I will add the works that I've done in the bootcamp.

1.Week - Python Basics(Data types-Loops-Non-Scalar-Data Types) Pandas and NLP example

2.Week - Probabilty-Statistics Project :https://github.com/enesbol/Kodluyoruz-Data-Science-Bootcamp/blob/main/Generative%20and%20Discriminative%20Models.pdf

3.Week -

4.Week -

5.Week ● RMSE ● RMSLE ● Categorical and Missing Data Problem ● Categorical Variable Conversion ● Missing Value ● First Model ● Bootstrap ● Subsamples ● Subsample New Logic ● Hyperparameters ● Random Feature Selectio

6.Week

7.Week

● What is Deep Learning? ● PyTorch ● Why does Gradient give the maximum direction of increase? ● Neural Network fundamentals - Everything can be thought of as a function ● Sigmoid Function ● Adding Non-linearity to the model and why is it necessary? ● Why does normalization help when training the model? - Inputs into the same logic space to pull ● Understanding Regularization ● Loss function definition ● Loss function vs Metric - Loss is for computer, metric is for us ● What is a batch, why is its size important? ● Binary Classification from 0 ● Multi-class classification from 0