2190513 Data Science (ICE) @CU (2025/1)

Syllabus:

Syllabus

Code:

Week01: Intro to Numpy, Pandas

1. Pandas:

2. Pandas with Youtube stat data:

3. (Advanced) Pandas with Youtube stat data:

Week02-03: Data Preparation

1. Impute Missing Value:

2. OneHotEncoder:

3. Outliers - Take Log:

4. Outliers - Remove them with Z-Score:

5. Split Train/Test:

Week04-07: Traditional ML

Week04: Tree-based model, Pipeline, Evaluation

Week05: Regression

Week06: kNN, GridSearch + Clustering

Week07: NN

1. Decision Trees:

2. Linear Regression:

3. Logistic Regression:

4. Neural Network:

5. K Nearest Neighbors (GridSearchCV):

6. SVM:

7. Save and Load Model:

8. K-Means:

9. Market-Basket Analysis:

10. Scikit-learn pipeline:

Week 08: Intro to Deep Learning

1. Image classification with CNN (PyTorch Lightning):

2-1. Image classification with EfficientNetV2s (PyTorch Lightning):

2-2. Image classification with EfficientNetV2s (PyTorch Lightning) with TensorBoard:

2-3. Image classification with EfficientNetV2s (PyTorch Lightning) with Weights & Biases:

2-4. Image classification with EfficientNetb0 (Load a Pretrained Model from Hugging Face) (PyTorch Lightning) with Weights & Biases:

3-1. Object detection with YOLOv8 (basic script):

3-2. Object detection with YOLOv8 (custom dataset):

4. Semantic segmentation with deeplabv3 (PyTorch Lightning):

5. Time series Forecasting: Stock Price: