This repository is for detailed understanding of machine learning and how to become an expert in that
Datasets: As of now contains only petals and sepals data
Learning: Contains learning that are done based on normal python files and jupyter notebooks
VMs: Contains virtual environments for various purposes:
- coding-practise
Agendas:
- Get proficient in Python Programming
- Understand Data Visualization Clearly
Things To Do
- Relearn Jupyter Notebooks
- Get upto pace to become proficient in Machine Learning
- Get more understanding on Models
- Learn more about Regression Models in Detail in SkLearn
- Complete Pandas Learning Kaggle
- Practise Pandas and Numpy (Data Camp, Real Python)
- Try to Create text in github heat map check how its done
Things Done
- Initial Setup completed
- Created Venv for Coding Practise
- Completed Introduction to Machine Learning in Kaggle
Regression Models that needs Deep Understanding:
- Decision Tree Regressor
- Random Forest Regressor
Note: Branch used for writing: citation