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This channel is for Programmers, Coders, Software Engineers.

1- Data Science

2- Machine Learning

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This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.me/addlist/8_rRW2scgfRhOTc0

✅ https://t.me/Codeprogrammer
23.04.2025, 15:50
t.me/datasciencem/1630
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Gradio: The easiest way to demo your models.

- Core Idea: Quickly turn #ML models into interactive web apps.

- No frontend skills needed. It's all #Python.

- Works with any Python code, including custom functions.

- Share via temporary links or deploy on #HuggingFace Spaces.

- Get user feedback to improve your models.

If you're looking to create interactive demos for your ML project, check out #Gradio!

♻️ Repost if you found this useful

⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
23.04.2025, 09:44
t.me/datasciencem/1629
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Follow me on linkedin (important for you)

https://www.linkedin.com/in/hussein-sheikho-4a8187246
21.04.2025, 15:27
t.me/datasciencem/1628
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A Complete Course to Learn Robotics and Perception

Notebook-based book "Introduction to Robotics and Perception" by Frank Dellaert and Seth Hutchinson

github.com/gtbook/robotics

roboticsbook.org/intro.html

#Robotics #Perception #AI #DeepLearning #ComputerVision #RoboticsCourse #MachineLearning #Education #RoboticsResearch #GitHub

⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
21.04.2025, 10:43
t.me/datasciencem/1627
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📢 5-Day Generative AI Intensive Course with #Google is now available as a self-paced Learn Guide!

Access whitepapers, podcasts, code labs, & recorded livestreams. Additionally, there is a bonus assignment for you!
https://www.kaggle.com/learn-guide/5-day-genai

#GenerativeAI #GoogleAI #AICourse #SelfPacedLearning #MachineLearning #DeepLearning #Kaggle #AICommunity #TechEducation #AIforEveryone

⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
19.04.2025, 21:09
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Data cleaning and preparation techniques.

https://t.me/DataScienceM 🌟
19.04.2025, 18:46
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📢 Join the SuperWAL Ecosystem – Step into the Real Value of Web3
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19.04.2025, 13:22
t.me/datasciencem/1624
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Datasets Guide 📚

A practical and beginner-friendly guide that walks you through everything you need to know about datasets in machine learning and deep learning. This guide explains how to load, preprocess, and use datasets effectively for training models. It's an essential resource for anyone working with LLMs or custom training workflows, especially with tools like Unsloth.

Importance:
Understanding how to properly handle datasets is a critical step in building accurate and efficient AI models. This guide simplifies the process, helping you avoid common pitfalls and optimize your data pipeline for better performance.

Link: https://docs.unsloth.ai/basics/datasets-guide

#MachineLearning #DeepLearning #Datasets #DataScience #AI #Unsloth #LLM #TrainingData #MLGuide

⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
18.04.2025, 12:23
t.me/datasciencem/1623
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17.04.2025, 15:03
t.me/datasciencem/1622
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Mathematical theory of Deep Learning:

[Download 282-page PDF. Updated version]:
arxiv.org/abs/2407.18384

#AI #ML #MachineLearning #DeepLearning #Mathematics #DataScience #DataScientist

⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
17.04.2025, 08:47
t.me/datasciencem/1621
Don't forget to attend this session!
17.04.2025, 08:10
t.me/datasciencem/1620
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Foundations of Large Language Models

Download it: https://readwise-assets.s3.amazonaws.com/media/wisereads/articles/foundations-of-large-language-/2501.09223v1.pdf

#LLM #AIresearch #DeepLearning #NLP #FoundationModels #MachineLearning #LanguageModels #ArtificialIntelligence #NeuralNetworks #AIPaper
16.04.2025, 22:22
t.me/datasciencem/1619
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Forget Coding; start Vibing! Tell AI what you want, and watch it build your dream website while you enjoy a cup of coffee.

Date: Thursday, April 17th at 9 PM IST

Register for FREE: https://lu.ma/4nczknky?tk=eAT3Bi

Limited FREE Seat !!!!!!
15.04.2025, 19:32
t.me/datasciencem/1618
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🎓 2025 Top IT Certification – Free Study Materials Are Here!

