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Introduction; Timeline; Man v/s Computer; Soft v/s Hard Classification
- Class notes • 30 pages • 2020
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Available in package deal
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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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Evaluation Metrics; Probability Functions; Tensors
- Class notes • 30 pages • 2020
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Available in package deal
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- $10.48
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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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Feature Extraction; Dealing with data; Regression
- Class notes • 30 pages • 2020
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Available in package deal
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- $10.48
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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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Multi-class Classification; Gradient Descent; Data Normalization
- Class notes • 30 pages • 2020
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Available in package deal
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- $10.48
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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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Support Vector Machines
- Summary • 8 pages • 2019
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- $7.49
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Basic Summary of how Support Vector Machines Work, with historical background and the algorithms idea from the basic to Kernel functions.
Make study stress less painful
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Pseudo Random Numbers
- Study guide • 4 pages • 2020
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- $50.48
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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...
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