Python | Machine Learning | Coding

Python | Machine Learning | Coding | R

😍 New to AI agents?

This free 10-part course on #GitHub will guide you from concept to #code as you start building #AI #agents:

⚡ Link: msft.it/6013qZ7Zb

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Python | Machine Learning | Coding | R

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Python | Machine Learning | Coding | R

course lecture on building Transformers from first principles:
https://www.dropbox.com/scl/fi/jhfgy8dnnvy5qq385tnms/lectureattentionneuralnetworks.pdf?rlkey=fddnkonsez76mf8bzider3hrv&dl=0

The #PyTorch notebooks also demonstrate how to implement #Transformers from scratch:
https://github.com/xbresson/CS52422025/tree/main/labslecture07

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Python | Machine Learning | Coding | R

👨🏻‍💻 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.

✅

Why should we use it?

🔢

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

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

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

🔢

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

┌ 🏳️‍🌈 The Algorithms - Python
└ 🐱 GitHub-Repos

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Python | Machine Learning | Coding | R

Pandas Introduction to Advanced.pdf

854.8 KB

📄

"Pandas Introduction to Advanced" booklet

👨🏻‍💻 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.

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

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Python | Machine Learning | Coding | R

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Python | Machine Learning | Coding | R

Exercises in Machine Learning

This book contains 75+ exercises

Download, read, and practice:
arxiv.org/pdf/2206.13446

GitHub Repo: https://github.com/michaelgutmann/ml-pen-and-paper-exercises

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Python | Machine Learning | Coding | R

Linear Algebra

The 2nd best book on linear algebra with ~1000 practice problems. A MUST for AI & Machine Learning.

Completely FREE.

Download it: https://www.cs.ox.ac.uk/files/12921/book.pdf

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Python | Machine Learning | Coding | R

Forwarded from Data Science Machine Learning Data Analysis Books

#MachineLearning Systems — Principles and Practices of Engineering Artificially Intelligent Systems: https://mlsysbook.ai/

open-source textbook focuses on how to design and implement AI systems effectively

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Python | Machine Learning | Coding | R

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Python | Machine Learning | Coding | R

🔗 Machine Learning from Scratch by Danny Friedman

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

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Python | Machine Learning | Coding | R

Stanford’s Machine Learning - by Andrew Ng

A complete lecture notes of 227 pages. Available Free.

Download the notes:
cs229.stanford.edu/main_notes.pdf

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Python | Machine Learning | Coding | R

"Machine Learning & LLMs for Beginners"

Don't miss these 2 books of 100-pages. Both are #FREE to read.

🌟 The Hundred-Page Machine Learning Book:
themlbook.com/wiki/doku.php

🌟 The Hundred-Page Language Model Book:
thelmbook.com

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Python | Machine Learning | Coding | R

Stanford's "Design and Analysis of Algorithms" Winter 2025

Lecture Notes & Slides: https://stanford-cs161.github.io/winter2025/lectures/

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Python | Machine Learning | Coding | R

"Introduction to Probability for Data Science"

One of the best books on #Probability. Available FREE.

Download the book:
probability4datascience.com/download.html

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Python | Machine Learning | Coding | R

📚

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.

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2️⃣ Deep Learning Interactive Book

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

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3️⃣ Anthology of Data Science Learning Resources

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

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4️⃣ Implementing algorithms from scratch

◀️ Coding popular ML algorithms from scratch

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5️⃣ Machine Learning Interview Guide

◀️ Fully prepared for job interviews

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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.

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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.

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1️⃣ UBC Machine Learning

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

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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.

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1️⃣

Explainable models in machine learning

◀️ Making complex models understandable.

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Data Analysis with Python

◀️ Data analysis skills using Pandas and NumPy libraries.

⚡️

BEST DATA SCIENCE CHANNELS ON TELEGRAM

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Python | Machine Learning | Coding | R

@Codeprogrammer Cheat Sheet Numpy.pdf

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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!

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