Machine Learning and Deep Learning Archive Notes

In this archive, I want to cover various topics, started from the list below:

Foundations

• Numpy Library
• Matplotlib Library
• Panda Library

Supervised Learning

• Linear Regression
• Gradient Descent
• Logistic Regression
• Decision Trees
• Random Forest
• XGBoost

Unsupervised Learning

• K-Means Clustering
• Anomaly Detection

Deep Learning Foundations

• Neural Networks Basics
• Backpropagation
• Activition Functions
• Multiclass Classification
• Transfer Learning
• Regularization

Recommender Systems

• Collaborative Systems
• Content Based Recommender Systems

Reinforcement Learning

• Markov Decision
• Advanced RL

Convolutional Neural Networks

• CNN Basics
• Computer Vision

Recurrent Neural Networks

• RNN Basics
• GRU
• LSTM
• Attention Models
• NLP

Disclaimer

I do not gurantee any of the subject discussed in the notebooks. It's based on my understanding from different courses that I have watched and they might be wrong. And for the paid resources, I try to not directly use any of their content, for the privacy of the courses. If you're looking for the exact content, please reach out to the original resource.

Contribution

You're very welcome to add topics and your suggestion to get included in the archive!