🔥Whether you're preparing for #Cisco #AWS #PMP #Python #Excel #Google #Microsoft #AI or any other in-demand certification – SPOTO has got you covered!

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15.04.2025, 08:36
t.me/datasciencem/1616
Репост
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🎓 2025 Top IT Certification – Free Study Materials Are Here!

🔥Whether you're preparing for #Cisco #AWS #PMP #Python #Excel #Google #Microsoft #AI or any other in-demand certification – SPOTO has got you covered!

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15.04.2025, 08:35
t.me/datasciencem/1615
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💧 18 of the best blogs to start and grow on your data science path!


👨🏻‍💻 If I were to start my data journey all over again, I would follow these 18 blogs without a break!

💬 The path to growth in data has nothing to do with your starting point, what matters is how consistently you learn.

✅ These blogs are like a mentor in your email; every week a new insight, a learning, a new idea. 👇


🥵 The Data Hustle Blog

⏪ Real experiences from the world of data in a friendly tone.

➖➖➖

🥵 Diving Into Data Blog

⏪ Summarizing data-heavy concepts in simple language.

➖➖➖

🥵 Tech Growth Series Blog

⏪ A guide to professional growth in the field of technology and data.

➖➖➖

🥵 Data Neighbor Blog

⏪ Discussion and experience of the real learning path in the data field.

➖➖➖

🥵 DataEngineer.io Blog

⏪ The world of data engineering with real solutions.

➖➖➖

🥵 The Data Analyst Blueprint Blog

⏪ A complete roadmap to becoming a data analyst step by step.

➖➖➖

🥵 Jam with AI Blog

⏪ Up-to-date content in the field of AI; suitable for starting and continuing learning.

➖➖➖

🥵 Zero2Dataengineer Blog

⏪ From zero to becoming an engineer with a specific path.

➖➖➖

🥵 Maistermind Blog

⏪ Deep insights and systems thinking in data.

➖➖➖

🥵 The Fit Data Scientist Blog

⏪ Data science + healthy lifestyle = true balance.

➖➖➖

🥵 To Be a Data Scientist Blog

⏪ Focus on career path, soft skills, and motivation to continue in the world of data.

➖➖➖

🥵 Smarter Techies Blog

⏪ Continuous learning and golden tips for data scientists.

➖➖➖

🥵 Data Marks Blog

⏪ An excellent compilation of useful resources and tools in the world of data.

➖➖➖

🥵 Tech Audience Accelerator Blog

⏪ Building a personal brand and growing in the technology space.

➖➖➖

🥵 ByteByteGo Blog

⏪ System design and technical concepts for every data engineer.

➖➖➖

🥵 The Neural Maze Blog

⏪ Exploring neural models and artificial intelligence with tangible examples.

➖➖➖

🥵 To Data & Beyond Blog

⏪ Data, personal growth, and new paths for the future of work.

➖➖➖

🥵 Non-Brand Data Blog

⏪ Combining data with marketing expertise and personalized strategy.
14.04.2025, 20:19
t.me/datasciencem/1614
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📖 100 Essential Data Science Interview Questions

👨🏻‍💻 Preparing for a data science interview?
Reviewing fundamental questions is one of the best strategies for success. During the interview, it's crucial to communicate clearly and simply—especially when explaining complex models and data.
These 100 carefully selected questions will not only help you impress your interviewer but also boost your confidence throughout the interview process.

#DataScienceInterview #TechCareers #InterviewPreparation

⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
14.04.2025, 17:38
t.me/datasciencem/1613
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This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.me/addlist/8_rRW2scgfRhOTc0

✅ https://t.me/Codeprogrammer
14.04.2025, 12:10
t.me/datasciencem/1612
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📚 Become a professional data scientist with these 17 resources!



1️⃣ Python libraries for machine learning

◀️ Introducing the best Python tools and packages for building ML models.

