README.md

Machine Learning

Aditya Rana

[](https://shields.io/) ``` 1. Basics - python Sololearn app/website - https://www.sololearn.com/learning/1073 Can find free courses on Udacity or Coursera.

2. Other resources Deep learning specialization - https://www.coursera.org/specializations/deep-learning Applied data science - https://www.coursera.org/specializations/data-science-python

3. Journey Learnt python in school from several different resources and got started with deep learning specialization towards the end of 1st year.

## Mani Bansal
<a href="https://www.linkedin.com/in/mani-bansal/">
  <img align="left" width="82px" src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white"  />
</a>
[![Generic badge](https://img.shields.io/badge/Batch-2022-<color>.svg)](https://shields.io/)

Great Resources for learning AI and ML :

1. Free Quality courses to get started with :

Andrew NG : https://www.coursera.org/specializations/machine-learning-introduction

Kirill Eremenko : https://www.udemy.com/course/machinelearning/

2. Sites to keep track of the latest trends in AI and Machine Learning :

Analytics Vidhya : https://www.analyticsvidhya.com

Towards Data Science : https://towardsdatascience.com

3. Great Youtube Channels for Learning ML :

Statquest with Josh Starmer : https://www.youtube.com/@statquest

CodeBasics : https://www.youtube.com/@codebasics

Krish Naik : https://www.youtube.com/@krishnaik06

Yannic Kilcher : https://www.youtube.com/@YannicKilcher

## Shrinjoy Mitra
<a href="https://www.linkedin.com/in/shrinjoy-mitra-3449861a5/">
  <img align="left" width="82px" src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white"  />
</a>
[![Generic badge](https://img.shields.io/badge/Batch-2024-<color>.svg)](https://shields.io/)

1)Python Basics:- https://www.youtube.com/watch?v=rfscVS0vtbw

2)Data Structures and Algorithms:- https://www.youtube.com/watch?v=pkYVOmU3MgA

3)Machine Learning:- https://www.youtube.com/watch?v=GwIo3gDZCVQ

4)Deep Learning:- https://www.youtube.com/watch?v=CS4cs9xVecg&list=PLkDaE6sCZn6Ec-XTbcX1uRg2_u4xOEky0

5)Linear Algebra for ML:- https://www.youtube.com/watch?v=rSjt1E9WHaQ&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab

6)Kaggle Learn:- https://www.kaggle.com/learn

7)Datasets:- https://www.kaggle.com/datasets

Basics In Jose's Course. For more on Text Mining and NLP check out Applied Text Mining in Python course on Coursera by Michigan University.

Research Papers Look-UP : https://analyticsindiamag.com/8-open-access-resources-for-ai-ml-research-papers/

## Parikh Goyal
<a href="https://www.linkedin.com/in/parikh-goyal-errpv/">
  <img align="left" width="82px" src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white"  />
</a>
[![Generic badge](https://img.shields.io/badge/Batch-2022-<color>.svg)](https://shields.io/)

Neural Networks

1. Stanford lecture series by Andrej Karpathy (Neural networks): https://www.youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC

2. Hackerearth ML & DL monthly hackathons (Learn as you do)

3. NLP and GANs: https://github.com/ibrahimjelliti/Deeplearning.ai-Natural-Language-Processing-Specialization

4. Practice on Google Colab (Easy to use and experiment)

5. Tensorflow2-GPU easy installation: https://towardsdatascience.com/tensorflow-gpu-installation-made-easy-use-conda-instead-of-pip-52e5249374bc

## Jyoti prakash Rout
<a href="https://www.linkedin.com/in/jyoti-prakash-rout">
  <img align="left" width="82px" src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white"  />
</a>
[![Generic badge](https://img.shields.io/badge/Batch-2024-<color>.svg)](https://shields.io/)

100% free machine learning courses:

• MIT 6.S191 Introduction to Deep Learning
• DS-GA 1008 Deep Learning
• UC Berkeley Full Stack Deep Learning
• UC Berkeley CS 182 Deep Learning
• Cornell Tech CS 5787 Applied Machine Learning

Top-notch. Google them. Pick one. Finish it.

## Rohit Bishla
<a href="https://www.linkedin.com/in/rohit-bishla-6a3a68202/">
  <img align="left" width="82px" src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white"  />
</a>
[![Generic badge](https://img.shields.io/badge/Batch-2024-<color>.svg)](https://shields.io/)

Some good free courses. https://learndigital.withgoogle.com/digitalgarage/course/machine-learning-crash-course https://www.udacity.com/course/deep-learning-pytorch--ud188 https://www.udacity.com/course/intro-to-machine-learning--ud120 https://www.udacity.com/course/aws-machine-learning-foundations--ud065 https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187 https://www.udacity.com/course/machine-learning-unsupervised-learning--ud741 https://www.udacity.com/course/reinforcement-learning--ud600

## ML/DL Resources Contributor
<a href="https://github.com/Aujasyarajput18">
  <img align="left" width="82px" src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white"  />
</a>
[![Generic badge](https://img.shields.io/badge/Batch-2025-<color>.svg)](https://shields.io/)

Advanced ML/DL Resources

Large Language Models (LLMs)

• Hugging Face NLP Course: https://huggingface.co/course/chapter1/1
• Stanford CS224N: Natural Language Processing with Deep Learning: http://web.stanford.edu/class/cs224n/
• LLM University by Cohere: https://docs.cohere.com/docs/llmu
• Practical guides on fine-tuning LLMs: https://www.deeplearning.ai/short-courses/finetuning-large-language-models/
• OpenAI GPT best practices: https://platform.openai.com/docs/guides/gpt-best-practices

Transformers Architecture

• Illustrated Transformer by Jay Alammar: http://jalammar.github.io/illustrated-transformer/
• Attention Is All You Need (Original Paper): https://arxiv.org/abs/1706.03762
• The Annotated Transformer: http://nlp.seas.harvard.edu/annotated-transformer/
• Hugging Face Transformers Documentation: https://huggingface.co/docs/transformers/index
• Stanford CS25: Transformers United: https://web.stanford.edu/class/cs25/

Generative Adversarial Networks (GANs)

• GAN Lab - Interactive Visualization: https://poloclub.github.io/ganlab/
• Stanford CS236: Deep Generative Models: https://deepgenerativemodels.github.io/
• GAN Specialization by DeepLearning.AI: https://www.coursera.org/specializations/generative-adversarial-networks-gans
• PyTorch GAN Tutorial: https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html
• Papers with Code - GANs: https://paperswithcode.com/methods/category/generative-adversarial-networks

Neural Network Architectures

• Deep Learning Architectures Visual Guide: https://www.asimovinstitute.org/neural-network-zoo/
• ResNet Paper (Deep Residual Learning): https://arxiv.org/abs/1512.03385
• EfficientNet: Rethinking Model Scaling: https://arxiv.org/abs/1905.11946
• Vision Transformers (ViT): https://arxiv.org/abs/2010.11929
• CNN Explainer - Interactive Visualization: https://poloclub.github.io/cnn-explainer/

Additional Resources

• Papers with Code: https://paperswithcode.com/
• Arxiv Sanity Preserver: http://www.arxiv-sanity.com/
• Distill.pub (Visual ML explanations): https://distill.pub/
• Two Minute Papers (YouTube): https://www.youtube.com/@TwoMinutePapers

  
<!--
-->