ML-Programs-from-scratch

Basics machine learning programs from scratch - without using scikit-learn or any another libraries.
Mainly used Pandas, Numpy, Matplotlib and Seaborn.
Most of the algorithms works on 'machine learning hello word' - iris dataset here.

pandas	numpy	matplotlib	seaborn

Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
0. Numpy, Pandas, Matplotlib.ipynb		0. Numpy, Pandas, Matplotlib.ipynb
1. Data Preprocessing.ipynb		1. Data Preprocessing.ipynb
2. Linear Regression using Normal Method.ipynb		2. Linear Regression using Normal Method.ipynb
3. Linear Regression using Gradient Descent.ipynb		3. Linear Regression using Gradient Descent.ipynb
4. Multivariate Linear Regression.ipynb		4. Multivariate Linear Regression.ipynb
5. Logistic Regression.ipynb		5. Logistic Regression.ipynb
6. KNN Classification.ipynb		6. KNN Classification.ipynb
7. Kmeans Clustering.ipynb		7. Kmeans Clustering.ipynb
8. ID3 - Decision Tree.ipynb		8. ID3 - Decision Tree.ipynb
9. CART - Decision Tree.ipynb		9. CART - Decision Tree.ipynb
91. C4.5 - Decision Tree.ipynb		91. C4.5 - Decision Tree.ipynb
92. SVM.ipynb		92. SVM.ipynb
93. Neural Network.ipynb		93. Neural Network.ipynb
94. Convolutional Neural Network - 1 Layer.ipynb		94. Convolutional Neural Network - 1 Layer.ipynb
99. MNIST Classification using Tensorflow.ipynb		99. MNIST Classification using Tensorflow.ipynb
DecisionTree.csv		DecisionTree.csv
README.md		README.md
iris.csv		iris.csv

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