➖➖➖

2️⃣ Deep Learning Interactive Book

◀️ Learn deep learning concepts by combining text, math, code, and images.

➖➖➖

3️⃣ Anthology of Data Science Learning Resources

◀️ The best courses, books, and tools for learning data science.

➖➖➖

4️⃣ Implementing algorithms from scratch

◀️ Coding popular ML algorithms from scratch

➖➖➖

5️⃣ Machine Learning Interview Guide

◀️ Fully prepared for job interviews

➖➖➖

6️⃣ Real-world machine learning projects

◀️ Learning how to build and deploy models.

➖➖➖

7️⃣ Designing machine learning systems

◀️ How to design a scalable and stable ML system.

➖➖➖

8️⃣ Machine Learning Mathematics

◀️ Basic mathematical concepts necessary to understand machine learning.

➖➖➖

9️⃣ Introduction to Statistical Learning

◀️ Learn algorithms with practical examples.

➖➖➖

1️⃣ Machine learning with a probabilistic approach

◀️ Better understanding modeling and uncertainty with a statistical perspective.

➖➖➖

1️⃣ UBC Machine Learning

◀️ Deep understanding of machine learning concepts with conceptual teaching from one of the leading professors in the field of ML,

➖➖➖

1️⃣ Deep Learning with Andrew Ng

◀️ A strong start in the world of neural networks, CNNs and RNNs.

➖➖➖

1️⃣ Linear Algebra with 3Blue1Brown

◀️ Intuitive and visual teaching of linear algebra concepts.

➖➖➖

🔴 Machine Learning Course

◀️ A combination of theory and practical training to strengthen ML skills.

➖➖➖

1️⃣ Mathematical Optimization with Python

◀️ You will learn the basic concepts of optimization with Python code.

➖➖➖

1️⃣ Explainable models in machine learning

◀️ Making complex models understandable.

➖➖➖

⚫️ Data Analysis with Python

◀️ Data analysis skills using Pandas and NumPy libraries.


#DataScience #MachineLearning #DeepLearning #Python #AI #MLProjects #DataAnalysis #ExplainableAI #100DaysOfCode #TechEducation #MLInterviewPrep #NeuralNetworks #MathForML #Statistics #Coding #AIForEveryone #PythonForDataScience


⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
12.04.2025, 20:39
t.me/datasciencem/1611
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Cheatsheet Machine Learning Algorithms

⭐️ BEST DATA SCIENCE CHANNELS ON TELEGRAM ⭐️
12.04.2025, 15:01
t.me/datasciencem/1610
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This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.me/addlist/8_rRW2scgfRhOTc0

✅ https://t.me/Codeprogrammer
9.04.2025, 23:04
t.me/datasciencem/1609
3
1
1.0 k
20250409-29925-1xvanq1.gif.mp4
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9.04.2025, 18:08
t.me/datasciencem/1608
4
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Algorithms for Massive Data

PDF: https://arxiv.org/pdf/2301.00754

https://t.me/DataScienceM
8.04.2025, 23:52
t.me/datasciencem/1607
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8.04.2025, 23:50
t.me/datasciencem/1606
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"Introduction to Probability for Data Science"

One of the best books on #Probability. Available FREE.

Download the book:
probability4datascience.com/download.html

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

https://t.me/CodeProgrammer ✅
8.04.2025, 23:50
t.me/datasciencem/1605
9
1.1 k
7.04.2025, 17:55
t.me/datasciencem/1603
9
1.1 k
7.04.2025, 17:55
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4 advanced attention mechanisms you should know:

• Slim attention — 8× less memory, 5× faster generation by storing only K from KV pairs and recomputing V.

• XAttention — 13.5× speedup on long sequences via "looking" at the sum of values along diagonal lines in the attention matrix.

• Kolmogorov-Arnold Attention, KArAt — Adaptable attention with learnable activation functions using KANs instead of softmax.

• Multi-token attention (MTA) — Lets the model consider groups of nearby words together for smarter long-context handling.

