Machine Learning
@DataScienceM
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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.

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Machine Learning
38.9K subscribers
Machine Learning
📌 Could Conversational AI-Driven Data Analytics Finally Solve the Data Democratization Riddle?

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-10-01 | ⏱️ Read time: 8 min read

A Data Leader’s Realistic Assessment
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📌 TSV in Pandas: A How-To Guide

🗂 Category: DATA SCIENCE

🕒 Date: 2024-10-01 | ⏱️ Read time: 6 min read

The Correct Way of Loading and Writing TSV Files with Pandas
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📌 Statistical Analysis on Scoring Bias

🗂 Category: DATA SCIENCE

🕒 Date: 2024-10-01 | ⏱️ Read time: 22 min read

In the 2024 Argentine Tango World Championship
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📌 Beyond the Hype: When Generative AI Isn’t Always the Answer

🗂 Category:

🕒 Date: 2024-10-01 | ⏱️ Read time: 9 min read

Why predictive AI might still be your best bet
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📌 P-Companion: Amazon’s Principled Framework for Diversified Complementary Product Recommendation

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-10-01 | ⏱️ Read time: 12 min read

A Deep Dive into Amazon’s Complementary Product Recommendation Framework
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📌 Under-trained and Unused tokens in Large Language Models

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-10-01 | ⏱️ Read time: 8 min read

Existence of under-trained and unused tokens and Identification Techniques using GPT-2 Small as an Example
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📌 My 7 Sources of Income as a Data Scientist

🗂 Category: CODING

🕒 Date: 2024-10-01 | ⏱️ Read time: 8 min read

All the ways I make money as a practising data scientist
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📌 Journey of an unlikely entrepreneur

🗂 Category:

🕒 Date: 2024-10-01 | ⏱️ Read time: 10 min read

Developing an oceanographic application for big game fishing
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📌 Graph Neural Networks Part 1. Graph Convolutional Networks Explained

🗂 Category:

🕒 Date: 2024-10-01 | ⏱️ Read time: 12 min read

Node classification with Graph Convolutional Networks
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📌 What I Learned in my First 9 Months as a Freelance Data Scientist

🗂 Category: DATA SCIENCE

🕒 Date: 2024-10-01 | ⏱️ Read time: 24 min read

Observations and lessons learned from in the trenches
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📌 Support Vector Classifier, Explained: A Visual Guide with Mini 2D Dataset

🗂 Category: DATA SCIENCE

🕒 Date: 2024-10-01 | ⏱️ Read time: 17 min read

Finding the best “line” to separate the classes? Yeah, sure…
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📌 Can Transformers Solve Everything?

🗂 Category: MACHINE LEARNING

🕒 Date: 2024-10-01 | ⏱️ Read time: 15 min read

Looking into the math and the data reveals that transformers are both overused and underused.
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📌 Evaluating performance of LLM-based Applications

🗂 Category:

🕒 Date: 2024-09-30 | ⏱️ Read time: 9 min read

Evaluation Framework for real-world requirements
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📌 5 Must-Know Techniques for Mastering Time-Series Analysis

🗂 Category: DATA SCIENCE

🕒 Date: 2024-09-30 | ⏱️ Read time: 22 min read

Elevate Your Machine Learning Forecasting with Accurate Data Splitting, Time-Series Cross-Validation, Feature Engineering, and More!
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📌 Exploring the World of Markov Chains: Unlocking the Power of Probabilistic Transitions

🗂 Category: PROBABILITY

🕒 Date: 2024-09-30 | ⏱️ Read time: 11 min read

An Introduction to Markov Chains, their applications, and how to use Monte Carlo Simulations in…
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📌 Is Less More? Do Deep Learning Forecasting Models Need Feature Reduction?

🗂 Category: ANALYTICS

🕒 Date: 2024-09-30 | ⏱️ Read time: 14 min read

To curate, or not to curate, that is the question
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📌 Stein’s Paradox

🗂 Category: DATA SCIENCE

🕒 Date: 2024-09-30 | ⏱️ Read time: 8 min read

Why the Sample Mean Isn’t Always the Best
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📌 Evaluating Train-Test Split Strategies in Machine Learning: Beyond the Basics

🗂 Category: DATA SCIENCE

🕒 Date: 2024-09-30 | ⏱️ Read time: 6 min read

Creating Appropriate Test Sets and Sleeping Soundly.
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📌 The AI Developer’s Dilemma: Proprietary AI vs. Open Source Ecosystem

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-09-30 | ⏱️ Read time: 20 min read

Fundamental Choices Impacting Integration and Deployment at Scale of GenAI into Businesses
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Forwarded from Python | Machine Learning | Coding | R
🏳️‍🌈 Learning Python for science is
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💬 Supplementary resources and exercises including project-based tutorials, guides, and practical exercises.

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🖥 Repo: Learn By Doing

💬 Project-based tutorials in AI and machine learning for all levels.

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🖥 Repo: Awesome Jupyter

💬 A curated collection of notebooks, tools, and powerful libraries for working with Jupyter.

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🖥 Repo: Python Mini Projects

💬 A collection of mini-projects like games and small apps that you can quickly run and practice.

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🖥 Repo: 100Projects of Code

💬 An educational challenge including 100 real projects; you practice and see your progress day by day.

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💬 Practical ideas and examples to start data science with Python.

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