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

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🔗 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 practices—such as feature engineering or balancing response variables—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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📚 Become a professional data scientist with these 17 resources!



1️⃣ Python libraries for machine learning

◀️ Introducing the best Python tools and packages for building ML models.



2️⃣ Deep Learning Interactive Book

◀️ Learn deep learning concepts by combining text, math, code, and images.



3️⃣ Anthology of Data Science Learning Resources

◀️ The best courses, books, and tools for learning data science.



4️⃣ Implementing algorithms from scratch

◀️ Coding popular ML algorithms from scratch



5️⃣ Machine Learning Interview Guide

◀️ Fully prepared for job interviews



6️⃣ Real-world machine learning projects

◀️ Learning how to build and deploy models.



7️⃣ Designing machine learning systems

◀️ How to design a scalable and stable ML system.



8️⃣ Machine Learning Mathematics

◀️ Basic mathematical concepts necessary to understand machine learning.



9️⃣ Introduction to Statistical Learning

◀️ Learn algorithms with practical examples.



1️⃣ Machine learning with a probabilistic approach

◀️ Better understanding modeling and uncertainty with a statistical perspective.



1️⃣ UBC Machine Learning

◀️ Deep understanding of machine learning concepts with conceptual teaching from one of the leading professors in the field of ML,



1️⃣ Deep Learning with Andrew Ng

◀️ A strong start in the world of neural networks, CNNs and RNNs.



1️⃣ Linear Algebra with 3Blue1Brown

◀️ Intuitive and visual teaching of linear algebra concepts.



🔴 Machine Learning Course

◀️ A combination of theory and practical training to strengthen ML skills.



1️⃣ Mathematical Optimization with Python

◀️ You will learn the basic concepts of optimization with Python code.



1️⃣ Explainable models in machine learning

◀️ Making complex models understandable.



⚫️ Data Analysis with Python

◀️ Data analysis skills using Pandas and NumPy libraries.


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@Codeprogrammer Cheat Sheet Numpy.pdf
213.7 KB
This checklist covers the essentials of NumPy in one place, helping you:

- Create and initialize arrays
- Perform element-wise computations
- Stack and split arrays
- Apply linear algebra functions
- Efficiently index, slice, and manipulate arrays

…and much more!

Feel free to share if you found this useful, and let me know in the comments if I missed anything!

⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟

#NumPy #Python #DataScience #MachineLearning #Automation #DeepLearning #Programming #Tech #DataAnalysis #SoftwareDevelopment #Coding #TechTips #PythonForDataScience
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from SQL to pandas.pdf
1.3 MB
🐼 "Comparison Between SQL and pandas" – A Handy Reference Guide

⚡️ As a data scientist, I often found myself switching back and forth between SQL and pandas during technical interviews. I was confident answering questions in SQL but sometimes struggled to translate the same logic into pandas – and vice versa.

🔸 To bridge this gap, I created a concise booklet in the form of a comparison table. It maps SQL queries directly to their equivalent pandas implementations, making it easy to understand and switch between both tools.

This reference guide has become an essential part of my interview prep. Before any interview, I quickly review it to ensure I’m ready to tackle data manipulation tasks using either SQL or pandas, depending on what’s required.

📕 Whether you're preparing for interviews or just want to solidify your understanding of both tools, this comparison guide is a great way to stay sharp and efficient.

#DataScience #SQL #pandas #InterviewPrep #Python #DataAnalysis #CareerGrowth #TechTips #Analytics

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