This is my solution to all the programming assignments and quizzes of Machine Learning (ML) from Stanford University at Coursera taught by Andrew Ng. After completing this course you will get a broad idea of ML algorithms. Try to solve all the assignments by yourself first, but if you get stuck somewhere then feel free to browse the code. You can find my certificate online or you can find it in the certificates/online/coursera/ folder inside the about repository.
The following list is showing the required dependencies for this project to run locally:
Here are some tutorials and documentation, if needed, to feel more comfortable about using and playing around with this repository:
Usage follow the instructions here to setup the current repository and extract the present data. To understand how the hereby repository is used for, read the following steps.
At this point, the only way to install this repository is manual. The instructions below are meant for Unix systems. More precisely, the instructions are meant to macOS.
Nonetheless, this kind of installation is as simple as cloning this repository. Virtually all Git and GitHub version control tools are capable of doing that. Through the console, we can use the command below, but other ways are also fine.
git clone https://github.com/FMCalisto/machine-learning-stanford-coursera.gitPlease, feel free to try out any of our exercises/ sampling. For instance, you can start by a file called ex1.m at the exercises/machine-learning-ex1/ex1/ directory. It can be used as follows:
$ cd exercises/machine-learning-ex1/ex1/
$ octaveNow, inside the octave console do:
octave:1> ex1
We need to follow the repository goal, by addressing the thereby information. Therefore, it is of chief importance to scale this solution supported by the repository. The repository solution follows the best practices, achieving the Core Infrastructure Initiative (CII) specifications.
Besides that, one of our goals involves creating a configuration file to automatically test and publish our code. It will be most likely prepared for the GitHub Actions. Other goals may be written here in the future.
This project exists thanks to all the people who contribute. We welcome everyone who wants to help us improve this downloader. As follows, we present some suggestions.
Either as something that seems missing or any need for support, just open a new issue. Regardless of being a simple request or a fully-structured feature, we will do our best to understand them and, eventually, solve them.
We like to develop, but we also like collaboration. You could ask us to add some features... Or you could want to do it yourself and fork this repository. Maybe even do some side-project of your own. If the latter ones, please let us share some insights about what we currently have.
The current information will summarize important items of this repository. In this section, we address all fundamental items that were crucial to the current information.
The present repository is under the terms of MIT and the hereby information is covered by this. You are free to make changes and use this in either personal or commercial projects. Attribution is not required, but it is welcomed. A little "Thanks!" (or something to that affect) would be much appreciated.
Our team brings everything together sharing ideas and the same purpose, developing even better work. In this section, we will nominate the full list of important people for this repository, as well as respective links.
- Francisco Maria Calisto [ Website | ResearchGate | GitHub | Twitter | LinkedIn ]
- Bruno Oliveiras
- Luís Ribeiro Gomes
Our organization is a non-profit organization. However, we have many needs across our activity. From infrastructure to service needs, we need some time and contribution, as well as help, to support our team and projects.
This project exists thanks to all the people who contribute. [Contribute].
Thank you to all our backers! 🙏 [Become a backer]
Support this project by becoming a sponsor. Your logo will show up here with a link to your website. [Become a sponsor]
