MACHINE LEARNING

Coursera - Machine Learning by Andrew Ng

Notebooks on :

Anomaly Detection

Bias-Variance plot - how they look like before and after training

Collaborative filtering and Recommender system - Used IMDB database dataset

Digit Identification using Logistic Regression - Image processing

K-Means

Neural Network - Handwritten Digit Identification

PCA - Facial features based recognition/Computer vision

Regularized Logistic Regression - Nelder Mead Method

SVM - Spam detection

Name		Name	Last commit message	Last commit date
Latest commit History 20 Commits
Anomaly+Detection.ipynb		Anomaly+Detection.ipynb
Bias+vs+Variance.ipynb		Bias+vs+Variance.ipynb
Collaborative+filtering+and+recommender+systems.ipynb		Collaborative+filtering+and+recommender+systems.ipynb
Digit+Identification+using+Logistic+Regression.ipynb		Digit+Identification+using+Logistic+Regression.ipynb
Kmeans.ipynb		Kmeans.ipynb
Logistic+Regression (1).ipynb		Logistic+Regression (1).ipynb
Neural Network T&P.ipynb		Neural Network T&P.ipynb
Neural_Network.ipynb		Neural_Network.ipynb
PCA.ipynb		PCA.ipynb
README.md		README.md
Regularised+Logistic+Regression-NelderMead.ipynb		Regularised+Logistic+Regression-NelderMead.ipynb
Regularised+Logistic+Regression.ipynb		Regularised+Logistic+Regression.ipynb
Support+Vector+Machines+%26+Spam+Mail+Classification.ipynb		Support+Vector+Machines+%26+Spam+Mail+Classification.ipynb
_machine_learning.py		_machine_learning.py