Machine Learning
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Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.

Admin: @HusseinSheikho || @Hussein_Sheikho
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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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I often see people say that it's impossible to enter the IT field without expensive courses.

However, there's a huge amount of high-quality materials available for free:

📚 Computer Science
https://github.com/ossu/computer-science

📚 Data Structures & Algorithms
https://github.com/jwasham/coding-interview-university

📚 System Design
https://github.com/donnemartin/system-design-primer

📚 Web Development
https://github.com/TheOdinProject/curriculum

📚 Frontend / Backend / DevOps / Cloud
https://github.com/kamranahmedse/developer-roadmap

📚 Data Engineering
https://github.com/DataTalksClub/data-engineering-zoomcamp

📚 Machine Learning & AI
https://github.com/microsoft/ML-For-Beginners

📚 MLOps
https://github.com/DataTalksClub/mlops-zoomcamp

📚 Cybersecurity
https://github.com/OWASP/CheatSheetSeries

📚 Linux
https://github.com/trimstray/the-book-of-secret-knowledge

📚 Free Programming Books
https://github.com/EbookFoundation/free-programming-books

If you have internet and a bit of free time, you can learn computer science, algorithms, system design, DevOps, clouds, security, and machine learning for free.

The problem now isn't a lack of information. The problem is regularly opening these repositories and actually working on them.

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