Machine Learning , 3rd Computer , Helwan University

Description

• We Are a study group that aimed to hand a well documented sample codes to accompany the machine learning course and to help new batches accelerate there learning.

• الهدف ان الدفعات الجديدة يكون ليها مكان فيه كل الاكواد اللي هيحتاجوها كبداية مع الشرح

Technologies We Used :

1. Sklearn

2. keras

3. tensorflow

4. opencv

5. numpy , pandas , matplotlib , seaborn

6. Gym

7. Pytorch

Models Currently available :

1. Machine Learning Models : Decision Trees , KNN , Logistic Regression , Random Forest , Naive Bayes , Svm , Linear Regression , Kmeans , PCA , HDBScan.

2. Deep Learning Models : ANN , CNN , RNN , LSTMS , GANS

3. RL Agents : Q-learning , Sarsa

How To Use :

• Every code section has either a documentation cell or a comment that describes the code , and visual aid to help , so read our explaination and if you get stuck just follow the original documentation for every library.

Roadmap :

Data Preprocessing & Vizualisation -- > ML Models -- > DL Models --> RL

Authors and acknowledgment :

It would be very nice to star & follow the people who worked on these notebooks , we were on a tight time schedule but we managed to provide as much as we can.

• This repository is not related to our university , but it's our team effort to help others.

• Special thanks to sarah saeed for her very clear explanation and time spent on these notebooks.