machine-learning-complete-guide

This repository contains the notebooks from the basic level to the production level of the machine learning process. This helps to understand how to approach the problem and also the syntaxes for the data preprocessing.

Table of Contents

• Python Classes
• ML Libraries
  • Numpy
  • Pandas
  • Matplotlib
  • Seaborn
• Play With Data
• Exploratory Data Analysis
• Feature Engineering
• Feature Construction and Feature Scaling
• Regression [Linear, Multi, Polynomial, Lasso and Ridge, ElasticNet, Logistic, Softmax Regression]
• Decision Trees
• Ensemble Learning
• Random Forest
• K-Means
• Gradient Boosting
• etc...

All General Code Snippet: machine-learning.py