MLP by hand ✍️ Excel

📂 Tags: #python #ML #MLP

https://t.iss.one/codeprogrammer ⭐️

Download Excel file 👇
https://t.iss.one/+Tdshx2j5cZ00N2Ji

AI & ML Guided Projects with Python 👑

Below is a list of guided projects to master AI & ML with Python that you should try ⭐

🔗 Link: https://thecleverprogrammer.com/2024/10/11/ai-ml-projects-with-python/

Is it useful to you❓

📂 Tags: #ML #Ai #Python

https://t.iss.one/codeprogrammer ⭐️

CS229 Lectures Notes (2023)

By: Andrew Ng & Tengyu Ma

Is it useful to you ❓

📂 Tags: #ML #Ai #RAG #LLM

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250 Coursera FREE Courses [Data Science, Machine Learning, Python] 2024 🏵

🔗 Link: https://www.mltut.com/coursera-free-courses/

📂 Tags: #DataScience #Python #ML #AI #courses

https://t.iss.one/codeprogrammer ⭐️

The opportunity is not too late, register before it is too late  😮

Pandas Data Cleaning (Guide)

🔑 Tags: #Pandas #DataCleaning #ML

https://t.iss.one/DataScienceM ✅

Pandas Data Cleaning (Guide)

🔑 Tags: #Pandas #DataCleaning #ML

https://t.iss.one/DataScienceM ✅

Confusion matrix (TP, FP, TN, FN), clearly explained

🔑 Tags: #PYTHON #AI #ML

https://t.iss.one/DataScienceM ✅

📝 Cheat sheets for data science and machine learning

Link: https://sites.google.com/view/datascience-cheat-sheets

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

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Top 100 Machine Learning Interview Questions & Answers Cheatsheet

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https://t.iss.one/CodeProgrammer ✅



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 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.

🌟 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

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