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Class 11 and 12 Science and mathematics 1
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Latest notes & summaries Any institution
Easiest way to remember the first 20 elements
- Presentation
- • 2 pages's •
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Any Institution•Chemistry
Preview 1 out of 2 pages
Easiest way to remember the first 20 elements
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
- Package deal
- Class notes
- • 4 pages's •
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Any institution•Machine Learning
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Complete Machine Learning Class Notes and Study Guide• By anweshan_mukherjee
Preview 1 out of 4 pages
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...
- Book
- Study guide
- • 4 pages's •
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Any institution•Machine Learning
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Pattern Recognition and Machine Learning • Christopher M. Bishop• ISBN 9781493938438
Preview 1 out of 4 pages
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
- Book & Paket-Deal
- Class notes
- • 30 pages's •
-
Any institution•Machine Learning
-
Pattern Recognition and Machine Learning • Christopher M. Bishop• ISBN 9781493938438
-
Complete Machine Learning Class Notes and Study Guide• By anweshan_mukherjee
Preview 3 out of 30 pages
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
- Book & Paket-Deal
- Class notes
- • 30 pages's •
-
Any institution•Machine Learning
-
Pattern Recognition and Machine Learning • Christopher M. Bishop• ISBN 9781493938438
-
Complete Machine Learning Class Notes and Study Guide• By anweshan_mukherjee
Preview 3 out of 30 pages
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
- Book & Paket-Deal
- Class notes
- • 30 pages's •
-
Any institution•Machine Learning
-
Pattern Recognition and Machine Learning • Christopher M. Bishop• ISBN 9781493938438
-
Complete Machine Learning Class Notes and Study Guide• By anweshan_mukherjee
Preview 3 out of 30 pages
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)
- Book & Paket-Deal
- Class notes
- • 30 pages's •
-
Any institution•Machine Learning
-
Pattern Recognition and Machine Learning • Christopher M. Bishop• ISBN 9781493938438
-
Complete Machine Learning Class Notes and Study Guide• By anweshan_mukherjee
Preview 3 out of 30 pages
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)