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lines changed- 1 Mathematics and Python
- Homework
- 1 linear algebra and texts similarity and function approximation
- .ipynb_checkpoints
- 2 optimization in python
- .ipynb_checkpoints
- 3 central limit theorem
- .ipynb_checkpoints
- Lectures
- Lectures notebooks
- 1 introduction to ipython
- 10 numpy arrays and operations with them
- 11 working with random numbers
- 12 sample distribution evaluation
- 13 bonus video
- 2 types in Python
- 3 reading data from files
- 4 ipython files data writing
- 5 pandas dataframe
- 6 pandas indexing and selection
- 7 numpy scipy matplotlib
- 8 optimization in scipy
- 9 vector operations
- 2 Supervised learning
- Homework
- 1 linear regression and main data analysis libraries
- .ipynb_checkpoints
- 10 1nn vs random forest
- .ipynb_checkpoints
- 2 linear regression and stochastic gradient descent
- .ipynb_checkpoints
- 3 linear regression and overfitting and regularization
- .ipynb_checkpoints
- 4 metriks in sklearn
- .ipynb_checkpoints
- 5 data preparation and logistic regression
- .ipynb_checkpoints
- 6 bagging and random forest
- .ipynb_checkpoints
- 7 gradient boosting
- .ipynb_checkpoints
- 8 neural networks
- .ipynb_checkpoints
- 9 choosing distribution family in naive bayes
- .ipynb_checkpoints
- Lectures
- Slides
- Lectures notebooks
- 1 sklearn datasets
- 10 sklearn random forest
- 11 xgboost gradient boosting
- 12 bonus video
- 2 sklearn cross validation
- 3 linear models classification
- 4 linear models regression
- 5 sklearn metrics
- 6 sklearn grid search
- 7 bike sharing demand part 1
- 8 bike sharing demand part 2
- 9 sklearn decision trees
- 3 Unsupervised learning
- Homework
- 1 banners placing
- .ipynb_checkpoints
- 2 principal component analysis (PCA)
- .ipynb_checkpoints
- 3 data visualizaton
- .ipynb_checkpoints
- 4 text themes
- .ipynb_checkpoints
- 5 text themes postnauka BigARTM
- .ipynb_checkpoints
- lectures_batches
- Lectures
- Slides
- Lectures notebooks
- 1 texts clustering
- 2 data visualization
- 3 gensim library
- 4 BigARTM library
- 4 Stats for data analysis
- Homework
- 1 test mean conf int
- .ipynb_checkpoints
- 10 test multiple hypothesis testing
- .ipynb_checkpoints
- 11 test regression
- .ipynb_checkpoints
- 12 project credit scoring
- .ipynb_checkpoints
- 13 project churn analysis
- .ipynb_checkpoints
- 14 test AB browser test
- .ipynb_checkpoints
- 15 project genom cancer
- .ipynb_checkpoints
- 2 test proportions conf int
- .ipynb_checkpoints
- 3 test bootstrap conf int
- .ipynb_checkpoints
- 4 test hypothesis testing
- .ipynb_checkpoints
- 5 test student tests
- .ipynb_checkpoints
- 6 test parametric tests
- .ipynb_checkpoints
- 7 test non-parametric tests
- .ipynb_checkpoints
- 8 test correlation
- .ipynb_checkpoints
- 9 test correlation analysis
- .ipynb_checkpoints
- Lectures
- Slides/Lekcii-4 kurs.Postroenie_vyvodov_po_dannym
- Lectures notebooks
- 1 mean conf int
- 10 non-parametric tests rel
- 11 non-parametric tests rel ind
- 12 correlation
- 13 multiple hypothesis testing
- 14 regression
- 2 proporion conf int
- 3 two proporions conf int upd
- 4 bootstrap intervals
- 5 binomial test for proportion
- 6 hi2 test
- 7 student tests
- 8 two proportions diff test
- 9 non-parametric tests 1sample
- 5 Data analysis applications
- Homework
- 1 test autocorrelation and stationarity
- .ipynb_checkpoints
- 2 project wage forecast for Russia
- .ipynb_checkpoints
- Salary
- __MACOSX
- wage_forecast_solomkin
- 3 project computer vision
- .ipynb_checkpoints
- 4 project text analysis
- .ipynb_checkpoints
- smsspamcollection
- 5 project recommender systems
- .ipynb_checkpoints
- Lectures
- 1-1. Prognozirovanie vremennyh ryadov
- 1-2. Analiz povedeniya pol'zovatelej
- 2-1. Komp'yuternoe zrenie
- 2-2. Nejronnye seti dlya analiza izobrazhenij
- 2-3. Prakticheskie zadachi komp'yuternogo zreniya
- 3-1. Tekstovye dannye i rabota s nimi
- 3-2. Prodvinutye metody analiza tekstov
- 3-3. Primery zadach analiza tekstov
- 4-1. Ranzhirovanie
- 4-2. Rekomendatel'nye sistemy
- Lectures notebooks
- 1 wine sales time series
- 2 texts sentiment analysis
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