This repository contains Python
Implementation of certain programming assignments of Andrew Ng’s Machine Learning Course on Coursera, created by Stanford University.
- 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
Datasets provided as course materials under programming assignments section.
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.
[1] Machine Learning | Coursera | Stanford University
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.
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I would like to thank professor Andrew Ng and the team of the Stanford Machine Learning class on Coursera for such an awesome course.
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The re-written instructions in
python
starter code embedded within the Jupyter Notebook for submitting assignments for grading is based ondibgerge
'spython
submission template of the assignments.