Machine Learning with Python – Telegram
Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.

Admin: @HusseinSheikho || @Hussein_Sheikho
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👩‍💻 algorithms is a useful repository with a collection of algorithms implemented in Python!

🌟 It covers a wide range of algorithmic topics, including sorting, searching, graph manipulation, data structures, dynamic programming, cryptography, and more. The main goal of the repository is to provide an educational resource for learning algorithms and improving programming skills.

🔐 License: MIT

🖥 Github

https://news.1rj.ru/str/CodeProgrammer
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👩‍💻 A simple explanation of working with list in Python!

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📊 Data Visualization Learning Roadmap

👩🏻‍💻 Many people ask me where to start to learn "data visualization"?

✏️ The truth is that data visualization has its own language that we need to learn. We need to start with the basics and then gradually move on to more complex topics.

I have used some great resources along the way that I am still learning from. With every project, consultation, or course I take, my experiences grow. Here is a summary of the useful resources that have helped me:👇


1️⃣ Part One: Articles and Books:

📑 Full article by Lisa Charlotte Muth

🔗 Article link: started data visualization



2️⃣ Books:

📚 Book: Storytelling with Data

📚 Book: Practical Charts



3️⃣ Learning communities:

👤 Membership link: Storytelling with Data

☑️ Membership link: DataVis Community



4️⃣ Podcasts:

🌟 Link: SWD Podcast
🌟 Link: Data Viz Today
🌟 Link: PolicyViz



5️⃣ Key resources:

📚 Data Visualization Guide: This guide includes great resources for learning and developing data visualization skills.



6️⃣ Training courses:

📣 Effective data visualization / Learning to turn data into art.

📣 Information design /storytelling with data in Illustrator.

📣 Data Visualization / This course provides creative tools and techniques for visualization.



7️⃣ Data visualization tools:

⬅️ Link: Scimago Graphica
⬅️ Link: Datawrapper
⬅️ Link: Flourish
⬅️ Link: Tableau Public
⬅️ Link: RStudio & Python

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Forwarded from Tomas
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🌟 Join @DeepLearning_ai & @MachineLearning_Programming! 🌟
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Check if Instagram or Facebook is down using #Python

https://news.1rj.ru/str/CodeProgrammer 🐍
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Audio to Text using Python

#python

https://news.1rj.ru/str/CodeProgrammer ⭐️
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Machine Learning Algorithm basics!
Support Vector Machines (SVM) vs k-Nearest Neighbors (k-NN)

#MachineLearning #algorithms #ML #DataScience #ArtificialIntelligence #AI

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Machine Learning concepts - Intermediate Level.
Convolutional Neural Network (CNN) Layers: Convolution vs Pooling vs Fully Connected

#MachineLearning #NeuralNetwork #DeepLearning #ArtificialIntelligence #AI #Algorithms

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To generate stunning maps from OpenStreetMap data in #Python, use Prettymaps. It enables map creation with minimal code and offers versatile customization options.

🚀 Link to Prettymaps: bit.ly/3Ps02Qj

🔖 Dive deeper into the post: bit.ly/3PxxJjm

https://news.1rj.ru/str/CodeProgrammer ☄️
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🖥 Large Language Model Course

The popular free LLM course has just been updated.

This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.

The course is divided into 3 parts:
1️⃣ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2️⃣ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3️⃣ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.

⭐️ 41.4k stars on Github

📌 https://github.com/mlabonne/llm-course

#llm #course #opensource #ml

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