Update README.md

README.md

@@ -38,15 +38,15 @@ Introduction, [video](https://youtu.be/QKTuw4PNOsU), [slides](https://bit.ly/2Nu
 
 ### Demo assignments
 The following are demo versions. Full versions are announced during course sessions.  
-1. Exploratory data analysis with Pandas, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment01_pandas_uci_adult.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-1-pandas-and-uci-adult-dataset)
-2. Analyzing cardiovascular disease data, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment02_analyzing_cardiovascular_desease_data.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-2-analyzing-cardiovascular-data)
-3. Decision trees with a toy task and the UCI Adult dataset, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment03_decision_trees.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-3-decision-trees)
-4. Sarcasm detection, [Kaggle Kernel](https://www.kaggle.com/kashnitsky/a4-demo-sarcasm-detection-with-logit). Linear Regression as an optimization problem, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment04_linreg_optimization.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-4-linear-regression-as-optimization)
+1. Exploratory data analysis with Pandas, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment01_pandas_uci_adult.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-1-pandas-and-uci-adult-dataset), [solution](https://www.kaggle.com/kashnitsky/a1-demo-pandas-and-uci-adult-dataset-solution)
+2. Analyzing cardiovascular disease data, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment02_analyzing_cardiovascular_desease_data.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-2-analyzing-cardiovascular-data), [solution](https://www.kaggle.com/kashnitsky/a2-demo-analyzing-cardiovascular-data-solution)
+3. Decision trees with a toy task and the UCI Adult dataset, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment03_decision_trees.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-3-decision-trees), [solution](https://www.kaggle.com/kashnitsky/a3-demo-decision-trees-solution)
+4. Sarcasm detection, [Kaggle Kernel](https://www.kaggle.com/kashnitsky/a4-demo-sarcasm-detection-with-logit), [solution](https://www.kaggle.com/kashnitsky/a4-demo-sarcasm-detection-with-logit-solution). Linear Regression as an optimization problem, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment04_linreg_optimization.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-4-linear-regression-as-optimization)
 5. Logistic Regression and Random Forest in the credit scoring problem, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment05_logit_rf_credit_scoring.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-5-logit-and-rf-for-credit-scoring)
-6. Exploring OLS, Lasso and Random Forest in a regression task, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment06_regression_wine.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-6-linear-models-and-rf-for-regression)
+6. Exploring OLS, Lasso and Random Forest in a regression task, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment06_regression_wine.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-6-linear-models-and-rf-for-regression), [solution](https://www.kaggle.com/kashnitsky/a6-demo-regression-solution)
 7. Unsupervised learning, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment07_unsupervised_learning.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-7-unupervised-learning)
-8. Implementing online regressor, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment08_implement_sgd_regressor.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-8-implementing-online-regressor)
-9. Time series analysis, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment09_time_series.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-9-time-series-analysis)
+8. Implementing online regressor, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment08_implement_sgd_regressor.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-8-implementing-online-regressor), [solution](https://www.kaggle.com/kashnitsky/a8-demo-implementing-online-regressor-solution)
+9. Time series analysis, [nbviewer](https://mlcourse.ai/notebooks/blob/master/jupyter_english/assignments_demo/assignment09_time_series.ipynb?flush_cache=true), [Kaggle Kernel](https://www.kaggle.com/kashnitsky/assignment-9-time-series-analysis), [solution](https://www.kaggle.com/kashnitsky/a9-demo-time-series-analysis-solution)
 10. Beating baseline in a competition, [Kaggle kernel](https://www.kaggle.com/kashnitsky/assignment-10-gradient-boosting-and-flight-delays)
 
 ### Kaggle competitions