Machine Learning with Python: Zero to GBMs

This repository is for storing all the projects and notebook for the course Machine Learning with Python: Zero to GBMs offered by Jovian

Overview

“Machine Learning with Python: Zero to GBMs” is an online course intended to provide a CodingFirst introduction to machine learning using the Scikit-learn library. The course takes a hands-on coding-focused approach and will be taught using live interactive Jupyter notebooks, allowing students to follow along and experiment. Theoretical concepts will be explained in simple terms using code.

Topics Covered:

• Exploratory Data Analysis with NumPy, Pandas, Matplotlib, Seaborn
• Download and Explore datasets from kaggle or other sources
• Explore Scikit-learn and it's function
• Build machine learning model on real world datasets
• Linear Regression, Logistic Regression algorithms
• Decision Tree, Random Forest algorithms
• Hyperparameter Tuning and Regularization
• Gradient Boosting with XGBoost

Selected Projects

• Walmart store's sales prediction by RandomForestRegression and XGBRegression

  • Notebook Link

• Rossmann Store's sales prediction by Gradient Boosting

  • Notebook Link

• Rainfall Prediction by Decision Tree and Random Forests

  • Notebook Link

• Rainfall Prediction by Logistic Regression

  • Notebook Link