These are the programming assignments(in ipython notebook) from Coursera's Machine Learning course "Machine Learning Foundations: A Case Study Approach" taught by University of Washington. Course Includes Study of:
- Linear Regression (one variable and multiple variables)
- Logistic Regression
- Support Vector Machine
- Regularization
- Clustering(K-means Algorithm,K-NN Algorithm)
- Anomaly Detection
- Recommender System(Personalized Recommendation Based on Collaborative Filtering)
- Deep learning(Neural networks)
Completed the course on machine learning with implementation using graphlab create(an optimized ML liberary for python) by university of washington for building the model of the following problems: • 1. Predicting House Prices: Build a Linear Regression model by learning the weights from training dataset. • 2. Sentiment Analysis on Amazon product reviews: Build a logistic regression model to predict classification of review as positive or negative. • 3. Retrieving Similar Wikipedia Articles: Build a model using k-nearest neighbors search and k-means clustering algorithm to find the similar articles. • 4. Song Recommendation System: Build a model using co-occurrence matrices for collaborative filtering to make personalized song recommendations. • 5. Deep Learning for Similar Images: Build a model using deep features to get similar images of animals.For ex- similar images of cat correspond to particular cat image.