Consistency is the real game-changer in learning to code.
You don’t need 10 hours a day.
You just need one focused hour, every day.
Whether you're just starting with Python, diving into machine learning, or building your first web app, the secret to growth isn’t in the intensity—it’s in the consistency.
I've seen firsthand (both personally and through mentoring others) that those who commit to steady, incremental progress often surpass those who rely on occasional bursts of effort.
Make it a habit. Show up every day.
Even on the days when it feels hard. Especially on those days.
Progress compounds—and that’s how coders are made.
Resources to Learn
01: Introduction to Python: https://youtu.be/9nkITaOCx_U
02: How to Get Started with Python in VS Code: https://youtu.be/EGdhnSEWKok
#Coding #Python #LearnToCode #DeveloperJourney #Consistency #GrowthMindset #TechCareers
You don’t need 10 hours a day.
You just need one focused hour, every day.
Whether you're just starting with Python, diving into machine learning, or building your first web app, the secret to growth isn’t in the intensity—it’s in the consistency.
I've seen firsthand (both personally and through mentoring others) that those who commit to steady, incremental progress often surpass those who rely on occasional bursts of effort.
Make it a habit. Show up every day.
Even on the days when it feels hard. Especially on those days.
Progress compounds—and that’s how coders are made.
Resources to Learn
01: Introduction to Python: https://youtu.be/9nkITaOCx_U
02: How to Get Started with Python in VS Code: https://youtu.be/EGdhnSEWKok
#Coding #Python #LearnToCode #DeveloperJourney #Consistency #GrowthMindset #TechCareers
How do you write comments in Python | Python Tutorial for New Coding Learner
https://youtu.be/BWxIMRvZdtM
https://youtu.be/BWxIMRvZdtM
YouTube
Python for Beginners | How To Code in Python 3 | How to Write Comments
Learn how to write effective comments in Python to improve code readability and maintainability. In this tutorial, you'll explore the difference between single-line and multi-line comments, see real-time examples, and understand best practices for making…
🚀 Model Comparison for Loan Classification
4 years ago, I built and compared several classification models to predict loan applicants as Creditworthy or Non-Creditworthy. After performing data cleansing, handling missing values, and tuning parameters, I evaluated the models using precision, recall, and F1-score.
🔍 The Random Forest Classifier stood out with an AUC of 80% and an accuracy of 79%, successfully classifying 418 loans as Creditworthy and 82 as Non-Creditworthy.
Looking back, it's been a great learning experience, and I encourage exploring different tuning parameters and cross-validation techniques to improve model performance even further.
Check out the full source code on GitHub! 💻
https://medium.com/@epythonlab/best-practices-of-classification-models-towards-predicting-loan-type-c510d9b0dff6
4 years ago, I built and compared several classification models to predict loan applicants as Creditworthy or Non-Creditworthy. After performing data cleansing, handling missing values, and tuning parameters, I evaluated the models using precision, recall, and F1-score.
🔍 The Random Forest Classifier stood out with an AUC of 80% and an accuracy of 79%, successfully classifying 418 loans as Creditworthy and 82 as Non-Creditworthy.
Looking back, it's been a great learning experience, and I encourage exploring different tuning parameters and cross-validation techniques to improve model performance even further.
Check out the full source code on GitHub! 💻
https://medium.com/@epythonlab/best-practices-of-classification-models-towards-predicting-loan-type-c510d9b0dff6
Medium
Best Practices for Classification Models in Predicting Loan Types
In this article, I have outlined the best practices for machine learning models that predict loan types (Creditworthy or Non-Creditworthy)…
Debugging and Troubleshooting in Python: A Developer’s Essential Guide
Debugging and troubleshooting are essential skills for any Python developer. While these tasks can be frustrating, they are a necessary part of the software development process. Proper debugging helps developers identify the root cause of issues and ensures smoother project delivery.
