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machine-learning-coursera

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

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# 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](https://colab.research.google.com/github/anwarcsebd/machine-learning-coursera/blob/master/machine-learning-ex1/exercise1.ipynb)
> In this exercise, you will implement linear regression and get to see how it work on real world datasets.

*  Programming Exercise 2: [Logistic Regression](https://colab.research.google.com/github/anwarcsebd/machine-learning-coursera/blob/master/machine-learning-ex2/exercise2.ipynb)
> In this exercise, you will implement logistic regression and apply it to two different datasets.

*  Programming Exercise 3: [Multi-class Classification and Neural Networks](https://colab.research.google.com/github/anwarcsebd/machine-learning-coursera/blob/master/machine-learning-ex3/exercise3.ipynb)
> 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](https://colab.research.google.com/github/anwarcsebd/machine-learning-coursera/blob/master/machine-learning-ex4/exercise4.ipynb)
> 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](https://colab.research.google.com/github/anwarcsebd/machine-learning-coursera/blob/master/machine-learning-ex5/exercise5.ipynb)
> 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](https://colab.research.google.com/github/anwarcsebd/machine-learning-coursera/blob/master/machine-learning-ex6/exercise6.ipynb)
> 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](https://colab.research.google.com/github/anwarcsebd/machine-learning-coursera/blob/master/machine-learning-ex7/exercise7.ipynb)
> 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](https://colab.research.google.com/github/anwarcsebd/machine-learning-coursera/blob/master/machine-learning-ex8/exercise8.ipynb)
> 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](https://www.coursera.org/learn/machine-learning)

### 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](https://github.com/dibgerge/ml-coursera-python-assignments) `python` submission template of the assignments.

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