anwarcsebd/machine-learning-coursera

This repository contains Python Implementation of certain programming assignments of Andrew Ng’s Machine Learning Course on Coursera, created by Stanford University.

Machine Learning | Coursera | Implementation in Python

This repository contains Python Implementation of certain programming assignments of Andrew Ng’s Machine Learning Course on Coursera, created by Stanford University.

Tools Used

- python==3.7.3
- numpy==1.16.4
- matplotlib==3.1.0
- scipy==1.2.1
- jupyter==1.0.0
- jupyter-client==5.3.1

Data Used

Datasets provided as course materials under programming assignments section.

Reports of the exercises

Check out the solution reports and code of the certain assignment(Google Colaboratory Link)-

• Programming Exercise 1: Linear Regression

In this exercise, you will implement linear regression and get to see how it work on real world datasets.

• Programming Exercise 2: Logistic Regression

In this exercise, you will implement logistic regression and apply it to two different datasets.

• Programming Exercise 3: Multi-class Classification and Neural Networks

In this exercise, you will implement one-vs-all logistic regression and feedforward propagation for neural networks to recognize handwritten digits.

• Programming Exercise 4: Neural Network Learning

In this exercise, you will implement the backpropagation algorithm for neural networks and apply it to the task of hand-written digit recognition.

• Programming Exercise 5: Regularized Linear Regression and Bias vs Variance

In this exercise, you will implement regularized linear regression and polynomial regression and use it to study models with different bias-variance properties.

• Programming Exercise 6: Support Vector Machines

In this exercise, you will implement support vector machine (SVM) with Gaussian Kernels and you will be using support vector machines (SVMs) to build a spam classifier.

• Programming Exercise 7: K-means Clustering and Principal Component Analysis

In this exercise, you will implement the K-means clustering algorithm and apply it to compress an image. In the second part, you will use principal component analysis to find a low-dimensional representation of face images.

• Programming Exercise 8: Anomaly Detection and Recommender Systems

In this exercise, you will implement the anomaly detection algorithm and apply it to detect failing servers on a network. In the second part, you will use collaborative filtering to build a recommender system for movies.

References

[1] Machine Learning | Coursera | Stanford University

IMPORTANT NOTES

The class does not officially support python. If your goal is to get a certificate, the grades which you get should be based on completing the Matlab/Octave assignments. The python assignments are for learning purpose only.

Acknowledgements

• I would like to thank professor Andrew Ng and the team of the Stanford Machine Learning class on Coursera for such an awesome course.

• The re-written instructions in python starter code embedded within the Jupyter Notebook for submitting assignments for grading is based on dibgerge's python submission template of the assignments.