Open-source enthusiast.

How to Normalize or Standardize distribution in Machine Learning.

Introduction to Neural Networks using MP neuron with full discussion on different things like threshold, loss function and learning algorithm.

Vector and Matrices are at the heart of all Neural Networks. Today we are going to learn about vector and Matrix mathematics with the help of Matplotlib and numpy.

SVM or support vector machines are supervised learning models that analyze data and recognize patterns on its own. They are used for both classification and regression analysis. In this post we are going to talk about Hyperplanes, Maximal Margin Classifier, Support vector classifier, support vector machines and will create a model using sklearn.

Classification tree beginner's explanation with Gini Index/ Gini Coefficient, Entropy, Information gain and sklearn and finally discussion on metrics of tree.

Tree pruning and finding prune subtree using cost complexity pruning or and finding optimal alpha value using sklearn

Beginner's guide to Regression Trees including the equation, Pruning, Prediction using stratification of features in decision Trees using sklearn.

Introduction to K-nearest neighbor( KNN) algorithm using sklearn. Using different distance metrics and why is it important to normalize KNN features?

Evaluating your machine learning model can be done using accuracy, recall, precision, F1-score and/or mean absolute error or mean square error.

Pandas is an open source library built on the top of NumPy that allows us to analyse and clean the data for further step to be performed upon.

The first and foremost thing you need to get started in the data science and machine learning world is to have a little bit of knowledge of Python/ R.