diff --git a/ML/README.md b/ML/README.md
index 4ee4747..a8a491c 100644
--- a/ML/README.md
+++ b/ML/README.md
@@ -1,19 +1,19 @@
- > Use this format to add your own ML resources (those that were personally used by you) in this README
- ```
+
+ # Machine Learning
@@ -26,8 +26,7 @@
-
-[](https://shields.io/)
+[](https://shields.io/)
```
1. Basics - python
Sololearn app/website - https://www.sololearn.com/learning/1073
@@ -39,226 +38,102 @@ Applied data science - https://www.coursera.org/specializations/data-science-pyt
3. Journey
Learnt python in school from several different resources and got started with deep learning specialization towards the end of 1st year.
-
-4. Blogs
-Medium - https://medium.com/
-Towardsdatascience - https://towardsdatascience.com/
```
-## Vibhu Chandransh Bhanot
-
+
+## Mani Bansal
+
+[](https://shields.io/)
+```
+Great Resources for learning AI and ML :
-[](https://shields.io/)
-
+1) Free Quality courses to get started with :
-```
-Exploratory data analysis
-There are a lot of online tutorials out there but I used the following which worked pretty well for me:
-1) Python Data Science Handbook by Jake VanderPlas
-Chapters: 2,3 in detail and initial part of ch 4….(to start with numpy, pandas and matplotlib)
-https://tanthiamhuat.files.wordpress.com/2018/04/pythondatasciencehandbook.pdf
-2) Introduction to data science with python coursera (University of Michigan)...(used to practise pandas)
-https://www.coursera.org/learn/python-data-analysis
-
-Machine Learning Algorithms
-Used parallel combination of Hands-On ML with scikit-learn,keras & TF (for checking out implementation and basic theory) and Andrew Ng stanford coursera course (for deeper parts of theory)
-https://www.coursera.org/learn/machine-learning.
-Often I hand-coded ML Algos implementations and tried out visualizing Decision Boundaries. My ML algos repo link: https://github.com/Vibhu1710/ML-Algos-Implementation
-Deep Learning
-Again used Hands-On ML for Deep Learning and parallely referred to Andrew Ng deep learning course https://www.coursera.org/learn/neural-networks-deep-learning?specialization=deep-learning
-Back Prop article link that i found handy:
-https://www.jeremyjordan.me/neural-networks-training/
-Started out implementing shallow neural networks and understood how they worked in steps (epochs, what is the batch size etc).
-
-Convolutional Neural network
-I guess the best explanation and theory for this is here:
-https://www.coursera.org/learn/convolutional-neural-networks?specialization=deep-learning
-Implementation is a bit of a slow process here. Some articles that were stepping stones:
-Transfer Learning implementation:
-https://towardsdatascience.coma-comprehensive-hands-on-guide-to-transfer-learning-with-real-world-applications-in-deep-learning-212bf3b2f27a
-At this point I found myself searching for implementations on kaggle.
-One of the starter CNN basic level implementation to check out is this:
-https://www.kaggle.com/uysimty/keras-cnn-dog-or-cat-classification
-```
-## Vikram Mondal
-
-
-
+Andrew NG : https://www.coursera.org/specializations/machine-learning-introduction
-[](https://shields.io/)
-```
-https://www.kaggle.com/
-https://www.codingninjas.com/?referralCode=JRQLA
+Kirill Eremenko : https://www.udemy.com/course/machinelearning/
-```
-## Aayush Makkar
-
-
-
+2) Sites to keep track of the latest trends in AI and Machine Learning :
-[](https://shields.io/)
-```
-Andrew NG’s stanford machine learning course to study in depth mathematics in machine learning. Go for as many guided projects as possible from coursera, pick up any random course for specific topic from udemy or go for complete ml track from Coding ninjas. After you are confident enough, start doing projects from kaggle. Random datasets can be found on kaggle,anazon datasets, UcI ml depository, etc.
-```
-## Pranab Jain
-
-
-
+Analytics Vidhya : https://www.analyticsvidhya.com
-[](https://shields.io/)
-```
-First Phase (Mostly Theory)
-1. Linear Algebra
-https://www.coursera.org/learn/linear-algebra-machine-learning
-2. Calculus
-https://www.coursera.org/learn/multivariate-calculus-machine-learning
-3. Statistics
-https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2
-4. Algorithms
-https://www.coursera.org/learn/algorithms-part1
-https://www.coursera.org/learn/algorithms-part2
-Second Phase (Mostly Practical)
-5. Data Science & Python
-https://www.coursera.org/learn/python-data-analysis?specialization=data-science-python
-6. Data Visualization
-https://www.coursera.org/learn/python-plotting?specialization=data-science-python
-7. Machine Learning
-https://www.coursera.org/learn/machine-learning (After this you can start kaggle competitions)
-8. Deep Learning
-https://www.coursera.org/specializations/deep-learning
-```
-## Komal
+Towards Data Science : https://towardsdatascience.com
-[](https://shields.io/)
-```
-I started learning machine learning from coursera Introduction to machine learning course by andrew ng. Then udemy AtoZ Machine learning course. After this one can do project oriented courses or individual/team ML projects. Kaggle is best place to start.
