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This document contains lucid description and class notes of the following topics: 1. Pseudo Random Numbers 2. Seed value in functions 3. Choosing seed value 4. Seed v/s Random state

This series of handwritten notes contains everything in a gist that a Computer Science or Statistics graduate student needs to study for his/her Machine Learning course. Topics covered: 1. History of Artificial Intelligence 2. The Turing Test 3. Weak AI v/s Strong AI 4. Human brain v/s Computer 5. Various Machine Learning domains 6. Feature Extraction 7. Soft Classification and Hard Classification 8. Linear Classifier 9. Evaluation Metrics 10. Probability Density Function 11. Probability Mass F...

This document contains class notes and lucid description of the following topics: 1. Introductory concepts of Artificial Intelligence 2. Why Machine Learning? 3. Timeline of Artificial Intelligence 4. Soft v/s Hard Classification 5. Various Machine Learning domains 6. Human brain v/s Computer

This document contains class notes and lucid description of the following topics: 1. Evaluation Metrics - Accuracy, Precision, Recall, F1 Score, PRC curve 2. Probability Density Function 3. Probability Mas Function 4. Cumulative Distribution Function 5. Dealing with tensors

This document contains class notes and lucid description of the following topics: 1. Feature extraction 2. Dealing with data 3. Least square solution 4. Minimum norm solution 5. Exploring the IRIS dataset using Python 6. Regression

This document contains class notes and lucid description of the following topics: 1. Classification problems 2. Gradient Descent Algorithm 3. Data Normalization 4. Multi-class classification (including non-linearity and loss function)