Read the overview of them in our free article on https://huggingface.co/blog/Kseniase/attentions

https://t.me/DataScienceM 🌟
7.04.2025, 17:55
t.me/datasciencem/1602
4
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The latest and the most up-to-date cyber news will be presented on PPHM HACKER NEWS.
PPHM subscribers are the first people that receive firsthand cybernews and Tech news.

You won't miss any cyber news with us.


https://t.me/pphm_HackerNews
5.04.2025, 20:16
t.me/datasciencem/1600
1
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The Roadmap for Mastering MLOps in 2025

Organizations increasingly adopt machine learning solutions into their daily operations and long-term strategies, and, as a result, the need for effective standards for deploying and maintaining machine learning systems has become critical. MLOps (short for machine learning operations) arose to meet these needs. It encompasses a series of practices that blend machine learning modeling, software engineering, and data engineering across the entire machine learning system lifecycle.

If you are keen on venturing into the realm of MLOps in 2025 and unsure of where to start, this article highlights and puts together its building blocks and latest trends, both crucial to gain understanding of the current #MLOps landscape.

Read: https://machinelearningmastery.com/the-roadmap-for-mastering-mlops-in-2025/

By: https://t.me/DataScienceM 💠
5.04.2025, 19:42
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The Beginner’s Guide to Clustering with Python

Clustering is a widely applied method in many domains like customer and image segmentation, image recognition, bioinformatics, and anomaly detection, all to group data into clusters in terms of similarity. Clustering methods have a double-sided nature: as a machine learning technique aimed at discovering knowledge underneath unlabeled data (unsupervised learning), and as a descriptive data analysis tool for uncovering hidden patterns in data.

This article provides a practical hands-on introduction to common clustering methods that can be used in Python, namely k-means clustering and hierarchical clustering.

Read: https://machinelearningmastery.com/the-beginners-guide-to-clustering-with-python/

By: https://t.me/DataScienceM 🌟
5.04.2025, 14:45
t.me/datasciencem/1598
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🔗 Machine Learning from Scratch by Danny Friedman

This book is for readers looking to learn new #machinelearning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different #algorithms create the models they do and the advantages and disadvantages of each one.

This book will be most helpful for those with practice in basic modeling. It does not review best practices—such as feature engineering or balancing response variables—or discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.

https://dafriedman97.github.io/mlbook/content/introduction.html

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

https://t.me/CodeProgrammer ✅
5.04.2025, 08:29
t.me/datasciencem/1597
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Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.

https://t.me/DataScienceN
4.04.2025, 15:56
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#MachineLearning Systems — Principles and Practices of Engineering Artificially Intelligent Systems: https://mlsysbook.ai/

open-source textbook focuses on how to design and implement AI systems effectively

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

https://t.me/DataScienceM ✅
4.04.2025, 09:47
t.me/datasciencem/1594
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Linear Algebra

The 2nd best book on linear algebra with ~1000 practice problems. A MUST for AI & Machine Learning.

Completely FREE.

Download it: https://www.cs.ox.ac.uk/files/12921/book.pdf

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

https://t.me/CodeProgrammer ✅
4.04.2025, 08:44
t.me/datasciencem/1592
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4.04.2025, 08:44
t.me/datasciencem/1593
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Exercises in Machine Learning

This book contains 75+ exercises

Download, read, and practice:
arxiv.org/pdf/2206.13446

GitHub Repo: https://github.com/michaelgutmann/ml-pen-and-paper-exercises

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

https://t.me/CodeProgrammer ✅
4.04.2025, 04:00
t.me/datasciencem/1591
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🙏💸 500$ FOR THE FIRST 500 WHO JOIN THE CHANNEL! 🙏💸

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3.04.2025, 15:09
t.me/datasciencem/1590
Репост
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472
This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.me/addlist/8_rRW2scgfRhOTc0

✅ https://t.me/Codeprogrammer
2.04.2025, 23:54
t.me/datasciencem/1589
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Find these FREE AI Courses here 👇

https://www.mltut.com/best-resources-to-learn-artificial-intelligence/

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

https://t.me/CodeProgrammer ✅
2.04.2025, 11:19
t.me/datasciencem/1588
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📄 "Pandas Introduction to Advanced" booklet

👨🏻‍💻 You can't attend a #datascience interview and not be asked about Pandas! But you don't have to memorize all its methods and functions! With this booklet, you'll learn everything you need.

