Btech-ML

This repository contains all the basic concepts of Machine Learning required to be learnt by begineers.

Name		Name	Last commit message	Last commit date
Latest commit History 35 Commits
assignment		assignment
practical		practical
1. Ml and it's applications.pdf		1. Ml and it's applications.pdf
10. knn example.pdf		10. knn example.pdf
11.theory of knn.pdf		11.theory of knn.pdf
12.Naive Baye's theorem.pdf		12.Naive Baye's theorem.pdf
13.svm part1.pdf		13.svm part1.pdf
14.svm part-2.pdf		14.svm part-2.pdf
15. svm part-3.pdf		15. svm part-3.pdf
16.Unsupervised Learning.pdf		16.Unsupervised Learning.pdf
17.Hierarchical Clustering.pdf		17.Hierarchical Clustering.pdf
18.Genetic Algorithm.pdf		18.Genetic Algorithm.pdf
19. ANN-1.pdf		19. ANN-1.pdf
2. types of learning.pdf		2. types of learning.pdf
20. ANN-2.pdf		20. ANN-2.pdf
21. Gradient Descent Algo.pdf		21. Gradient Descent Algo.pdf
22. Training MLP.pdf		22. Training MLP.pdf
23. Activation function and vanishing gradient.pdf		23. Activation function and vanishing gradient.pdf
3. Performance measures(Confusion Matrix).pdf		3. Performance measures(Confusion Matrix).pdf
4. Precision, Recall, F-1 score.pdf		4. Precision, Recall, F-1 score.pdf
5. linear regression.pdf		5. linear regression.pdf
6. logistic regression.pdf		6. logistic regression.pdf
7. decision tree using IG and entropy.pdf		7. decision tree using IG and entropy.pdf
8. decision tree using gain ratio, gini index.pdf		8. decision tree using gain ratio, gini index.pdf
9. over under fitting variance bias.pdf		9. over under fitting variance bias.pdf
README.md		README.md
linear regression.pdf		linear regression.pdf

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