Useful For Students to understand the formulas, and For Managers to understand which ML method is necessary for business, For Developers it's a chance to finally understand the theory.
Understand…
🟢 How The ML Engine Works?
Frameworks like scikit-learn have made us lazy. Calling model. fit has become so commonplace that in the era of Gen AI, it seems that training a model is just a matter of parameter selection.
ML engineers juggle with models of increasing complexity, but they are not always able to manually recalculate and explain the results of even the simplest algorithms: linear regression or classifier.
Models have become "black boxes", and this is a huge problem, because knowing what's behind each function is critical for understanding the process.
The cool thing is that all the material is explained in Excel. It sounds crazy, but that's the genius of it. Unlike code, where operations are hidden behind functions, in Excel every formula, every number, every calculation is in plain sight. No "black boxes".
7 articles have already been published:
Day 1 : k-NN Regressor
Day 2 : k-NN Classifier
Day 4 : GNB, LDA, QDA
Day 5 : GMM (Gaussian Mixture Model)
Day 6 : Decision Tree Regressor
Day 7 : Decision Tree Classifier
The cycle will help answer questions that often remain behind the scenes: how to properly handle categorical features, when scaling is not the right solution, and how to measure the importance of features by interpreting them directly with the model, bypassing model-agnostic packages LIME and SHAP.
Must-read for those who want to stop being a library operator. You can monitor New Articles Here, “One Day - One Article”
#AI #ML #DL #Tutorial #Excel
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🤖 Data Science, ML & Big Data with @DataXplore