Files

1 Mathematics and Python
2 Supervised learning
- Homework
  - 1 linear regression and main data analysis libraries
  - 10 1nn vs random forest
  - 2 linear regression and stochastic gradient descent
  - 3 linear regression and overfitting and regularization
  - 4 metriks in sklearn
  - 5 data preparation and logistic regression
    - Preprocessing_LR.ipynb
    - data.csv
    - preprocessing_lr_answer1.txt
    - preprocessing_lr_answer2.txt
    - preprocessing_lr_answer3.txt
    - preprocessing_lr_answer4.txt
    - preprocessing_lr_answer5.txt
    - preprocessing_lr_answer6.txt
  - 6 bagging and random forest
  - 7 gradient boosting
  - 8 neural networks
  - 9 choosing distribution family in naive bayes
- Lectures notebooks
- Lectures
- Interesting resources.docx
3 Unsupervised learning
4 Stats for data analysis
5 Data analysis applications
6 Data analysis project
Certificates
.gitattributes
.gitignore
LICENSE
README.md

preprocessing_lr_answer5.txt

Aug 27, 2017

7e26b71 · Aug 27, 2017