Course 1-1-1 Introduction_1647172987327.pdf
Course 1-1-2 Python programming_1647172987351.pdf
Course 1-1-3 Basics of mathematical analysis_1647172987369.pdf
Course 1-2-1 Introduction to linear algebra_1647172987397.pdf
Course 1-2-2 Matrixes and basic matrix operations_1647172987417.pdf
Course 1-3-1 The gradient and optimization of smooth functions_1647172987432.pdf
Course 1-3-2 Optimization methods_1647172987450.pdf
Course 1-3-3 Matrix decompositions_1647172987470.pdf
Course 1-4-1 Probability and random variables_1647172987548.pdf
Course 1-4-2 Statistics_1647172987579.pdf
Course 2-1-1 Introduction to machine learning_1647172988030.pdf
Course 2-1-2 Linear models_1647172988045.pdf
Course 2-2-1 The problem of overfitting_1647172988077.pdf
Course 2-2-2 Quality metrics_1647172988101.pdf
Course 2-3-1 Statistical view of linear models_1647172988136.pdf
Course 2-3-2 Practical recomendations for linear models_1647172988327.pdf
Course 2-4-1 Decision trees_1647172988466.pdf
Course 2-4-2 Random forest_1647172988554.pdf
Course 2-4-3 Gradient boosting_1647172988629.pdf
Course 2-5-1 Neural networks_1647172988661.pdf
Course 2-5-2 Bayesian classification and regression_1647172988777.pdf
Course 2-5-3 Metric algorithms and SVM_1647172988875.pdf
Course 2-5-4 Bayes theorem in machine learning_1647172988893.pdf
xgboost_1647172988931.pdf
Course 2-1-1 Introduction to machine learning - Slides_1647172989034.pdf
Course 2-1-2 Linear models - Slides_1647172989472.pdf
Course 2-2-1 The problem of overfitting - Slides_1647172989853.pdf
Course 2-2-2 Quality metrics - Slides_1647172990167.pdf
Course 2-3-1 Statistical view of linear models - Slides_1647172990576.pdf
Course 2-3-2 Practical recomendations for linear models - Slides_1647172991395.pdf
Course 2-4-1 Decision trees - Slides_1647172991781.pdf
Course 2-4-2 Random forest - Slides_1647172992135.pdf
Course 2-4-3 Gradient boosting - Slides_1647172992523.pdf
Course 2-5-1 Neural networks - Slides_1647172992886.pdf
Course 2-5-2 Bayesian classification and regression - Slides_1647172993462.pdf
Course 2-5-3 Metric algorithms and SVM - Slides_1647172993901.pdf
Course 2-5-4 Bayes theorem in machine learning - Slides_1647172994229.pdf
Course 5-1-1 Time series forecasting_1647172994996.pdf
Course 5-2-1 Computer vision_1647172995896.pdf
Course 5-2-2 Neural networks for image analysis_1647172997456.pdf
Course 5-2-3 Practical tasks of computer vision_1647172998084.pdf
Course 5-3-1 Text data_1647172998380.pdf
Course 5-3-2 Advanced methods of text analysis_1647172998471.pdf
Course 5-4-1 Ranking_1647172998491.pdf
1. Vremennye ryady_1647172998526.pdf
2. Avtokorrelyaciya_1647172998651.pdf
3 Stacionarnost'_1647172998903.pdf
4. ARMA_1647172999164.pdf
5. Modeli ARIMA_1647172999204.pdf
6. Vybor ARIMA i prognozirovanie_1647172999421.pdf
7. Analiz ostatkov_1647172999511.pdf
8. Regressionnyj podhod k prognozirovaniyu_1647172999604.pdf
1. Analiz povedeniya pol'zovatelej_1647172999645.pdf
2. Auditornye metriki privlechenie_1647172999750.pdf
3. Auditornye metriki aktivnost'_1647172999754.pdf
4. Auditornye metriki monetizaciya_1647172999819.pdf
5. Auditornye metriki uderzhanie_1647172999834.pdf
6. Prognozirovanie ottoka pol'zovatelej. Postanovka zadachi_1647172999859.pdf
7. Prognozirovanie ottoka pol'zovatelej Postroenie i ocenka modeli_1647172999875.pdf
1. Komp'yuternoe zrenie_1647173000002.pdf
2. Zadachi komp'yuternogo zreniya_1647173000373.pdf
3. Nizkourovnevoe zrenie_1647173000812.pdf
4. Linejnaya fil'traciya izobrazhenij_1647173001314.pdf
1. Klassifikaciya izobrazhenij_1647173001642.pdf
2. Zadacha klassifikacii izobrazhenij na praktike_1647173001813.pdf
3. Raspoznavanie lic_1647173002025.pdf
1. Detekciya obektov_1647173002382.pdf
2. Stilizaciya izobrazhenij_1647173002752.pdf
3. Raspoznavanie kitov_1647173003093.pdf
4. Sbor bol'shih kollekcij izobrazhenij_1647173003238.pdf
1. Rabota s tekstovymi dannymi_1647173003352.pdf
2. Predobrabotka teksta_1647173003356.pdf
3. Izvlechenie priznakov iz teksta_1647173003426.pdf
4. Izvlechenie priznakov iz teksta - 2_1647173003453.pdf
5. Obuchenie modelej na tekstah_1647173003477.pdf
1. word2vec_1647173003503.pdf
2. Rekurrentnye seti_1647173003543.pdf
1. Vydelenie kollokacij_1647173003567.pdf
2. YAzykovye modeli_1647173003575.pdf
3. Analiz tonal'nosti teksta_1647173003601.pdf
6. Annotirovanie_1647173003631.pdf
1. Zadacha ranzhirovaniya_1647173003665.pdf
2. Metriki kachestva ranzhirovaniya_1647173003700.pdf
3. Metody ranzhirovaniya_1647173003725.pdf
1. Rekomendatel'nye sistemy_1647173003792.pdf
2. kNN i matrichnye razlozheniya_1647173004069.pdf
3. Podhody k postroeniyu rekomendatel'nyh sistem_1647173004098.pdf
4. Gibridnye rekomendatel'nye sistemy_1647173004121.pdf
5. Offlajn ocenka kachestva_1647173004151.pdf
6. Onlajnovaya ocenka kachestva_1647173004171.pdf
7. Maksimizaciya pribyli magazina_1647173004200.pdf
Coursera_ML_DA_1_1647173007719.pdf
Coursera_ML_DA_2_1647173007755.pdf
Coursera_ML_DA_3_1647173007763.pdf
Coursera_ML_DA_4_1647173007779.pdf
Coursera_ML_DA_5_1647173007801.pdf
Coursera_ML_DA_6_1647173007809.pdf
Coursera_ML_DA_Specialization_1647173007903.pdf