MLStudyJams2022-23

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MLStudyJams2022-23

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This is the repository for all the resources (code, notes and guides) used during the ML Study Jams 2022-23 program hosted at GDSC-TIU. (Maintainer: Aryan Pareek @diffrxction)

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# :star: Welcome to GDSC-TIU's ML Study Jams 2022! :star:

Hi everyone! A very warm welcome to the ML Study Jams 2022. This is the repository for all the resources that we create and learn from during our ML Study Jams program. And this README is a guide for you students to get familiar with the material as well as use the GitHub website to access resources and ask doubts here itself.

## Files
The files are divided into seperate weekly folders and you can access the session resources there.

Download or click on the pdf file to view the notes for the session.

- [Day 01](https://github.com/dsc-tiu/MLStudyJams2022/tree/main/Intro%20to%20ML%20Study%20Jams%20Day%201%20Material)
- [Day 02](https://github.com/dsc-tiu/MLStudyJams2022/tree/main/ML%20Study%20Jams%20Day%2002%20Classification)
- [Day 03](https://github.com/dsc-tiu/MLStudyJams2022/tree/main/ML%20Study%20Jams%20Day%2003%20ML%20Implementation%20and%20Accuracy%20Measures)
- [Day 04](https://github.com/dsc-tiu/MLStudyJams2022/tree/main/ML%20Study%20Jams%20Day%2004%20Regression%20Implementation%20and%20Error%20Terminology)

## Roadmap

The roadmap for the event is as follows:
1. Week 1
    - Introduction to Machine Learning [Kaggle Course Link](https://www.kaggle.com/learn/intro-to-machine-learning)
    - Pandas [Kaggle Course Link](https://www.kaggle.com/learn/pandas)

2. Week 2
    - Intermediate Machine Learning [Kaggle Course Link](https://www.kaggle.com/learn/intermediate-machine-learning)
    - Feature Engineering [Kaggle Course Link](https://www.kaggle.com/learn/feature-engineering)
  
3. Week 3
    - Introduction to deep learning [Kaggle Course Link](https://www.kaggle.com/learn/intro-to-deep-learning)
    - Computer vision [Kaggle Course Link](https://www.kaggle.com/learn/computer-vision)
  
4. Week 4
    - Time Series [Kaggle Course Link](https://www.kaggle.com/learn/time-series)
    - Intro to AI Ethics [Kaggle Course Link](https://www.kaggle.com/learn/intro-to-ai-ethics)

5. Week 5
    - Cover up any pending modules and allow for course completion on Kaggle.
    - Doubts and discussion on the road after this event.

## Kaggle course links

The Kaggle links are provided in the roadmap above.

## Creating a Kaggle account

Here is a step by step guide on how to register an account and create a profile on Kaggle. Please remember that Kaggle only allows one profile per user.

1.  Go to the Kaggle website  and click on sign up section.
    
2.  Setup your Kaggle profile by providing a brief bio, picture, location, title and current workplace. You can also add your LinkedIn, GitHub and Facebook accounts so people can contact you.
    
Joining Kaggle is a straightforward process that requires very little time and effort. As a Kaggler, you have access to unlimited data sets, Kaggle competitions, and the Kaggle discussion forum. You can also follow strong Kaggle users to learn from.

**P.S. You will have to create a Kaggle account to complete the free courses and at the end of the program, please share your Kaggle profiles in a Google form that will be shared later on to guarantee your GDSC certifications**

## Doubt solving and Discussions panel

In order to effectively communicate and get your doubts solved, please use the `Discussions` section for this repository. There, you can ask questions and tag my GitHub account @diffrxction and I will try to cover your doubts within the same week. Please do not hesitate when asking for help.

Here's the [link](https://github.com/dsc-tiu/MLStudyJams2022/discussions) to the Discussions panel and here's a [quick guide](https://github.com/dsc-tiu/MLStudyJams2022/discussions/2) on how to ask doubts and engage with fellow community members.

> "True guidance is like a small torch in a dark forest. It doesn't show everything once. But gives enough light for the next step to be safe." - Swami Vivekananda

## FAQs

**Q1. How long will this event last?**

A. This event is planned to last 4 weeks but we may extend it to 5 weeks as the later portions involving deep learning and computer vision need some thorough explanation.

**Q2. How will I get my certificates?**

A. After the completion of a course on Kaggle,

For the Kaggle certificates, 
You can find your certificates by navigating to the Courses homepage: [https://www.kaggle.com/learn](https://www.kaggle.com/learn).

-   Click on the **[Your Completed]** tab.
-   Use the **[View Certificate]** button to get your certificate!

If you'd like to share the certificate with your social network, you can begin by downloading the image with the **[Download certificate]** button that appears at the bottom of your certificate.

You will get a certificate for each course that you complete and all the courses are free.

**For the GDSC certificate**, after the completion of all four weeks, you will be asked to submit your Kaggle profile links in a Google form that will be shared later and you will get **a certificate** verifying your participation in the event and we also have **swags** for good consistency throughout the four weeks.

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#### P.S.  In case you use this repository and any material here is of some help to you, it is a kind request to star it as that will help us grow and reach out to a larger audience group. :heart:
Thank you. Happy learning :smiley:

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Maintainer: Aryan Pareek [(@diffrxction)](https://github.com/diffrxction) , ML Lead, GDSC-TIU

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