This repository consists of my notes and implementations of different machine learning models.
Note: This repo is regarding the A-Z machine learning course, and so I have kept some images of the slides in the colab files for my reference.
- It consists of templates for a variety of topics starting from the preprocessing stage to numerous machine learning models.
- I've tried to implement different models covering regression, classification, clustering, reinforcement learning, natural language processing, deep learning, and association-rule learning.
- While completing the course, I thought of maintaining a repository for quick access to templates of different models as well as, the concepts behind that model.
- Only python implementation for each model is present.
- Click on any one of the below categories to get direct access to any model.
- Click on 'open in colab' option to get access to the ipynb file on Google Colab.
- Everything is present with two major categories as Theory and Implementation in the colab file.
- Remove the Theory part to get a basic template for that model.
- Data_Preprocessing
- Regression
- Classification
- Clustering
- Association_Rule_Learning
- Reinforcement_Learning
- Natural_Language_Processing
- Deep_Learning
- I shall highly recommend the Machine Learning A-Z Course to everyone, especially a beginner in ML, who wants to have a clear understanding of different concepts in Machine Learning and wants to kickstart their journey in this highly evolving domain.
- The course is available on Udemy here. It is also available for free here.
- Note: This course does not provide an in-depth explanation of some core topics like deep learning. However, a high-level overview with implementation is delivered.