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1 | | -Statistics |
2 | | - T test |
3 | | - Z test |
4 | | - ANOVA |
5 | | - Chi Square |
6 | | - Correlation |
7 | | - Covariance |
8 | | - Hypothesis Testing |
9 | | - |
10 | | -Classic ML |
11 | | - Linear Regression |
12 | | - Logistic Regression |
13 | | - Regulazisation (Rigde and Lasso) |
14 | | - Cost Functions |
15 | | - Decision Tree |
16 | | - Random Forest |
17 | | - Ensemble Learning |
18 | | - Bagging and Boosting |
19 | | - XGBoost |
20 | | - LightGBM |
21 | | - |
22 | | -Hyperparamter Tuning |
23 | | - Grid Search |
24 | | - Random Search |
25 | | - HyperOpt |
26 | | - Feature Selection - PCA |
27 | | - |
28 | | -Normmaliztion |
29 | | - Imbalance Dataaet |
30 | | - Imputing Missing data |
31 | | - Handling Outliers |
32 | | - Cross Validation |
33 | | - |
34 | | -Clustering |
35 | | - K-Means clsutering |
36 | | - KNN |
37 | | - Principal Component Analysis |
38 | | - |
39 | | -Perfromance Measures |
40 | | - R-square |
41 | | - Adjusted R-square |
42 | | - Mean Square Error |
43 | | - Root Mean Square Error |
44 | | - MAPE |
45 | | - Mean Absolute Error |
46 | | - |
47 | | - Recall |
48 | | - Precision |
49 | | - Accuracy |
50 | | - F1-Score |
51 | | - ROC-AUC |
52 | | - Confusion Matrix |
53 | | - |
54 | | - Type1 Error |
55 | | - Type2 Error |
56 | | - True Positive Rate |
57 | | - False Positive Rate |
58 | | - |
59 | | - |
60 | | -Adavnced ML |
61 | | - CNN |
62 | | - RCNN |
63 | | - LSTM |
64 | | - Transfromers |
65 | | - BERT |
66 | | - |
67 | | - |
68 | | -Time Series |
69 | | - Trend |
70 | | - Seasonality |
71 | | - Irregualrity |
72 | | - Cyclicity |
73 | | - Stationality |
74 | | - ADF |
75 | | - Making data stationary |
76 | | - White Noise |
77 | | - Holt Winters |
78 | | - FB-Prophet |
79 | | - |
80 | | - |
81 | | -Drift Detection |
82 | | - Type of drifts |
83 | | - KS Test |
84 | | - KL Divergence |
85 | | - Wassertein distance |
86 | | - ADWIN |
87 | | - |
88 | | -NLP |
89 | | - Stemming |
90 | | - Lemmatization |
91 | | - TF-IDF |
92 | | - Word2Vec |
93 | | - Bag of Words models |
94 | | - Spacy |
95 | | - |
96 | | -MLOPS |
97 | | - MLFlow |
98 | | - Model Registry |
99 | | - Data Versioning |
100 | | - Artifacts |
| 1 | +# Statistics |
| 2 | + *T test |
| 3 | + *Z test |
| 4 | + *ANOVA |
| 5 | + *Chi Square |
| 6 | + *Correlation |
| 7 | + *Covariance |
| 8 | + *Hypothesis Testing |
| 9 | + |
| 10 | +# Classic ML |
| 11 | + *Linear Regression |
| 12 | + *Logistic Regression |
| 13 | + *Regulazisation (Rigde and Lasso) |
| 14 | + *Cost Functions |
| 15 | + *Decision Tree |
| 16 | + *Random Forest |
| 17 | + *Ensemble Learning |
| 18 | + *Bagging and Boosting |
| 19 | + *XGBoost |
| 20 | + *LightGBM |
| 21 | + |
| 22 | +# Hyperparamter Tuning |
| 23 | + *Grid Search |
| 24 | + *Random Search |
| 25 | + *HyperOpt |
| 26 | + *Feature Selection - PCA |
| 27 | + |
| 28 | +# Normmaliztion |
| 29 | + *Imbalance Dataaet |
| 30 | + *Imputing Missing data |
| 31 | + *Handling Outliers |
| 32 | + *Cross Validation |
| 33 | + |
| 34 | +# Clustering |
| 35 | + *K-Means clsutering |
| 36 | + *KNN |
| 37 | + *Principal Component Analysis |
| 38 | + |
| 39 | +# Perfromance Measures |
| 40 | + *R-square |
| 41 | + *Adjusted R-square |
| 42 | + *Mean Square Error |
| 43 | + *Root Mean Square Error |
| 44 | + *MAPE |
| 45 | + *Mean Absolute Error |
| 46 | + |
| 47 | + *Recall |
| 48 | + *Precision |
| 49 | + *Accuracy |
| 50 | + *F1-Score |
| 51 | + *ROC-AUC |
| 52 | + *Confusion Matrix |
| 53 | + |
| 54 | + *Type1 Error |
| 55 | + *Type2 Error |
| 56 | + *True Positive Rate |
| 57 | + *False Positive Rate |
| 58 | + |
| 59 | + |
| 60 | +# Adavnced ML |
| 61 | + *CNN |
| 62 | + *RCNN |
| 63 | + *LSTM |
| 64 | + *Transfromers |
| 65 | + *BERT |
| 66 | + |
| 67 | + |
| 68 | +# Time Series |
| 69 | + *Trend |
| 70 | + *Seasonality |
| 71 | + *Irregualrity |
| 72 | + *Cyclicity |
| 73 | + *Stationality |
| 74 | + *ADF |
| 75 | + *Making data stationary |
| 76 | + *White Noise |
| 77 | + *Holt Winters |
| 78 | + *FB-Prophet |
| 79 | + |
| 80 | + |
| 81 | +# Drift Detection |
| 82 | + *Type of drifts |
| 83 | + *KS Test |
| 84 | + *KL Divergence |
| 85 | + *Wassertein distance |
| 86 | + *ADWIN |
| 87 | + |
| 88 | +# NLP |
| 89 | + *Stemming |
| 90 | + *Lemmatization |
| 91 | + *TF-IDF |
| 92 | + *Word2Vec |
| 93 | + *Bag of Words models |
| 94 | + *Spacy |
| 95 | + |
| 96 | +# MLOPS |
| 97 | + *MLFlow |
| 98 | + *Model Registry |
| 99 | + *Data Versioning |
| 100 | + *Artifacts |
101 | 101 |
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102 | 102 |
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103 | 103 |
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