In this article, you will explore common debugging challenges, essential techniques, and how you can improve your debugging efficiency with Python. Whether you’re a beginner or an experienced developer, mastering debugging techniques will save you countless hours of frustration.
https://medium.com/@epythonlab/debugging-and-troubleshooting-in-python-a-developers-essential-guide-b3415f53b1e0
Debugging and troubleshooting are essential skills for any Python developer. While these tasks can be frustrating, they are a necessary part of the software development process. Proper debugging helps developers identify the root cause of issues and ensures smoother project delivery.
In this article, you will explore common debugging challenges, essential techniques, and how you can improve your debugging efficiency with Python. Whether you’re a beginner or an experienced developer, mastering debugging techniques will save you countless hours of frustration.
https://medium.com/@epythonlab/debugging-and-troubleshooting-in-python-a-developers-essential-guide-b3415f53b1e0
Medium
Debugging and Troubleshooting in Python: A Developer’s Essential Guide
Debugging and troubleshooting are essential skills for any Python developer. While these tasks can be frustrating, they are a necessary…
Understanding what kind of Data you should store in computer memory
https://youtu.be/1UN_iU4UGho
https://youtu.be/1UN_iU4UGho
YouTube
Python for Beginners | What are Python Data Types
Learn the essential built-in data types in Python and how they work. In this beginner-friendly tutorial, we cover integers, floating-point numbers (floats), Booleans, and strings with simple examples and clear explanations. Mastering data types is one of…
Python for Beginners | How to Code in Python | How to Store and Access Data with Variables in Python
https://youtu.be/yeRbfdvfWnU
https://youtu.be/yeRbfdvfWnU
YouTube
Python for Beginners | How to Store and Access Data with Variables in Python
🎓 Learn Python Variables with Real-World Examples
Welcome to your next step in Python programming! In this beginner-friendly tutorial, you’ll learn what variables are, how Python stores data in memory, and how to use variables in real projects like user…
Welcome to your next step in Python programming! In this beginner-friendly tutorial, you’ll learn what variables are, how Python stores data in memory, and how to use variables in real projects like user…
Do you know how Python Executes your code? https://www.youtube.com/watch?v=az-7vPbfGYc
YouTube
Python for Beginners | How Python Executes Code
Understand how Python runs your code behind the scenes.
In this tutorial, we explain Python's execution process step-by-step: from how the interpreter reads your code, how Python compiles it into bytecode, and how the Python Virtual Machine (PVM) executes…
In this tutorial, we explain Python's execution process step-by-step: from how the interpreter reads your code, how Python compiles it into bytecode, and how the Python Virtual Machine (PVM) executes…
Python for Beginners | How to Work with Strings in Python | Create, Combine, Repeat, Store
https://youtu.be/vEhUfeT1ar4
https://youtu.be/vEhUfeT1ar4
YouTube
Python for Beginners | How to Work with Strings in Python | Create, Combine, Repeat, Store
Strings are one of the most important data types in Python, and understanding how to work with them is essential for every programmer.
In this detailed and beginner-friendly tutorial, you will learn:
• How to create and print strings
• How to concatenate…
In this detailed and beginner-friendly tutorial, you will learn:
• How to create and print strings
• How to concatenate…
🎯 Want to break into FinTech with Python and machine learning?
I just launched the FinTech ML Labs video series — a practical guide to building real-world financial systems using Python and modern ML libraries.
📌 Episode 1 is live:
"Build FinTech Machine Learning Projects with Python: Intro to FinTech ML"
Inside this episode:
What FinTech ML really is (and why it's in demand)
5 real-world ML applications: fraud detection, credit scoring, trading bots & more
How companies like Stripe, PayPal, and Robinhood use ML at scale
Tools we’ll use: Python, scikit-learn, XGBoost, spaCy, Hugging Face Transformers
💡 Every episode includes code, datasets, and walkthroughs so you can follow along.
🔗 Watch now: https://youtu.be/dy87uyYQWrg
If you’re a developer looking to build applied ML skills or transition into FinTech, this series is for you.
Let’s build real systems — not just toy models.
I just launched the FinTech ML Labs video series — a practical guide to building real-world financial systems using Python and modern ML libraries.
