This is an AI Series where we will cover Machine Learning and Deep Learning topics from the very basics.
All the material and codes of this series are in there respective branches.
View Demo
·
Report Bug
·
Request Feature
Imagine looking at a graph to explore your data. In two dimensions everything makes sense. In three it gets complex, but how do we look at data that has 200 dimensions? It would happen to you that you have to read a thousand documents in a week, you couldn't handle it.
What these things have to do with AI? AI used to be a fanciful concept from science fiction, but now it’s becoming a daily reality. AI can mean different things in different contexts for different people. For data scientists, it is a good excuse to build cool stuff whereas for end-users AI means to ease the workload. So there's a good reason to be enthusiastic about AI and that brings the next question to all of us "How do I get started to build a career in Data Science, AI, or ML? Don't you worry! We have got you covered.
So, coming to the point, what you will get to learn from this series. We will be covering all the domains and concepts under the umbrella of Artificial intelligence. This series is going to have multiple courses, Numpy, Pandas, Seaborn, Matplotlib, Scipy, Dataset Generation, Web Scraping, Data preprocessing, Big Data, Deep learning and much much more!
To get going with the repo and AI concepts we expect you to follow all the topics in the given manner, which eventually will lead to end of the series.
- Data Manipulation and Visualization
- Dataset Generation, Web Scraping, Data preprocessing
- Machine Learning
The only pre-requisite required for this course is python basics
The following links contain the usage examples of that respective session.(All the links will become available as we gradually progress)
- Data Manipulation and Visualization
- Dataset Generation, Web Scraping, Data preprocessing
- Machine Learning
To propose or request any feature as well as to report any error in the given material and code please create a issue in this repository.
How to create an issue :
- Under this repository name, click Issues.
- Click New issue.
- If there are multiple issue types, click Get started next to the type of issue you'd like to open.
- Optionally, click Open a blank issue. if the type of issue you'd like to open isn't included in the available options.
- Type a title and description for your issue.
- When you're finished, click Submit new issue.
For more details visit this page
See the open issues for a list of proposed features (and known issues).
- Find a project you want to contribute to
- Fork it
- Clone it to your local system
- Make a new branch
- Make your changes
- Push it back to your repo
- Click the Compare & pull request button
- Click Create pull request to open a new pull request If the reviewers ask for changes, repeat steps 5 and 6 to add more commits to your pull request.
If you feel to add any piece of code or something extra which is not there in the given material or if you want to request any feature you can raise an issue. Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated by ISTE-VESIT.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.
ISTE-VESIT - @istevesit.org - [email protected]
Project Link: https://github.com/ISTE-VESIT-ORG/Machinera-2020