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

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