Machine Learning
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Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.

Admin: @HusseinSheikho
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20x faster KMeans with Faiss!!

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

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

#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras

https://t.iss.one/DataScienceM
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πŸ”₯ 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.

The lectures are released for free on YouTube and the #MIT platform on Mondays, with the first one already available

.

All slides, #code and additional materials can be found at the link provided.

πŸ“Œ Fresh lecture : https://youtu.be/alfdI7S6wCY?si=6682DD2LlFwmghew

#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.iss.one/CodeProgrammer βœ…
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Numpy @CodeProgrammer.pdf
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πŸ³οΈβ€πŸŒˆ "NumPy Library" Tutorial

πŸ‘¨πŸ»β€πŸ’» 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.

βœ… 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.πŸ’―

#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.iss.one/CodeProgrammer βœ…
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πŸ‘©β€πŸ’» 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.

Link: https://realpython.com/practical-prompt-engineering/

#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.iss.one/CodeProgrammer βœ…
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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.

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

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

#DataScience #MachineLearning #CheatSheet #stats #analytics #ML #IA #AI #programming #code #rstats #python #deeplearning #DL #CNN #Keras #R

https://t.iss.one/CodeProgrammer βœ…
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