Let's Contribute To Open-source

Hacktoberfest, in its 8th year, is a month-long celebration of open source software run by DigitalOcean. During the month of October, we invite you to join open-source software enthusiasts, beginners, and the developer community by contributing to open-source projects.

Completing the Challenge

If you have previously never contributed to any open-source software then these steps will help you get started:

1. Go to Hacktoberfest official website and sign in there using your GitHub.
2. Install git and setup in your computer. Download and install it from here.
3. Fork this repository by click the Fork button in the top right of this page or simply click here.
4. Once it is forked, clone the repository in your computer. For this, copy the URL in the address bar, and use the following command:

git clone url_you_just_copied

4. Open this cloned repository in your preferred code editor. Also, open a terminal in this directory.
5. Now type in the following command in the terminal and replace username with your GitHub username.

git checkout -b username

6. Fill this block with necessary info of yourself.

    {
      "name": {YOUR_NAME},
      "batch": {YOUR_BATCH_COMMENCEMENT_YEAR},
      "major": {YOUR_DEPARTMENT},
      "githubUsername": {YOUR_GITHUB_USERNAME},
      "favoriteLanguage": {YOUR_FAVOURITE_PROGRAMMING_LANGUAGE}
    }

7. Now add the above filled block to the array in profiles.json file

8. Once you have done all this, commit your changes to GitHub. You can do this with the following commands. Make sure you execute them in the precise order one after another in your terminal.

# copy and paste the following in the terminal
git add .

# copy and paste the following in the terminal after you have executed the previous command
git commit -m "hacktoberfest contribution"

# copy and paste the following in the terminal after you have executed the previous command
git push -u origin your_github_username

9. Now open the forked repository on your GitHub. You will see a yellow box at the top telling you that some changes are pushed. You will also see a button called Compare & pull request. Click on it.
10. Now add a title, some description! You have opened a pull request in this repository.

You need to open four valid pull requests in order to complete the challenge. If you have performed the above steps, you have already opened one pull request. And you need only three more.

Note: Those repositories who have hacktoberfest as a label are considered for Hacktoberfest challenge only.

Get Published With AI

AI is a world’s leading multidisciplinary science Industry and the future of computing. Here I publish the best and free resources related to AI that have been suggested and read by thought-leaders and decision-makers around the world.

Why should you contribute in this OpenSource Project ?

• Because in AI Industry, your audience will be larger and we’ll make sure to spread the word not only on our social media channels, but our all networks as much as we could. We have seen a high engagement rate with high-quality articles.
• You'll get to know the best and free resources including latest articles, news and be able to download resources
• Grow with our community and be able to get feedback as necessary.

Clearing the Confusion: AI vs Machine Learning vs Deep Learning Differences

Raise your hand if you’ve been caught in the confusion of differentiating artificial intelligence (AI) vs machine learning (ML) vs deep learning (DL).Bring down your hand, buddy, we can’t see it! Although the three terminologies are usually used interchangeably, they do not quite refer to the same things.

Andrey Bulezyuk, who is a German-based computer expert and has more than five years of experience in teaching people how artificial intelligence systems work, says that “practitioners in this field can clearly articulate the differences between the three closely-related terms.”

Therefore, is there a difference between artificial intelligence, machine learning, and deep learning?

Here is an image that attempts to visualize the distinction between them:

From above image, you can see DL is a subset of ML, which is also a subset of AI. Interesting, right?

So, AI is the all-encompassing concept that initially erupted, then followed by ML that thrived later, and lastly DL that is promising to escalate the advances of AI to another level. If you want to learn more then click here.

FREE AI COURSES:

• EdX’s Artificial Intelligence
• Udacity’s Intro to Artificial Intelligence
• Artificial Intelligence: Principles and Techniques By Stanford
• Udacity’s Artificial Intelligence for Robotics by Georgia Tech
• IBM's Data Science and Cognitive Computing courses
• MIT Deep Learning
• Elements of AI

FREE MACHINE LEARNING COURSES:

• Machine Learning by Andrew NG
• Intro to ML by Udacity
• EdX’s Learning from Data(Introductory Machine Learning
• Introduction to Machine Learning for Coders
• Statistical Machine Learning by CMU
• Coursera’s Neural Networks for Machine Learning
• Kaggle Complete Roadmap for Machine Learning
• EdX’s Principles of Machine Learning
• Coursera’s Machine Learning Specialization
• Machine Learning Crash Course by Google
• Machine Learning With Python By FreeCodeCamp
• Best Practices for ML Engineering
• Machine Learning for Everyone By DataCamp
• Machine Learning by EdX's (Columbia University)
• Machine Learning by EdX's (The University of Texas)
• Machine Learning Crash Course by Google
• Machine Learning Course at W3Schools
• Intro to Machine Learning Course at Kaggle
• Machine Learning Certification For Beginners By Analytics Vidhya

