courseramachinelearning's Introduction

CourseraMachineLearning

You can either use matlab or octave to run codes

1. Linear and polynomial regression models

ex1.mx is the guide file, you can follow the instructions.

2. Logistic regression

ex2.mx is the guide file, you can follow the instructions.

3. Multi-class classification model and classifying digit image

ex3.mx is the guide file, you can follow the instructions.

4. Neural Networks

ex4.mx is the guide file, you can follow the instruction to build a Neural Network

5. Advice for Applying Machine Learning

ex5.mx is the guide file, you can follow the instruction to learn basic level tuning for your ML model.

6. Support Vector Machines

ex6.mx is the guide file you can follow the instruction to learn SVM and most importantly you'll develop a Spam Classifier.

7. Gaussian Distribution, Anomaly Detection and Recommendation Systems

ex7.mx is the guide file you can follow the instruction to apply K-Means and Principal Component Analysis. You can also learn dimensionality reduction and reconstruction of a sample. `ex8.mx' is the guid file you can follow the instruction to apply Anomaly Detection and to develop Recommender Systems.

In the first part of the exercise an Anomaly Detection Model is developed by using Gaussian Distribution and Threshold is defined by F1 Score.
In the second part of the exercise a Recommender System based on Collaborative Filtering is developed. Movie Lens 100K Dataset is used for the model.

Recommend Projects

aysebilgegunduz / courseramachinelearning Goto Github PK