Python: Setting up
Python: Basics
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5. Data types (7:25)
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6. Python numbers (7:37)
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7. Variables and assignment (7:30)
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8. String basics (8:26)
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9. String Start Stop and Step (13:12)
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10. String slicing (4:47)
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11. String formatting (7:33)
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12. Lists in Python (7:54)
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13. List shorting, reversing, removing, clear, list of list (10:05)
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14. Sets (12:08)
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15. Tuples (7:02)
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16. Dictionary in python (7:53)
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17. None and Bool (5:18)
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18. Comparison operators (7:26)
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19. Logical operators (11:28)
Python: Statements
Python: Method and Functions
Python: Module and packages
Python: OOPS in python
Python: Errors handling
Python decorators and Generators
Python: Regular expression
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48. Regular expression introduction (12:38)
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49. Regular expression, grouping and pipe (11:37)
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50. Repetition and range (11:41)
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51. Greedy, non-greedy matches and findall (12:58)
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52. BeginsWith endsWith and dot character (18:14)
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53. BeginsWith endsWith and dot character continues (7:23)
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54. Sets (10:57)
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55. Literal matching, Sub and verbose (10:58)
Python: Files
Python: Numpy
Python: Pandas
More useful modules
Python: Matplotlib
ML: Before we start
Visualisation ( Exploratory Data Analysis) with Seaborn
Linear Algebra basics for ML
Pre-processing
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102. Data import (8:39)
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103. handling missing data (11:45)
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104. Feature selection and Encoding categorical data (8:27)
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105. Test and train data split and Feature scaling (16:52)
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106. Under and over sampling (14:10)
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107. Assignment and tips (3:29)
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108. Assignment solution and OneHotEncoding - Part 01 (16:26)
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109. Assignment solution and OneHotEncoding - Part 02 (16:22)
Linear Regression
Multiple linear regression
Polynomial regression
Before we move forward
Decision Tree regression
Random forest regression
Bagging and boosting
AdaBoost and XGBoost regressor
SVM (regression)
Evaluation technique background (Regression)
Regression models master template
K-Fold validation, GridSearch
Pre-processing revisited
K-nearest neighbors algorithm (KNN)
Logistic regression classifier
Naive bayes classification
Few good things to know about ML
Support Vector machines
Decision Tree and Random forest
AdaBoost and XGBoost classifier
Evaluation techniques (Manual)
Classification model master template
GridSearchCV, RandomizedSearchCV and KFold validation
Evaluation techniques using curves (ROC,AUC, PR, CAP)
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180. ROC,AUC and PR curve background (31:26)
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181. ROC,AUC-Evaluating best model (8:17)
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182. ROC,AUC-Calculating the optimal threshold(Youdens method) (17:27)
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183. ROC,AUC-Calculating the optimal threshold(best Accuracy method) (10:42)
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184. CAP curve background (12:39)
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185. CAP curve implementation (10:32)
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186. CAP curve with multiple models and multi-class (16:43)
Ensemble techniques
Model deployment basics