FREE DATA SCIENCE COURSES:

• Introduction to Data Science
• Applied Data Science with Python
• IBM Data Science Professional Certificate
• Data Science Training Videos
• Data Analysis in Python and Pandas
• Data Analysis in Python by RMOTR and FreeCodeCamp

DATASET REPOSITORIES:

• Data World
• Google Data Sets
• Kaggle Data Sets

COMPETITION PLATFORMS

• Numerai
• Tianchi
• Omdena
• HackerEarth
• aicrowd

FREE DEEP LEARNING COURSES:

• Google’s Deep Learning Course
• Practical Deep Learning for Coders
• Deep Learning from the Foundations
• Introduction to Deep Learning
• Deep Sequence Modelling
• Deep Learning for Computer Vision
• Deep Generative Models
• Deep Learning Basics
• Deep Learning tutorial
• Deep Learning with TensorFlow

DEEP EARNING BY MIT

Introduction to Deep Learning

Deep Sequence Modelling

Deep Learning for Computer Vision

Deep Generative Models

Deep Reinforcement Learning

Limitations and New Frontiers

STATISTICAL SOFTWARE FOR BEGINNERS

• Simple Linear Regression
• Coding Dummy Variables
• Forecasting New Observations
• Forecasting a Large Number of Observations
• Logistic Regression

FREE NLP COURSES:

• A Code-First Introduction to Natural Language Processing
• Natural Language Processing and Capstone Assignment
• Natural Language Processing with Probabilistic Models

FREE MACHINE LEARNING IN GRAPHICS AND VISION COURSES

• CVPR 2020: Neural Rendering

AI RESEARCH AT BIG COMPANIES:

• Machine Learning at Apple
• AI at Uber
• Machine Learning at Careem
• Data Science at Grab
• Autopilot AI at Tesla
• AI at Microsoft
• AI Research at Google
• Self Driving Car Research at Lyft
• AI Research at Huawei
• AI Research at Samsung
• AI at Alibaba
• Data Science at Gojek
• Intelligent Transportation Technology and Security at Didi Chuxing
• Amazon Science
• Data Science at Bolt

DEVELOPER RESOURCES:

• Apple Resources
• Google Resources
• Facebook Resources

AI CHEAT-SHEETS:

• Best of AI Cheat-Sheets
• Stanford CS229 Machine Learning
• Stanford CS230 Deep Learning
• Stanford CS221 Artificial Intelligence
• Collection of AI Cheat-Sheets

YOUTUBE CHANNELS

• MIT's Computer Science and AI Laboratory
• The Allen Institute for Artificial Intelligence
• DeepMind
• Applied AI Course
• StarCraft Artificial Intelligence Tournament
• Sentdex's Data Analysis Tutorials
• Amazon Machine Learning University

Course Downloads

Courses	School	Duration	Effort	Frequency	Prerequisites
Machine Learning Engineer Nanodegree	Udacity	3 months	10 hours / week	self-paced	Intermediate Python & ML
AI for Healthcare Nanodegree Program	Udacity	4 months	15 hours / week	self-paced	Intermediate Python & ML
Intel® Edge AI for IoT Developers Nanodegree Program	Udacity	3 months	10 hours / week	self-paced	Intermediate Python & DL
AI Product Manager Nanodegree	Udacity	2 months	2-5 hours / week	self-paced	none
Data Engineering Nanodegree	Udacity	5 months	5 hrs / week	self-paced	Intermediate Python & SQL

Contribution Guideline:

Feel free to open a PR if you feel like something needs to be added or you want to suggest something then your commit message should be in given format: added to -->resource_name-->section_name

🌟 Please star the repo so that it gets maximum exposure and more people can benefit from it!

Terms & Conditions:

• Please submit unpublished drafts with at least some Intresting Free resources anout AI.
• We do not accept plagiarism. You may reference text from other sources, but it must be referenced ([1][2] and so on), and it cannot exceed 10% of an author’s content. Otherwise, we will reject your article.
• Try to captivate your audience with a nice image — open-source images can be found at Pixabay, Unsplash, StockSnap, Flickr, Pexels, Burst, The Stocks.
• Make sure your story has meaning — give more than you get.
• No heavy self-promotion please, you can talk about your business, ventures, and others, yet, make sure that your audience stays on-board, and we prefer it to be on the bottom of the article after you have shared your insights and work.
Please utilize a grammar and readability tool such as Grammarly. If your article has too many grammatical errors, it won’t be accepted.

Important Notice:

All product names, logos, and brands are property of their respective owners. All company, product and service names used in this repository are for identification purposes only. Use of these names, logos, and brands does not imply endorsement.