https://github.com/swat1563/machine-learning-tutorial
You can watch the tutorial on
https://github.com/swat1563/machine-learning-tutorial
Last synced: 11 months ago
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You can watch the tutorial on
- Host: GitHub
- URL: https://github.com/swat1563/machine-learning-tutorial
- Owner: SwAt1563
- Created: 2022-04-01T22:06:14.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2023-01-25T16:40:01.000Z (over 3 years ago)
- Last Synced: 2025-01-21T12:34:08.520Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 3.81 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Machine-Learning-Tutorial
You can watch the tutorial on [SwAt1563](https://www.youtube.com/watch?v=Y-aaf1xxakY&list=PLYgImg3VllLrt9JjYw52kWXRyBLlYqBjc) channel
## PART 1 NLTK
1) stopwrods
2) clean text - punctuation
3) swear words
4) roots
## PART 2 panda
1) panda - info - percentage
2) split data
3) show information gain
4) plot information gain
## PART 3 bias feature
1) bias feature
## PART 4 CountVectorizer
1) naive bias MultionmialNB - CountVectorizer
## PART 5 WITH JUST TEXT FEATURES
1) preprocessing
2) decision tree
3) network MLP
4) naive bias Gaussian
5) show information gain
6) accuracy scores - confusion matrix
## PART 6 WITH JUST FILE FEATURES
1) decision tree
2) network MLP
3) naive bias Gaussian
4) show information gain
5) accuracy scores - confusion matrix
## Cows Detection Models
1) Load Images
2) Features Vector Extraction
3) Convert Images to Patches
4) Convert Patches to Images
5) get Results
6) Confusion matrix
7) Cross Validation
8) KNN Model
9) Decision Tree Model
10) Logistic Regression Model
11) Random Forest Model
12) Gradient Boosting Model