Machine Learning
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Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.

Admin: @HusseinSheikho || @Hussein_Sheikho
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πŸ“Œ How I Created a Data Science Project Following CRISP-DM Lifecycle

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-11-13 | ⏱️ Read time: 26 min read

An end-to-end project using the CRISP-DM framework
πŸ“Œ 3 Triangle-Shaped Chart Ideas as Alternatives to Some Basic Charts

πŸ—‚ Category: DATA VISUALIZATION

πŸ•’ Date: 2024-11-19 | ⏱️ Read time: 8 min read

Creating data visualizations with Python as alternatives to bar charts, pie charts, and some 3D…
πŸ“Œ Building a Local Voice Assistant with LLMs and Neural Networks on Your CPU Laptop

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-11-19 | ⏱️ Read time: 6 min read

A practical guide to run lightweight LLMs using python
πŸ“Œ Data Visualization Explained: What It Is and Why It Matters

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2025-09-21 | ⏱️ Read time: 8 min read

A brief introduction to data visualization and its importance in today’s technological landscape.
πŸ“Œ Python Can Now Call Mojo

πŸ—‚ Category: PROGRAMMING

πŸ•’ Date: 2025-09-21 | ⏱️ Read time: 14 min read

Boost your runtimes with lightning-fast Mojo code
πŸ“Œ Demystifying the Correlation Matrix in Data Science

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-11-13 | ⏱️ Read time: 16 min read

Understanding the Connections Between Variables: A Comprehensive Guide to Correlation Matrices and Their Applications
πŸ“Œ Nobody Puts AI in a Corner!

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-11-13 | ⏱️ Read time: 9 min read

Two short anecdotes about transformations, and what it takes if you want to become β€œAI-enabled”
πŸ“Œ The Ultimate Guide to Evaluating the Impact of Outlier Treatment in Time Series

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2024-11-13 | ⏱️ Read time: 22 min read

Sensitivity Analysis, Model Validation, Feature Importance & More!
πŸ“Œ Increase Trust in Your Regression Model The Easy Way

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-11-13 | ⏱️ Read time: 5 min read

How to use Conformalized Quantile Regression
πŸ“Œ Game Theory, Part 3 – You are the average of the five people you spend the most time with

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-11-13 | ⏱️ Read time: 5 min read

Is Tit-for-tat the best strategy in the Iterated Prisoner’s Dilemma game?
πŸ“Œ Boosting Algorithms in Machine Learning, Part II: Gradient Boosting

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2024-11-12 | ⏱️ Read time: 11 min read

Uncovering a simple yet powerful, award-winning machine learning algorithm
πŸ“Œ Reporting in Excel Could Be Costing Your Business More Than You Think – Here’s How to Fix It…

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-11-12 | ⏱️ Read time: 7 min read

Discover how you can save hours, eliminate costly data errors, and free up your team…
πŸ“Œ Beyond RAG: Precision Filtering in a Semantic World

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2024-11-12 | ⏱️ Read time: 9 min read

Aligning expectations with reality by using traditional ML to bridge the gap in a LLM’s…
πŸ“Œ From Parallel Computing Principles to Programming for CPU and GPU Architectures

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2024-11-12 | ⏱️ Read time: 23 min read

For early ML Engineers and Data Scientists, to understand memory fundamentals, parallel execution, and how…
πŸ“Œ Economics of Hosting Open Source LLMs

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-11-12 | ⏱️ Read time: 23 min read

Leveraging various deployment options
πŸ“Œ NER in Czech Documents with XLM-RoBERTa using Accelerate

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-11-12 | ⏱️ Read time: 10 min read

Decisions I made during the development of a document processing model that was successfully deployed
πŸ“Œ Decoding One-Hot Encoding: A Beginner’s Guide to Categorical Data

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-11-11 | ⏱️ Read time: 6 min read

Learning to transform categorical data into a format that a machine learning model can understand
πŸ“Œ Bessel’s Correction: Why Do We Divide by nβˆ’1 Instead of n in Sample Variance?

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2024-11-11 | ⏱️ Read time: 9 min read

Understanding the Unbiased Estimation of Population Variance
πŸ“Œ Why ETL-Zero? Understanding the shift in Data Integration

πŸ—‚ Category:

πŸ•’ Date: 2024-11-11 | ⏱️ Read time: 11 min read

When I was preparing for the Salesforce Data Cloud certification, I came across the term…
πŸ“Œ Detecting Anomalies in Social Media Volume Time Series

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-11-11 | ⏱️ Read time: 6 min read

How I detect anomalies in social Media volumes: A Residual-Based Approach
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πŸ“Œ Calibrating Marketing Mix Models In Python

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-11-11 | ⏱️ Read time: 12 min read

Part 2 of a hands-on guide to help you master MMM in pymc