This repo contains a collection of Python programs and Jupyter Notebooks for Machine Learning, Deep Learning, , NLP, Image recognition using OpenCV and other topics related to Artificial Intelligence for Beginners

Disclaimer : Most of the scripts are from the Udemy course "Machine Learning A-Z: Hands-On Python & R in Data Science" by Kirill Eremenko and Hadelin de Ponteves.

The repository is composed of the following sections:

• datasets : This folder contains the various Datasets used in the Jupyter Notebooks in CSV files
• templates : This folder contains templates that can be used when writing new scripts for various Models
• Regression : This folder contains Jupyter notebooks for the following Regression Models:
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Support Vector Regression
  • Decision Tree Regression
  • Random Forest Regression
• Classification : This folder contains Jupyter notebooks for the following Classification Models:
  • Logistic Regression
  • K - Nearest Neighbor (K-NN)
  • Support Vector Machine (SVM)
  • Kernel SVM
  • Naive Bayes
  • Decision Tree Classification
  • Random Forest Classification
• Clustering : This folder contains Jupyter notebooks for the following Clustering Models:
  • K-Means Clustering
  • Hierarchical Clustering
• Association Rule Learning : This folder contains Jupyter notebooks for the following Association Rule Learning Models:
  • Apriori
• Reinforcement Learning : This folder contains Jupyter notebooks for the following Reinforcement Learning Models:
  • Upper Confidence Bound (UCB)
  • Thompson Sampling
• Natural Language Processing : This folder contains Jupyter notebook for the Natural Language Processing Model
• Deep Learning : This folder contains the Jupyter notebooks for the following Deep Learning Models:
  • Artificial Neural Networks (ANN)
  • Convolutional Neural Networks (CNN)