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Complete Machine Learning Class Notes and Study Guide
- Package deal • 5 items • 2020
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- $50.48
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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 for both your exam preparation and for self study.
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Pseudo Random Numbers
- Class notes • 4 pages • 2020
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Available in package deal
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- $9.48
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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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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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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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- $10.48
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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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