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Discover powerful insights with Python, Machine Learning, Coding, and Rβ€”your essential toolkit for data-driven solutions, smart alg

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


1️⃣ Data Science Principles:

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


2️⃣ Building recommender systems:

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


3️⃣ Advanced Model Techniques and Applications:

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


4️⃣ The world of large language models (LLMs):

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

πŸ“‚ 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

πŸ“‚ Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

http://t.me/codeprogrammer ⭐️
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IP Address Information using Python πŸ–₯

πŸ“‚ Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

http://t.me/codeprogrammer ⭐️
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πŸš€ Free Python Course with CertificateπŸš€

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

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πŸ‘‰ Apply now - https://t.ly/6IvgN

πŸ“‚ Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

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

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πŸ“‚ Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

http://t.me/codeprogrammer ⭐️
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python πŸ’™βœ¨.pdf
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Python Notes ⭐️

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πŸ“‚ Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

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πŸŽ₯ Free course "Computational Thinking and Data Science"
πŸ“Š MIT University

πŸ‘¨πŸ»β€πŸ’» 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:πŸ‘‡

πŸ“„ Slides link: Lecture Slides and Files

πŸ“Ή Video links: Lecture Videos

πŸ“‚ Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV #MIT

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πŸ‘¨πŸ»β€πŸ’» 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.

βœ… Why should we use it?

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

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

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

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


β”Œ πŸ³οΈβ€πŸŒˆ The Algorithms - Python
β””
🐱 GitHub-Repos

πŸ“‚ Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV #MIT

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