This repository serves as an excellent introduction to implementing machine learning algorithms in depth such as linear and logistic regression, decision tree, random forest, SVM, Naive Bayes, KNN, ...

I have created a python code called regression_algorithms.ipynb for understanding how we are able to implement different approaches of non-linear regression algorithms in machine learning. Non-linear ...

Businesspeople need to demand more from machine learning so they can connect ... The primary goal of a linear regression training algorithm is to compute coefficients that make the difference ...

Now that you have a solid foundation in Supervised Learning, we shift our attention to uncovering the hidden structure from unlabeled data. We will start with an introduction to Unsupervised Learning.

The experiment for all the selected machine learning algorithm applied using anaconda IDE in Jupyter lab environment. As a result R-square of linear regression scores 0.95 and MLP scores 0.83 where as ...