**Why Excel?**

– Most business computers already have it installed, and it is the most familiar and widely used tool for office analysis, so there is no need for additional installation or language learning time.

– Most ML Boot Camps focus more on technical education in coding than understanding algorithms, making it difficult to understand the cause when there is a problem with the actual result, and hard to conceptually understand what role hyperparameters play.

– Even after attending an ML camp, if coding is not continuously required for work, you may forget the coding syntax over time.

– Not everyone needs Python coding for analysis, and not all problems have to be solved using Python.

– Many errors are found when explaining theories on YouTube or blogs, but actual coding is not done one by one, but by calling a package (in one line), so most YouTubers or bloggers cannot verify errors.

– Using Excel’s Worksheet, you can select cells, enter functions, and see numbers move visually, allowing you to learn the concepts of ML/DL algorithms in a much more three-dimensional way.

– Excel has a “Find Solution” optimization feature, so you only have to work up to defining the concepts of complex algorithms, and the actual calculation part can be solved by finding the solution.

– Excel has a repeat calculation function, so you can execute Do looping.

I hope this helps! Let me know if you need further assistance.

**Constraints of Excel**

Excel itself does not provide any functions for ML other than linear regression, so it’s impossible to implement without understanding the algorithm. If there are more than 200 variables, it’s impossible to solve DL problems with Excel.

**Whom this blog can help**

– Those who want to learn the methodology of ML/DL/RL without coding experience

– Designers of NPU etc. who want to learn the operating principles of ML/DL/RL

– Clear conceptualization of ML/DL/RL regardless of analytical experience

– Providing new educational methodologies and concepts as a textbook for ML/DL/RL classes for college students

**Blog Structure**

The entire blog is divided into supervised learning-regression, classification, unsupervised learning, artificial neural network, reinforcement learning. Each blog proceeds with a detailed mathematical derivation process and a simple example using Excel for each algorithm, and the corresponding Excel file is attached for each algorithm, so you can freely download and verify it yourself.