https://github.com/urbanclimatefr/machine-learning-coursera-python

This repository contains python Implementation of certain programming assignments of Andrew Ng’s Machine Learning Course on Coursera, created by Stanford University.
https://github.com/urbanclimatefr/machine-learning-coursera-python

coursera machine-learning python

Last synced: about 2 months ago
JSON representation

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

• Host: GitHub
• URL: https://github.com/urbanclimatefr/machine-learning-coursera-python
• Owner: urbanclimatefr
• License: mit
• Created: 2021-03-07T13:50:01.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2021-03-07T13:57:18.000Z (almost 4 years ago)
• Last Synced: 2024-11-05T14:27:25.378Z (2 months ago)
• Topics: coursera, machine-learning, python
• Language: Jupyter Notebook
• Homepage:
• Size: 23.8 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# machine-learning-coursera-python

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

* 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.