📌 Episode 1 is live:
"Build FinTech Machine Learning Projects with Python: Intro to FinTech ML"
Inside this episode:
What FinTech ML really is (and why it's in demand)
5 real-world ML applications: fraud detection, credit scoring, trading bots & more
How companies like Stripe, PayPal, and Robinhood use ML at scale
Tools we’ll use: Python, scikit-learn, XGBoost, spaCy, Hugging Face Transformers
💡 Every episode includes code, datasets, and walkthroughs so you can follow along.
🔗 Watch now: https://youtu.be/dy87uyYQWrg
If you’re a developer looking to build applied ML skills or transition into FinTech, this series is for you.
Let’s build real systems — not just toy models.
How to format Text in Python
https://youtu.be/Qs5Jtaxl7Lc
https://youtu.be/Qs5Jtaxl7Lc
YouTube
Python for Beginners | How to Format Text in Python
Learn how to format text in Python the right way. This tutorial covers everything from handling quotes and apostrophes to using escape characters, line breaks, tabs, and multi-line strings. Whether you are building a chatbot, generating clean output for users…
🚀 New Tutorial: Build a Credit Scoring Model in Python
🎯 Real-World FinTech Machine Learning Project – Episode 2: Watch the full tutorial here https://youtu.be/pWOoYpJsaDc
I have published a practical tutorial that demonstrates how to build a credit scoring model using Python, pandas, and scikit-learn. This project simulates a real-life use case from the fintech industry, focusing on predicting loan defaults based on applicant data.
📌 What you will learn:
Data cleaning and preprocessing for financial datasets
Logistic Regression for binary classification
Feature scaling and performance metrics (Precision, Recall, F1 Score)
Visualizing feature importance for interpretability
📊 Why this matters:
Credit scoring is a core component in lending, digital banking, and microfinance. Understanding how to implement this model can open doors in risk analytics, credit platforms, and fintech applications.
🔗 GitHub code and dataset are also available in the video description.
If you are building a career in data science, machine learning, or fintech, this project will give you strong, applicable experience.
🎯 Real-World FinTech Machine Learning Project – Episode 2: Watch the full tutorial here https://youtu.be/pWOoYpJsaDc
I have published a practical tutorial that demonstrates how to build a credit scoring model using Python, pandas, and scikit-learn. This project simulates a real-life use case from the fintech industry, focusing on predicting loan defaults based on applicant data.
📌 What you will learn:
Data cleaning and preprocessing for financial datasets
Logistic Regression for binary classification
Feature scaling and performance metrics (Precision, Recall, F1 Score)
Visualizing feature importance for interpretability
📊 Why this matters:
Credit scoring is a core component in lending, digital banking, and microfinance. Understanding how to implement this model can open doors in risk analytics, credit platforms, and fintech applications.
🔗 GitHub code and dataset are also available in the video description.
If you are building a career in data science, machine learning, or fintech, this project will give you strong, applicable experience.
YouTube
Build a Credit Scoring Model with Python | FinTech ML Project for Beginners
📘 Build a Credit Scoring Model with Python – A FinTech ML Labs Tutorial
This comprehensive, hands-on tutorial will guide you through building a credit scoring model using Python, pandas, and scikit-learn. You'll learn how to prepare and preprocess real-world…
This comprehensive, hands-on tutorial will guide you through building a credit scoring model using Python, pandas, and scikit-learn. You'll learn how to prepare and preprocess real-world…
String methods in Python: A comprehensive tutorial for beginners
https://youtu.be/9gniK8C6va0
https://youtu.be/9gniK8C6va0
YouTube
Python for Beginners | Learn String Methods in Python: A Comprehensive Tutorial
Unlock the power of Python string functions in this comprehensive tutorial. Whether you're a beginner or looking to refresh your skills, this video covers essential methods like join(), split(), replace(), len(), strip(), and more. Learn how to manipulate…
How do you interpret the insights of the loan dataset distribution plot
Github https://github.com/epythonlab.com2/fintech-ml-labs/blob/main/notebooks%2Fcredit_scoring_model.ipynb😃
Github https://github.com/epythonlab.com2/fintech-ml-labs/blob/main/notebooks%2Fcredit_scoring_model.ipynb😃
How to Index and Slicing Strings: A comprehensive Beginners Tutorial
https://www.youtube.com/watch?v=K-488Zr3Fe0
https://www.youtube.com/watch?v=K-488Zr3Fe0
YouTube
Python for Beginners | Python String Slicing Mastery: Indexing, Steps & Reversing Explained Simply
Unlock the full power of Python string slicing in this complete guide!
