This notebook attempts to elaborate and build on the examples in Aurelien Geron's book "Hands-on Machine Learning with Scikit-Learn and TensorFlow".
- Explore different Scikit Machine Learning models (Linear Regressor, Decision Tree Regressor, Random Forest Regressor) with pipeline automation.
- Explore Number Classifications (Stochastic Gradient Descent, MultiLabel, Multi-output KNeighbors Classifiers)
- Explore Kaggle Competitions Topics (Titanic, Santander Customer Transaction Prediction)
- Work on different Regression models with visualization (matplotlib)
- Basic TensorFlow with modularity, sharing variable, name scoping and observing graphs in TensorBoard
- Employ Data Visualization techniques and various Training models on Kaggle datasets (IEEE-CIS Fraud Detection)
- Explore Image Classifications on Fashion_MNIST dataset using 4-layers Neural Network with Keras