Machine Learning – II

– Linear and Polynomial Regression, K-Nearest Neighbors, Decision Tree

– Balancing Bias vs Variance of ML Model, Ensemble Learning, Random Forest and Adaptive Boost

– Identifying Important Features of Data

– Time Series Analysis