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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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πŸ”– The book that paved the way for me to "data science"!

πŸ‘¨πŸ»β€πŸ’» "Where do I start now?" This was the first and biggest question I faced when I started my Data Science learning journey!

βͺ I was really overwhelmed by the large number of scattered sources, long courses, and specialized books full of heavy terminology. I didn't know how to start and move forward in this direction...

βœ”οΈ But the book Intro to Data Science with Python changed everything for me and gave me a new perspective!

✏️ This book is a complete guide to starting from scratch and is great for both beginners and professionals in this field!! From coding with Python to working with data, visualization, and even AI tools, it explains everything in the simplest and most practical way possible.

πŸ’Έ A great start for anyone looking to learn data science with Python!πŸ‘‡

β”Œ πŸ³οΈβ€πŸŒˆ Intro to Data Science with Python
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πŸ“„ E-book
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🐱 GitHub-Repos

#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.me/CodeProgrammer βœ…
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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
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🐱 GitHub-Repos

#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

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Pandas Introduction to Advanced.pdf
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πŸ“„ "Pandas Introduction to Advanced" booklet

πŸ‘¨πŸ»β€πŸ’» You can't attend a #datascience interview and not be asked about Pandas! But you don't have to memorize all its methods and functions! With this booklet, you'll learn everything you need.

βœ”οΈ One of the most useful and interesting combinations is using #Pandas with #AWS Lambda, which can be very useful in real projects.

#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

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πŸ”— Machine Learning from Scratch by Danny Friedman

This book is for readers looking to learn new #machinelearning 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.


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

#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

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