This bundle contains class notes of all the important concepts for Machine Learning undergraduate level course. Starting from the history of AI, to ways of dealing with data and finally programs written and explained lucidly in Python, this bundle is exactly all that you need to keep a handy gist f...
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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
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Add to cartThis 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 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)
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Add to cartThis 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)
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
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Add to cartThis 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. 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
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Add to cartThis 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. 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
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Add to cartThis 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
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