Demystifying Machine Learning

Important terms used in Lesson 2:

• Eyeballing your data/labels
• Supervised learning, Unsupervised learning, Reinforcement learning
• Imbalanced dataset
• Features and Feature vectors
• Features selection and Feature extraction
• Parametric and Non-parametric models
• Bias, Variance and Bias-Variance Tradeoff
• Overfitting and Underfitting
• Occam’s razor
• Train/test errors
• Baseline
• Upper bound
• Confusion matrix