-```
-## Hamza Ali Rizvi
-
-
-
+3) Great Youtube Channels for Learning ML :
-[](https://shields.io/)
-```
-1) Learn Python3 the Hard Way by Shaw
-2) Pandas, Matplotlib and Numpy courses on Kaggle.
-3) Datacamp course on Introduction to Machine Learning.
-4) Deep Learning with Tensorflow specialisation on Coursera by Deeplearning.ai
-5) Computational Linear Algebra by fast.ai
-6) Statquest YouTube channel by Josh Starmer
-```
-## Shubh Ashish
-
-
-
+Statquest with Josh Starmer : https://www.youtube.com/@statquest
-[](https://shields.io/)
+CodeBasics : https://www.youtube.com/@codebasics
+
+Krish Naik : https://www.youtube.com/@krishnaik06
+
+Yannic Kilcher : https://www.youtube.com/@YannicKilcher
```
-Exploratory data analysis :
-Loads of tutorials are present online. I followed this one:
-https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/
-
-Machine Learning Algorithms :
-Followed Jose Portilla’s (above) and Andrew NG’s courses:
-https://www.coursera.org/learn/machine-learning
-Josh Starmer’s wonderful explanation for topics like PCA, clustering, etc. helped, too:
-https://www.youtube.com/c/joshstarmer
-Tried implementing everything.
-
-Deep Learning :
-Again, Andrew NG’s Deep Learning specialization:
-https://www.coursera.org/learn/neural-networks-deep-learning?specialization=deep-learning
-And loads of towardsdatascience.com articles.
-
-Convolutional Neural Networks :
-Andrew NG, again:
-https://www.coursera.org/learn/convolutional-neural-networks?specialization=deep-learning
-Tried implementing stuff, with help of towardsdatascience.com articles, every now and then. Did the generic MNIST classification and tried a hand on the Kaggle Dog-vs-Cat dataset.
-
-Recurrent Neural Networks :
-Andrew NG’s got you covered:
-https://www.coursera.org/learn/nlp-sequence-models?specialization=deep-learning
-This, of course, was followed by hands-on implementation, with article reading from towardsdatascience.com and analyticsvidhya.com
-
-Natural Language Processing :
-Got some ideas from Jose’s course. Read many articles. Implemented and understood stuff like CountVectorization.
-Did a basic task of sentiment analysis. (Many datasets on Kaggle.)
-```
-## Bavesh Kumar
-
+
+## Shrinjoy Mitra
+
-
-[](https://shields.io/)
+[](https://shields.io/)
```
-Exploratory data analysis :
-https://www.coursera.org/learn/python-data-analysis
-https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/
-(first few modules cover data analysis and visualization part)
-https://www.kaggle.com/search?q=tag%3A%22exploratory+data+analysis%22 (Practice)
-
-Machine Learning Algorithms :
-To Learn Machine Learning Underlying concepts and mathematics follow
-Andrew NG course (Stanford University) on Coursera.
-https://www.coursera.org/learn/machine-learning
-(but it’s in matlab so just learn concepts and to apply algorithms using python follow Jose Portialla’s course)
-https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/
-
-Scratch Implementation of ML algorithms
-(https://github.com/eriklindernoren/ML-From-Scratch/tree/master/mlfromscratch)
-
-Edureka and Codebasics channel on youtube can also help!!
-
-Deep Learning :
-
-DeepLearning.AI’s Deep Learning Specialization covers almost everything.
-Specialization Link
-1st Course:- Build a neural Network from scratch
-2nd Course:- Improving bare minimum model by tuning
-3rd Course:- Best practices to follow in DL/ML.
-4th Course:- Convolution Neural Nets
-5th Course:- Recurrent Neural Nets.
-Specialization has tons of assignments and projects so go for it.
-To implement DL with Python you can go for Tensorflow it’s documentation is extremely great.
-(https://www.tensorflow.org/)
-
-Towardsdatascience.com, Medium.com, MachineLearningMastery all are always there to help with the implementation part.
-
-Natural Language Processing :
-Basics In Jose’s Course.
+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
-
-[](https://shields.io/)
+[](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
+
3) Practice on Google Colab (Easy to use and experiment)
+
4) Tensorflow2-GPU easy installation: https://towardsdatascience.com/tensorflow-gpu-installation-made-easy-use-conda-instead-of-pip-52e5249374bc
```
+
## Jyoti prakash Rout
-
-[](https://shields.io/)
+[](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
@@ -266,14 +141,13 @@ https://github.com/ibrahimjelliti/Deeplearning.ai-Natural-Language-Processing-Sp
- Cornell Tech CS 5787 Applied Machine Learning
Top-notch. Google them. Pick one. Finish it.
-
```
+
## Rohit Bishla
-
-[](https://shields.io/)
+[](https://shields.io/)
```
Some good free courses.
https://learndigital.withgoogle.com/digitalgarage/course/machine-learning-crash-course
@@ -283,6 +157,50 @@ 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
+
+
+
+[](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
+```
+
+