithm identifies:

Prices using patterns like "ብር" (currency).

Locations from a predefined list.

Products through contextual analysis.

💡 Example: "ዋጋ 4800 ብር" -> "B-PRICE I-PRICE I-PRICE"

💡 Discussion: How can we simplify labeling entities in low-resource languages?

#NER #Amharic #DataLabeling #Ethiopia

Python Data Structures for absolute beginners with Project
https://www.youtube.com/watch?v=lbdKQI8Jsok

📢Day 19/100: Choosing the Right Language Model

For Amharic Named Entity Recognition, we fine-tuned three models:

1️⃣ XLM-Roberta: Best for multilingual NLP.

2️⃣ mBERT: Balanced performance.

3️⃣ DistilBERT: Lightweight but slightly less accurate.

💡 Insight: XLM-Roberta outperformed others in accuracy and entity recognition for Amharic e-commerce data.

💡 Question: What’s your experience with fine-tuning NLP models for underrepresented languages?

#AI #NLP #ModelSelection #FintechAfrica

📢Day 20/100: Overcoming Tokenization Challenges
Tokenization is critical for NLP tasks like Named Entity Recognition.

Key steps:
1️⃣ Aligning tokens with Amharic text.
2️⃣ Preserving the relationship between tokens and their labels.
3️⃣ Using model-specific tokenizers (XLM-Roberta, mBERT).

💡 Takeaway: Tokenization errors can significantly impact the accuracy of entity recognition models.

#AI #Tokenization #AmharicNLP #FintechInnovation

𝘼𝙄 𝙄𝙨 𝙍𝙚𝙫𝙤𝙡𝙪𝙩𝙞𝙤𝙣𝙖𝙧𝙮, 𝘽𝙪𝙩 𝘼𝙧𝙚 𝙒𝙚 𝙊𝙫𝙚𝙧𝙡𝙤𝙤𝙠𝙞𝙣𝙜 𝙌𝙪𝙖𝙣𝙩𝙪𝙢 𝘾𝙤𝙢𝙥𝙪𝙩𝙞𝙣𝙜?
In the tech world, discussions of Artificial Intelligence dominate the stage—and rightly so. AI has transformed industries, revolutionized how we work, and opened the door to possibilities once thought unattainable.
But here’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 can’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 we’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? Let’s start a conversation about how these technologies can shape the future—together.

hashtag#AI hashtag#QuantumComputing hashtag#Innovation hashtag#FutureTech https://medium.com/@epythonlab/whats-next-after-ai-the-emerging-frontiers-of-technology-822c73b9c7c9

15 𝘽𝙚𝙨𝙩 𝙋𝙮𝙩𝙝𝙤𝙣 𝘼𝙄/ 𝙈𝙖𝙘𝙝𝙞𝙣𝙚 𝙇𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝙋𝙧𝙤𝙟𝙚𝙘𝙩𝙨 𝙩𝙤 𝘽𝙤𝙤𝙨𝙩 𝙔𝙤𝙪𝙧 𝙎𝙠𝙞𝙡𝙡𝙨 https://medium.com/p/96677345b57d

📢𝗗𝗮𝘆 𝟮𝟭/𝟭𝟬𝟬: 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗔𝗺𝗵𝗮𝗿𝗶𝗰 𝗡𝗘𝗥 𝗠𝗼𝗱𝗲𝗹𝘀

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.



💡 Finding: Smaller batches helped balance performance and computational efficiency.

💡 Question: How do you optimize parameters for low-resource NLP tasks?

#AI #ModelTraining #Ethiopia #NLP