Computer Science notes for anyone
Get comprehensive class notes that you need for excelling any exam or for your self study!
- 6
- 0
- 1
Community
- Followers
- Following
7 items
Complete Machine Learning Class Notes and Study Guide
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.
- Package deal
- • 5 items •
- Pseudo Random Numbers • Class notes
- Multi-class Classification; Gradient Descent; Data Normalization • Class notes
- Feature Extraction; Dealing with data; Regression • Class notes
- Evaluation Metrics; Probability Functions; Tensors • Class notes
- Introduction; Timeline; Man v/s Computer; Soft v/s Hard Classification • Class notes
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.
Pseudo Random Numbers
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 •
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
Pseudo Random Numbers
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 •
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...
Introduction; Timeline; Man v/s Computer; Soft v/s Hard Classification
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 •
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
Evaluation Metrics; Probability Functions; Tensors
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 •
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
Feature Extraction; Dealing with data; Regression
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 •
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
Multi-class Classification; Gradient Descent; Data Normalization
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 •
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)