This tutorial teaches everything from basic string indexing to advanced slicing with steps and negative strides. Whether you are a beginner or revisiting the concept, this video makes…
This tutorial teaches everything from basic string indexing to advanced slicing with steps and negative strides. Whether you are a beginner or revisiting the concept, this video makes…
Forwarded from Epython Lab
ETL Process Pipeline with Python: https://youtu.be/3J1D33US7NM
Test ETL Pipeline: https://youtu.be/78x6V5q34qs
Test ETL Pipeline: https://youtu.be/78x6V5q34qs
🚀 Launching: ML for FinTech Projects – Real-World Implementations for ML Enthusiasts
I am excited to launch a practical, hands-on series dedicated to Machine Learning in FinTech. This initiative is designed for ML enthusiasts and professionals eager to explore real-world implementations of machine learning in financial systems.
In this series, you will learn step-by-step how to build and deploy FinTech solutions, including:
✅ Credit Scoring Models https://youtu.be/pWOoYpJsaDc
✅ Fraud Detection Systems
✅ Loan Default Predictions https://youtu.be/pWOoYpJsaDc
✅ Customer Segmentation
✅ Transaction Risk Analysis
...and much more.
Each episode will include:
🔹 Clear explanations of ML techniques in a FinTech context
🔹 Real datasets and coding walkthroughs
🔹 End-to-end project structure from data prep to model deployment
Stay tuned, subscribe, and get ready to build solutions that make a real impact.
I am excited to launch a practical, hands-on series dedicated to Machine Learning in FinTech. This initiative is designed for ML enthusiasts and professionals eager to explore real-world implementations of machine learning in financial systems.
In this series, you will learn step-by-step how to build and deploy FinTech solutions, including:
✅ Credit Scoring Models https://youtu.be/pWOoYpJsaDc
✅ Fraud Detection Systems
✅ Loan Default Predictions https://youtu.be/pWOoYpJsaDc
✅ Customer Segmentation
✅ Transaction Risk Analysis
...and much more.
Each episode will include:
🔹 Clear explanations of ML techniques in a FinTech context
🔹 Real datasets and coding walkthroughs
🔹 End-to-end project structure from data prep to model deployment
Stay tuned, subscribe, and get ready to build solutions that make a real impact.
Avoid Type Error Master Python Data Type Conversion FAST | Type Conversion Tutorial
https://youtu.be/ovmjYmU8Jrc
https://youtu.be/ovmjYmU8Jrc
YouTube
Python for Beginners | Master Python Data Type Conversion FAST | Type Conversion Tutorial
Unlock the power of Python data type conversion and stop wasting time on avoidable type errors. In this hands-on Python tutorial, you will learn how to convert between strings, integers, floats, lists, and tuples like a pro—with real-world examples and a…
➡️ Beginner's Guide to Python Programming: https://youtube.com/playlist?list=PL0nX4ZoMtjYGSy-rn7-JKt0XMwKBpxyoE&si=N8rHxnIYnZvF-WBz
This tutorial is designed for absolute beginners, with no prior experience required. Learn the basics, build real projects, and confidently grow your skills.
🔔 Subscribe for more learning resources and updates!
This tutorial is designed for absolute beginners, with no prior experience required. Learn the basics, build real projects, and confidently grow your skills.
🔔 Subscribe for more learning resources and updates!
YouTube
Python from Zero to Hero | Python for Beginners | How to Learn Python in VSCode
Welcome to your ultimate Python learning path — Python from Zero to Hero!This playlist is designed for absolute beginners who want to master Python programmi...
