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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

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

http://t.me/codeprogrammer ⭐️
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Data Visualization Cheat sheets and Resources.zip
127.4 MB
Data Visualization Cheat sheets and Resources

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

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

http://t.me/codeprogrammer ⭐️
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IP Address Information using Python 🖥

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

http://t.me/codeprogrammer ⭐️
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🚀 Free Python Course with Certificate🚀

Unlock the world of coding with our
Python Fundamentals for Beginners course—completely FREE! 🚀 Whether you're new to programming or want to brush up on your skills, this course has everything you need to get started.

Why Choose This Course?
💡 100% FREE—No hidden fees
💡 Flexible learning at your own pace
💡 Certificate of completion to boost your resume
💡 Practical, hands-on learning with real examples

👉 Apply now - https://t.ly/6IvgN

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

http://t.me/codeprogrammer ⭐️
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All Cheat Sheets Collection (3).pdf
2.7 MB
Python Cheatsheets ⭐️

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

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python 💙.pdf
916.8 KB
Python Notes ⭐️

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

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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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👨🏻‍💻 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

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

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15 ways to optimize neural network training

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

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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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🪙 Everything you need to get started in machine learning.

Start with Python, explore scikit-learn, and neural networks using PyTorch. Perfect for beginners—get the skills you need to advance your career in just a few hours.

Start for free ▶️ ow.ly/HBtl50UtwCA

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

http://t.me/codeprogrammer ⭐️
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Arcade Academy - Learn Python 🖥

📖 Book

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

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pandas Project: Make a Gradebook With Python & pandas

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

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

http://t.me/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

https://t.me/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

https://t.me/CodeProgrammer 👑
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