Maşın öyrənməsi — StatQuest

Maşın öyrənməsinin əsas metodlarını sadə, vizual izahlarla öyrənin.

StatQuest with Josh Starmer
27h 22m
100 dərs
English

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Kurs haqqında

Reqressiya, qərar ağacları, çarpaz yoxlama, gradient boosting və neyron şəbəkələri kimi mövzuları addım-addım izah edir. Data elminə başlayanlar üçündür. Dərslər ingilis dilindədir, kanal StatQuest with Josh Starmer.

Kurikulum

100 dərs · 27h 22m

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Maşın öyrənməsi — StatQuest

Kurs haqqında

Kurikulum

1A Gentle Introduction to Machine Learning13 min13 min

2Основы машинного обучения: Кросс-валидация.6 min6 min

3Machine Learning Fundamentals: The Confusion Matrix7 min7 min

4Machine Learning Fundamentals: Sensitivity and Specificity12 min12 min

5The Sensitivity, Specificity, Precision, Recall Sing-a-Long!!!1 min1 min

6Machine Learning Fundamentals: Bias and Variance7 min7 min

7Entropy (for data science) Clearly Explained!!!17 min17 min

8Mutual Information, Clearly Explained!!!16 min16 min

9The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)9 min9 min

10Linear Regression, Clearly Explained!!!27 min27 min

11Multiple Regression, Clearly Explained!!!5 min5 min

12Using Linear Models for t-tests and ANOVA, Clearly Explained!!!12 min12 min

13Design Matrices For Linear Models, Clearly Explained!!!15 min15 min

14Odds and Log(Odds), Clearly Explained!!!12 min12 min

15Odds Ratios and Log(Odds Ratios), Clearly Explained!!!16 min16 min

16StatQuest: Logistic Regression9 min9 min

17Logistic Regression Details Pt1: Coefficients19 min19 min

18Logistic Regression Details Pt 2: Maximum Likelihood10 min10 min

19Logistic Regression Details Pt 3: R-squared and p-value15 min15 min

20Saturated Models and Deviance19 min19 min

21Logistic Regression in R, Clearly Explained!!!!17 min17 min

22Deviance Residuals6 min6 min

23ROC and AUC, Clearly Explained!16 min16 min

24ROC and AUC in R15 min15 min

25Regularization Part 1: Ridge (L2) Regression20 min20 min

26Regularization Part 2: Lasso (L1) Regression8 min8 min

27Ridge vs Lasso Regression, Visualized!!!9 min9 min

28Regularization Part 3: Elastic Net Regression5 min5 min

29Ridge, Lasso and Elastic-Net Regression in R18 min18 min

30StatQuest: Principal Component Analysis (PCA), Step-by-Step22 min22 min

31StatQuest: PCA main ideas in only 5 minutes!!!6 min6 min

32StatQuest: PCA - Practical Tips8 min8 min

33StatQuest: PCA in R9 min9 min

34StatQuest: PCA in Python12 min12 min

35StatQuest: Linear Discriminant Analysis (LDA) clearly explained.15 min15 min

36Bam!!! Clearly Explained!!!3 min3 min

37StatQuest: MDS and PCoA8 min8 min

38StatQuest: MDS and PCoA in R8 min8 min

39StatQuest: t-SNE, Clearly Explained12 min12 min

40StatQuest: Hierarchical Clustering11 min11 min

41StatQuest: K-means clustering9 min9 min

42Clustering with DBSCAN, Clearly Explained!!!10 min10 min

43StatQuest: K-nearest neighbors, Clearly Explained6 min6 min

44Naive Bayes, Clearly Explained!!!15 min15 min

45Gaussian Naive Bayes, Clearly Explained!!!9 min9 min

46Decision and Classification Trees, Clearly Explained!!!18 min18 min

47StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data5 min5 min

48Regression Trees, Clearly Explained!!!23 min23 min

49How to Prune Regression Trees, Clearly Explained!!!16 min16 min

50One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!!15 min15 min

51Classification Trees in Python from Start to Finish1h 6m1h 6m

52StatQuest: Random Forests Part 1 - Building, Using and Evaluating10 min10 min

53StatQuest: Random Forests Part 2: Missing data and clustering11 min11 min

54StatQuest: Random Forests in R15 min15 min

55The Chain Rule, Clearly Explained!!!18 min18 min

56Gradient Descent, Step-by-Step24 min24 min

57Stochastic Gradient Descent, Clearly Explained!!!11 min11 min

58AdaBoost, Clearly Explained21 min21 min

59Gradient Boost Part 1 (of 4): Regression Main Ideas16 min16 min

60Gradient Boost Part 2 (of 4): Regression Details27 min27 min

61Gradient Boost Part 3 (of 4): Classification17 min17 min

62Gradient Boost Part 4 (of 4): Classification Details37 min37 min

63Troll 2, Clearly Explained!!!5 min5 min

64XGBoost Part 1 (of 4): Regression26 min26 min

65XGBoost Part 2 (of 4): Classification25 min25 min

66XGBoost Part 3 (of 4): Mathematical Details27 min27 min

67XGBoost Part 4 (of 4): Crazy Cool Optimizations24 min24 min

68XGBoost in Python from Start to Finish57 min57 min

69CatBoost Part 1: Ordered Target Encoding9 min9 min

70CatBoost Part 2: Building and Using Trees16 min16 min

71Cosine Similarity, Clearly Explained!!!10 min10 min

72Support Vector Machines Part 1 (of 3): Main Ideas!!!21 min21 min

73Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)7 min7 min

74Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)16 min16 min

75Support Vector Machines in Python from Start to Finish.45 min45 min

76The Essential Main Ideas of Neural Networks19 min19 min

77Neural Networks Pt. 2: Backpropagation Main Ideas18 min18 min