Code Testing is the most crucial skill you should master
CI/CD
How to test your Python function before deployment
https://youtu.be/0heKg51lR0c
CI/CD
How to test your Python function before deployment
https://youtu.be/0heKg51lR0c
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Forwarded from Epython Lab
😘How do you reduce complexity of Dictionary data while searching in Python?
Fore more 👉https://youtu.be/tB6VDz4kwxY
🥰🥰 Follow Epython Lab for more contents 🥰
#complexity #data #python #machinelearning
Fore more 👉https://youtu.be/tB6VDz4kwxY
🥰🥰 Follow Epython Lab for more contents 🥰
#complexity #data #python #machinelearning
YouTube
Sort Dictionary by Key and Value in Python
In this tutorial, I have practically explained how to sort dictionary data by key or value in Python.
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Join this channel to get exclusive access:
https://bit.ly/363MzLo
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Forwarded from Epython Lab
Numpy🥰 is the most popular library used to manipulate numerical data
Here is a tutorial, to perform statistical analysis of your data
👉 https://youtu.be/cb_-745LZpg
Here is a tutorial, to perform statistical analysis of your data
👉 https://youtu.be/cb_-745LZpg
YouTube
Data Manipulation with Numpy : Mean/Median/Average in Numpy Array | Statistics I
Join this channel to get access to perks:
https://bit.ly/363MzLo
In this tutorial, you will be learning about the statistics concepts of how to calculate:
- mean
- median
- and weighted average of NumPy array
#python #machinelearning #datascience #numpy…
https://bit.ly/363MzLo
In this tutorial, you will be learning about the statistics concepts of how to calculate:
- mean
- median
- and weighted average of NumPy array
#python #machinelearning #datascience #numpy…
👍1
Forwarded from Epython Lab
I'm curious🤭 about statistics Vs Probability
Here, I have made some tutorials about probability distribution for Machine learning using Scipy Python library https://www.youtube.com/watch?v=TkFipAuH-rY&list=PL0nX4ZoMtjYEl_1ONxAZHu65DPCQcsHmI
Here, I have made some tutorials about probability distribution for Machine learning using Scipy Python library https://www.youtube.com/watch?v=TkFipAuH-rY&list=PL0nX4ZoMtjYEl_1ONxAZHu65DPCQcsHmI
YouTube
Introduction to Probability Distribution for Machine Learning | Random Variable in Python
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Learn introduction to Probability Distribution for Machine Learning
- Random Variable in Python
#python #probability #machinelearning #randomvariable…
https://www.youtube.com/channel/UCsFz0IGS9qFcwrh7a91juPg/join
Learn introduction to Probability Distribution for Machine Learning
- Random Variable in Python
#python #probability #machinelearning #randomvariable…
Forwarded from Epython Lab
3 most commonly used string formatting
https://youtu.be/d6K5u5S9wn4
https://youtu.be/d6K5u5S9wn4
YouTube
String Formatting in Python
In this tutorial, you will learn three different but commonly used string formatting in Python. You will also learn how to use them and the difference between them.
#python #datascience #machinelearning
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#python #datascience #machinelearning
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Forwarded from Epython Lab
What are the concepts behind list, tuple, and dictionary?
This tutorial will give you an insight about them
https://youtu.be/YYzOGQCBUjo
This tutorial will give you an insight about them
https://youtu.be/YYzOGQCBUjo
YouTube
The concept behind the built-in collections of Python | list vs. tuple vs. set vs. dictionary
Join this channel to get access to perks:
https://bit.ly/363MzLo
You can learn the concept behind the list, sets, tuples and dictionary in Python.
#python #machinelearning #datascience #pythoncollections
Ask your question at https://t.me/epythonlab/
Thanks…
https://bit.ly/363MzLo
You can learn the concept behind the list, sets, tuples and dictionary in Python.
#python #machinelearning #datascience #pythoncollections
Ask your question at https://t.me/epythonlab/
Thanks…
❤4
The hidden costs of data quality issues in Machine Learning
https://youtu.be/TdMu-0TEppM
https://youtu.be/TdMu-0TEppM
YouTube
The Hidden Costs of Data Quality Issues in Machine Learning
Hi! Welcome back! In this tutorial, I will explore a topic that many beginners overlook but is crucial to understanding: machine learning data quality. Poor data quality can make or break your model’s performance, costing you time, accuracy, and in some cases…
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📢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
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
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