Data Science Course

Content

1. Visualization

2. Exploratory Data Analysis

3. A/B testing and tracking Experiment via CometML

4. EDA and Feature Engineering

5. Classification

6. Encoders from Sklearn Library

7. Hyperparameters: Various methods to tune

8. Coordinate and Stochastic Gradient Descents

9. Linear Algebra in context of Linear Models

10. RFM Segmentation and Analysis for E-Commerce enterprise

11. Naive Bayes Classifier