✔️ One of the most useful and interesting combinations is using #Pandas with #AWS Lambda, which can be very useful in real projects.

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29.03.2025, 23:09
t.me/datasciencem/1587
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The Beginner’s Guide to Machine Learning with Rust

In this article, we’ll explore the fundamentals of #machinelearning in #Rust, walk through essential libraries, and build a simple machine learning model. Whether you’re a Rust developer curious about machine learning or an machine learning practitioner looking for a high-performance alternative, this article will help you get started.

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course lecture on building Transformers from first principles:
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10 Must-Know Python Libraries for LLMs in 2025

Large language models (LLMs) are changing the way we think about AI. They help with #chatbots, text generation, and search tools, among other natural language processing tasks and beyond. To work with #LLMs, you need the right #Python libraries.

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🔖 The book that paved the way for me to "data science"!

👨🏻‍💻 "Where do I start now?" This was the first and biggest question I faced when I started my Data Science learning journey!

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Machine Learning from Scratch by Danny Friedman

This book is for readers looking to learn new machine learning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different algorithms create the models they do and the advantages and disadvantages of each one.

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Top 100 Machine Learning Interview Questions & Answers Cheatsheet

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The Roadmap for Mastering Language Models in 2025

Link: https://machinelearningmastery.com/the-roadmap-for-mastering-language-models-in-2025/

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Official #matplotlib cheat sheet

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Google Cloud just recently released "The PyTorch developer's guide to JAX fundamentals".

Contains a side-by-side implementation of a training loop in both #Pytorch and #JAX + Flax NNX sor those interested in exploring the JAX world in familiar terms.

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📝 Cheat sheets for data science and machine learning

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Keras Cheat Sheet: Neural Networks in Python

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15.03.2025, 14:50
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💡 Here is a useful cheat sheet for KNNs!

#CheatSheet #KNNs #DataScience

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15.03.2025, 10:58
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12.03.2025, 16:14
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👩‍💻 Prompt Engineering: A Practical Example

This real-world project tutorial covers zero-shot and few-shot prompting, delimiters, numbered steps, role prompts, chain-of-thought prompting, and more. Improve your LLM-assisted projects today.

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PCAP-certified Associate Python Programming Exam-Prep (2023)

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🏳️‍🌈 "NumPy Library" Tutorial

👨🏻‍💻 For the past few days, I've been busy preparing this comprehensive tutorial on the NumPy library for data science, trying to cover all the tips and tricks of this library.

✅ Why is this booklet different? Because it is not written based on just theoretical concepts, but is the result of my own experiences and learning. It has real and practical examples that will help you better understand #NumPy concepts and use them in your projects.💯

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🔥 MIT has updated its famous course 6.S191: Introduction to Deep Learning.

The program covers topics of #NLP, #CV, #LLM and the use of technology in medicine, offering a full cycle of training - from theory to practical classes using current versions of libraries.

The course is designed even for beginners: if you know how to take derivatives and multiply matrices, everything else will be explained in the process.

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Machine Learning Glossary

Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.

Link: https://ml-cheatsheet.readthedocs.io/en/latest/index.html
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10 Python One-Liners That Will Boost Your Data Preparation Workflow

Data preparation is a step within the data project lifecycle where we prepare the raw data for subsequent processes, such as data analysis and machine learning modeling. Data preparation can quite literally make or break your data project, as inadequate preparation will produce lousy output.

Given the importance of data preparation, we need a proper methodology for it. That’s why this article will explore how a simple one-liner Python code can boost your data preparation workflow.