🚨 Fraud Isn’t Just a Risk—It’s a Reality. Here’s How We’re Fighting Back with ML in Fintech. 💡https://youtu.be/kQHpXSH4G_E
In the fast-moving world of fintech, trust is currency. And nothing erodes trust faster than fraud.
Recently, I took a deep dive into building a fraud detection engine using classification algorithms in Python—but not just with the traditional plug-and-play mindset.
Instead of asking “Which model performs best?”, I asked: 🔍 How can we build a system that understands fraud like a human analyst would—but at scale and in real time?
📊 Here's the approach:
1. Behavioral Pattern Recognition: Mapped transaction flows to user behavior signatures, not just features. Outliers aren’t always fraud—but often they are.
2. Hybrid Classification Stack: Instead of relying on one algorithm (e.g., Random Forest or Logistic Regression), I built a layered model that integrates explainable models with high-performance black-box learners.
3. Anomaly-Aware Sampling: Balanced class imbalance with strategic undersampling, but retained edge-case patterns using synthetic minority over-sampling (SMOTE with domain tweaks).
4. Real-World Feedback Loop: Built an active learning system that retrains from confirmed fraud cases—turning human analysts into model trainers.
🧠 The result? A system that doesn’t just flag suspicious activity—but learns from every incident.
🎯 Tools used:
Python, Scikit-learn, XGBoost
Pandas, Seaborn (for EDA)
SHAP (for interpretability)
Flask + Streamlit for dashboarding
💬 Fintech peers: How are you balancing accuracy vs explainability in fraud detection models?
Let’s connect if you’re working on ML in fintech—especially in risk, fraud, or anomaly detection. Happy to exchange ideas and build smarter, safer systems together. 🔐📈
#Fintech #MachineLearning #FraudDetection #Python #AI #Classification #DataScience #XAI #MLinFinance #CyberSecurity
In the fast-moving world of fintech, trust is currency. And nothing erodes trust faster than fraud.
Recently, I took a deep dive into building a fraud detection engine using classification algorithms in Python—but not just with the traditional plug-and-play mindset.
Instead of asking “Which model performs best?”, I asked: 🔍 How can we build a system that understands fraud like a human analyst would—but at scale and in real time?
📊 Here's the approach:
1. Behavioral Pattern Recognition: Mapped transaction flows to user behavior signatures, not just features. Outliers aren’t always fraud—but often they are.
2. Hybrid Classification Stack: Instead of relying on one algorithm (e.g., Random Forest or Logistic Regression), I built a layered model that integrates explainable models with high-performance black-box learners.
3. Anomaly-Aware Sampling: Balanced class imbalance with strategic undersampling, but retained edge-case patterns using synthetic minority over-sampling (SMOTE with domain tweaks).
4. Real-World Feedback Loop: Built an active learning system that retrains from confirmed fraud cases—turning human analysts into model trainers.
🧠 The result? A system that doesn’t just flag suspicious activity—but learns from every incident.
🎯 Tools used:
Python, Scikit-learn, XGBoost
Pandas, Seaborn (for EDA)
SHAP (for interpretability)
Flask + Streamlit for dashboarding
💬 Fintech peers: How are you balancing accuracy vs explainability in fraud detection models?
Let’s connect if you’re working on ML in fintech—especially in risk, fraud, or anomaly detection. Happy to exchange ideas and build smarter, safer systems together. 🔐📈
#Fintech #MachineLearning #FraudDetection #Python #AI #Classification #DataScience #XAI #MLinFinance #CyberSecurity
YouTube
Build a Fraud Detection with XGBoost in Python | ML FinTech Project for Beginners
Build a Fraud Detection System using XGBoost in Python — the most in-demand machine learning project for beginners in FinTech!
In this end-to-end machine learning project, you will learn how to:
✅ Load and clean real-world financial data using pandas
✅ Apply…
In this end-to-end machine learning project, you will learn how to:
✅ Load and clean real-world financial data using pandas
✅ Apply…