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Some basic ML analisis and algorithms from the Data Mining course

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Name		Name	Last commit message	Last commit date
data		data
.gitignore		.gitignore
1. PCA.ipynb		1. PCA.ipynb
2. Web Scrapping.ipynb		2. Web Scrapping.ipynb
3. Random Subsampling & Hold Out.ipynb		3. Random Subsampling & Hold Out.ipynb
4. Linear Ridge Regression & Gaussian Kernel.ipynb		4. Linear Ridge Regression & Gaussian Kernel.ipynb
5. Logistic Regression.ipynb		5. Logistic Regression.ipynb
6. Naive Bayes.ipynb		6. Naive Bayes.ipynb
7. KNN.ipynb		7. KNN.ipynb
README.md		README.md
T1. Apriori algorithm.ipynb		T1. Apriori algorithm.ipynb
T2. Regression Tree.ipynb		T2. Regression Tree.ipynb
Untitled.ipynb		Untitled.ipynb
a. Linear Model for Regression and Clasification.ipynb		a. Linear Model for Regression and Clasification.ipynb
b. Nearest-Neighbor Methods.ipynb		b. Nearest-Neighbor Methods.ipynb
c. Bias-Variance and Curse of Dimensionality example.ipynb		c. Bias-Variance and Curse of Dimensionality example.ipynb
d. Multiple Regression and Gram-Schimdt Process.ipynb		d. Multiple Regression and Gram-Schimdt Process.ipynb
regression_tree.gv		regression_tree.gv
regression_tree.gv.pdf		regression_tree.gv.pdf
t1 extensive.ipynb		t1 extensive.ipynb
unix.gv		unix.gv

Machine Learning analisis and algorithms.

Some basic ML analisis and algorithms from the Data Mining course

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