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5 #AI Projects You Can Build This Weekend (with #Python)

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🔰 How to Work With Polars LazyFrames

In this tutorial, you'll gain an understanding of the principles behind #Polars LazyFrames. You'll also learn why using #LazyFrames is often the preferred option over more traditional #DataFrames

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👾 Agentic RAG Survey

A guide exploring agent-enhanced #RAG systems. This repository demonstrates single and multi-agent RAG implementations using #LangGraph, with practical examples for building intelligent applications.

Explore the evolution of RAG 🤖
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NumPy Practical Examples: Useful Techniques

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Learning Python, 6th Edition (2025)

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✉️ A comprehensive cheat sheet for Python for Data Science!

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Numpy - Arithmetic Operations

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🗂 10 “Real Data Science Portfolio” Examples

📁 I've brought you 10 of the best portfolios from data science professionals, each of whom has followed a unique path! Check out these 10 and get inspired to build a strong portfolio of your own!👇
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✅ Free ML Books You Should Know!

1⃣ Dive into Deep Learning: https://d2l.ai

🔢 Deep Learning: https://deeplearningbook.org

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t.me/datasciencem/1541
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16
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The Hundred-Page Language Models Book

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t.me/datasciencem/1540
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Prepared a Statistical Analysis Cheatsheet Using Python.

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✔️ Awesome AI/ML Resources: Learn AI/ML for beginners with a roadmap and free resources.

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18.02.2025, 15:21
t.me/datasciencem/1538
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12
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20x faster KMeans with Faiss!!

#KMeans uses a slow, exhaustive search to find the nearest centroids.

#Faiss uses "Inverted Index"—an optimized data structure to store and index data points for approximate neighbor search.

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18.02.2025, 10:22
t.me/datasciencem/1537
3
10
1.3 k
Create pivot tables in your Jupyter Notebook:

Here's the link to the #GitHub repo and documentation:

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17.02.2025, 10:43
t.me/datasciencem/1536
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🍃 Stem-Leaf Plot - An intelligent visualization!

It's a simple and effective way to visualize and compare datasets.

📊 Imagine we have two datasets: Set 1 (7, 12, 14, 17, 19, 23, 25) and Set 2 (3, 11, 16, 18, 20, 21, 24). We'll use a stem-leaf plot to compare them.

🌿 First, let's create the 'stem' which represents the tens place (0, 1, 2) and the 'leaf' represents the ones place (0-9).

🔍 By comparing the plots, we can see that Dataset 1 has higher values in the tens place, while Dataset 2 has a more uniform distribution.

🎯 Stem-leaf plots are great for small datasets and provide a clear picture of data distribution. The special thing about a stem-and-leaf diagram is that the original data can be read out of the graphical representation.

Give it a try next time you need to compare datasets!

✍🏽 Have you used stem-leaf plots before?

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15.02.2025, 16:08
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Complete Roadmap to Become a Data Scientist

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Best Deep Learning Courses:
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"Introduction to Applied Linear Algebra" by S. Boyd (Stanford) & L. Vandenberghe (UCLA)

📘 Freely available at: https://web.stanford.edu/~boyd/vmls/

📽 Lecture Videos at: https://youtube.com/playlist?list=PLoROMvodv4rMz-WbFQtNUsUElIh2cPmN9

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Last week we introduced how transformer LLMs work, this week we go deeper into one of its key elements—the attention mechanism, in a new #OpenSourceAI course, Attention in Transformers: Concepts and #Code in #PyTorch

Enroll Free: https://www.deeplearning.ai/short-courses/attention-in-transformers-concepts-and-code-in-pytorch/

#LLMCourse #Transformers #MachineLearning #AIeducation #DeepLearning #TechSkills #ArtificialIntelligence

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Discover an incredible LLM course designed to deepen your understanding of the transformer architecture and its role in building powerful Large Language Models (LLMs). This course breaks down complex concepts into easy-to-grasp modules, making it perfect for both beginners and advanced learners. Dive into the mechanics of attention mechanisms, encoding-decoding processes, and much more. Elevate your AI knowledge and stay ahead in the world of machine learning!

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#LLMCourse #Transformers #MachineLearning #AIeducation #DeepLearning #TechSkills #ArtificialIntelligence

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