[Workshop] Machine Learning and It's Dimensions
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Description
# Title
Introduction To Machine Learning and Deep Learning
**Type**
- [x] Workshop / Hands on
- [ ] Guest Lecture
- [ ] Webinar
**Abstract**
An introductory workshop on Machine Learning where we'll be visualizing and training our ML models from scratch.
**About**
We'll be starting our session with Introduction to machine learning. Then we'll move to various machine learning models and will visualize them in the simplest and most fun way.
Then I'll be presenting how one can easily write their ML models from scratch. This will be a hands-on session where beginners can easily learn.
After this, I'll be introducing the audience to Deep Learning and Neural Networks. Also, I'll be showing the audience my research that I'm currently working on, "Forecasting Earthquakes with Neural Networks."
**Pre-requisites**
Basics of Python
- _Required setups_
- Projector
-Internet
- Laptop (jupyter notebook and Anaconda installed)
- Speakers
**Expected duration**
1.5hour
**Level**
Beginner
**Speaker Bio**
I'm a fourth-year undergrad student at GB Pant Government Engineering College, currently pursuing my B.tech in Electronics and Communication Engineering. I'm a Deep Learning and Robotics enthusiast.
_Write a brief description about you here_
**Social Links to your profile**
https://www.linkedin.com/in/shivang-singh-kaira-989706138/
###### - Can be done after the talk/workshop -
[Include link to slides here](link)
[Include link to the video here](link)
**Month**
_A month in which you will be available to give the talk_
August
Contributor guide
Research direction
No repository files, tests, or entry points are mentioned; start by reviewing the event-proposal requirements and the stated August availability. Completion is not defined beyond the proposed workshop and optional slide/video links, so confirm the maintainer's intended next step before taking this on.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- anaconda, jupyter-notebook, machine-learning, python
- Domain
- content, machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100