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
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πŸ“Œ How I Streamline My Research and Presentation with LlamaIndex Workflows

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 19 min read

An example of orchestrating AI workflow with robustness, flexibility and controllability
πŸ“Œ How to Create a Powerful AI Email Search for Gmail with RAG

πŸ—‚ Category:

πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 17 min read

Learn how you can develop an application to search emails using RAG
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πŸ“Œ To Care, or Not to Care: Using XmR Charts to Differentiate Signals from Noise in Metrics

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 12 min read

A Step-by-Step Guide to Creating and Interpreting XmR Charts for Effective Data Analysis
πŸ“Œ How Tiny Neural Networks Represent Basic Functions

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 9 min read

A gentle introduction to mechanistic interpretability through simple algorithmic examples
πŸ“Œ Introducing NumPy, Part 1: Understanding Arrays

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 22 min read

Creating, describing, and accessing attributes
πŸ“Œ Automating Research Workflows with LLMs

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 14 min read

Augmenting researchers with atomic usage of AI
πŸ“Œ Linear Programming Optimization: The Simplex Method

πŸ—‚ Category: STATISTICS

πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 15 min read

Part 3: The algorithm under the hood
πŸ“Œ Open-Source Data Observability with Elementaryβ€Š-β€ŠFrom Zero to Hero (Part 2)

πŸ—‚ Category:

πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 7 min read

The guide to take your dbt tests to the next level for free
πŸ“Œ Open-Source Data Observability with Elementary – From Zero to Hero (Part 1)

πŸ—‚ Category: DATA ENGINEERING

πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 7 min read

A step-by-step hands-on guide I wish I had when I was a beginner
πŸ“Œ Logistic Regression, Explained: A Visual Guide with Code Examples for Beginners

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 9 min read

Finding the perfect weights to fit the data in
πŸ“Œ Practical Introduction to Polars

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 13 min read

Hands-on guide with side-by-side examples in Pandas
πŸ“Œ The Art of Asking Questions for Engineers

πŸ—‚ Category: BUSINESS

πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 6 min read

A Guideline for Asking Impactful Questions
πŸ“Œ Key Insights for Teaching AI Agents to Remember

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 20 min read

Recommendations on building robust memory capabilities based on experimentation with Autogen’s β€œTeachable Agents”
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πŸ“Œ Introducing NumPy, Part 3: Manipulating Arrays

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-09-15 | ⏱️ Read time: 7 min read

Shaping, transposing, joining, and splitting arrays
πŸ“Œ Build a Data Dashboard Using HTML, CSS, and JavaScript

πŸ—‚ Category: PROGRAMMING

πŸ•’ Date: 2025-10-03 | ⏱️ Read time: 14 min read

A framework-free guide for Python programmers
πŸ“Œ MobileNetV2 Paper Walkthrough: The Smarter Tiny Giant

πŸ—‚ Category: DEEP LEARNING

πŸ•’ Date: 2025-10-03 | ⏱️ Read time: 28 min read

Understanding and implementing MobileNetV2 with PyTorchβ€Š β€” the next generation of MobileNetV1
πŸ“Œ Is Multi-Collinearity Destroying Your Causal Inferences In Marketing Mix Modelling?

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-09-10 | ⏱️ Read time: 18 min read

Causal AI, exploring the integration of causal reasoning into machine learning
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πŸ“Œ Does Semi-Supervised Learning Help to Train Better Models?

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2024-09-09 | ⏱️ Read time: 8 min read

Evaluating how semi-supervised learning can leverage unlabeled data
πŸ“Œ Benchmarking Hallucination Detection Methods in RAG

πŸ—‚ Category: LARGE LANGUAGE MODELS

πŸ•’ Date: 2024-09-09 | ⏱️ Read time: 11 min read

Evaluating methods to enhance reliability in LLM-generated responses.