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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://t.me/CodeProgrammer


pandas Project: Make a Gradebook With Python & pandas

Link: https://realpython.com/pandas-project-gradebook/


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Arcade Academy - Learn Python 🖥

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🪙 Everything you need to get started in machine learning.

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GANs clearly explained with visuals

This website provides a clear explanation
, Try it out yourself: poloclub.github.io/ganlab/

📂 Tags: #DataScience #Python #ML #AI #LLM #Courses #Pandas #DV #GAN

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15 ways to optimize neural network training

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👨🏻‍💻 One of the most popular GitHub repositories for "learning and using algorithms in Python" is The Algorithms - Python repo with 196K stars.

✏️ It has a lot of organized and categorized code that you can use to find, read, and run different algorithms. Everything you can think of is here; from simple algorithms like sorting to advanced algorithms for machine learning, artificial intelligence, neural networks, and more.

Why should we use it?

🔢 For learning: If you're looking to learn algorithms in action, this is great.

🔢 For practice: You can take the codes, run them, and modify them to better understand.

🔢 For projects : You can even use the codes here in real-life or academic projects.

🔢 For interviews: If you're preparing for data science interviews, this is full of practical algorithms.


🏳️‍🌈 The Algorithms - Python
🐱 GitHub-Repos

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🎥 Free course "Computational Thinking and Data Science"
📊 MIT University

👨🏻‍💻 One of the best resources I've found for learning computational thinking and data science is this free course from MIT. It covers concepts like data analysis, computational modeling, and using algorithms to solve complex problems. I've included the links to the slides and videos from the course below:👇

📄 Slides link: Lecture Slides and Files

📹 Video links: Lecture Videos

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV #MIT

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python 💙✨.pdf
916.8Kb
Python Notes ⭐️

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📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

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All Cheat Sheets Collection (3).pdf
2.7Mb
Python Cheatsheets ⭐

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🚀 Free Python Course with Certificate🚀

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IP Address Information using Python 🖥

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Data Visualization Cheat sheets and Resources.zip
127.4Mb
Data Visualization Cheat sheets and Resources

Corpus of 32 DV cheat sheets, 32 DV charts and 7 recommended DV books

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http://t.me/codeprogrammer ⭐️


A comprehensive playlist to step into and master the world of machine learning and data science!


1️⃣ Data Science Principles:

😉 Essential Mathematics for Machine Learning: Link
😉 Overview and commonly used terms: Link
😉 Current interview trends: Link
😉 Linear Regression Guide: Link
😉 Logistic Regression Playlist: Link
😉 Classification criteria: Link
😉 Simple Bayes Classifier: Link
😉 Types of variables: Link
😉 Dimension reduction: Link
😉 Entropy, mutual entropy, KL divergence: link
😉 Dynamic Pricing Overview: Link


2️⃣ Building recommender systems:

😉 Netflix Calibrated Recommendations: Link
😉 Netflix Integrated Recommendation Model: Link
😉 The Evolution of Recommender Systems: Link
😉 Embedding tutorial: Link
😉 Annoy library for approximate nearest neighbor: link
😉 Reducer product for ANN: Link
😉 Model-based account recommendations: Link
😉 PID controller for diversity: link
😉 Instagram Recommender System: Link
😉 LinkedIn CTR Modeling: Link
😉 Meituan's two-tower recommendation model: Link
😉 Scalable Two Tower Model Question-Item: Link
😉 Twitter Recommender Algorithm: Link
😉 eBay language model for recommender system: link
😉 Overcoming biases for recommender systems: Link


3️⃣ Advanced Model Techniques and Applications:

😉 Importance of Model Calibration: Link
😉 Detect and monitor data changes: Link
😉 Neural Networks Training: Link
😉 Analytics-based advertising with Pinterest: Link
😉 Using Pre-trained Bert: Link
😉 Model Compression with Knowledge Distillation: Link
😉 Multi-Armed Bandit Strategies: Link


4️⃣ The world of large language models (LLMs):

😉 Conversational AI: Link
😉 The dual nature of conversational language models: link
😉 Frontier Developments in LLM: Link
😉 Improving the performance of open source LLMs: Link
😉 Building artificial intelligence in Shah Rukh Khan style: Link

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🎯 All free IBM courses for data science
Along with a certificate of completion


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✏️ Using the R language to analyze data and implement data science projects.
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✏️ Learn how to create professional charts and visualize data with tools like ggplot2.
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✏️ Learn how to use data to improve business decisions.
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✏️ Familiarity with the basics of deep learning and the concepts of neural networks.
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1️⃣ Deep Learning Course with TensorFlow

✏️ Working with TensorFlow to build and train deep learning models.

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👩‍💻 Python Developer Roadmap is a guide for aspiring Python developers that helps structure and plan their learning and career development!

🌟 It provides a step-by-step plan that covers key aspects of Python development, from basic knowledge and syntax to more advanced topics such as databases, web development, testing, machine learning, and microservices development.

🔐 License: MIT

🖥 Github

https://t.me/CodeProgrammer


Git commands basics

#MachineLearning #DeepLearning #BigData #Datascience #ML #Pandas #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents

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Python Network Programming Cheat Sheet 🖥

#MachineLearning #DeepLearning #BigData #Datascience #ML #Pandas #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents

http://t.me/codeprogrammer

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