64. Numpy operations | StudyEasy.org

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Machine Learning from A to Z with examples (Pre-Launched)

Python: Setting up

1. Python setting up (7:32)
2. Jupyter notebook (11:33)
3. Pycharm python IDE (7:10)
4. Update: Anaconda website updated (7:28)

Python: Basics

5. Data types (7:25)
6. Python numbers (7:37)
7. Variables and assignment (7:30)
8. String basics (8:26)
9. String Start Stop and Step (13:12)
10. String slicing (4:47)
11. String formatting (7:33)
12. Lists in Python (7:54)
13. List shorting, reversing, removing, clear, list of list (10:05)
14. Sets (12:08)
15. Tuples (7:02)
16. Dictionary in python (7:53)
17. None and Bool (5:18)
18. Comparison operators (7:26)
19. Logical operators (11:28)

Python: Statements

20. If ElIf & else (11:31)
21. While loop (9:15)
22. For loop (8:44)
23. Tuple unpacking (10:20)
24. Break, continue and pass (13:02)
25. Range, enumerate and zip (21:31)
26. In python (9:36)
27. Input and import (7:54)

Python: Method and Functions

28. User-defined functions (12:39)
29. Help function (5:45)
30. Scopes (12:59)
31. args and kwargs (11:46)
32. Maps, Filters and Lambdas (18:41)
33. Lambda once again (12:41)

Python: Module and packages

34. Python packages (10:04)
35. User defined packages (18:33)
36. User defined packages continues (5:42)

Python: OOPS in python

37. Naming conventions and introduction (11:49)
38. Class attributes and Methods (10:15)
39. Inheritance (10:53)
40. Multiple, multi level inheritance and MRO (18:46)
41. Polymorphism (11:04)
42. Special class methods (14:58)

Python: Errors handling

43. Try except finally (9:29)
44. Error types, else and finally (14:14)

Python decorators and Generators

45. Python decorators (16:39)
46. Class method decorator (10:20)
47. Python generators (15:55)

Python: Regular expression

48. Regular expression introduction (12:38)
49. Regular expression, grouping and pipe (11:37)
50. Repetition and range (11:41)
51. Greedy, non-greedy matches and findall (12:58)
52. BeginsWith endsWith and dot character (18:14)
53. BeginsWith endsWith and dot character continues (7:23)
54. Sets (10:57)
55. Literal matching, Sub and verbose (10:58)

Python: Files

56. Files introduction (3:49)
57. Paths (12:28)
58. Read mode, write mode and methods (15:18)

Python: Numpy

59. Setting up (9:29)
60. NumPy array functions - Array generate (12:27)
61. Random array based methods (10:31)
62. Slicing and broadcast (12:47)
63. Matrices selection and conditional selection (14:54)
64. Numpy operations (6:47)

Python: Pandas

65. Panda series (10:58)
66. DataFrame introduction (21:23)
67. DataFrame Selections (16:19)
68. GroupBy and Concatenation (8:13)
69. Concatenation (13:16)
70. Operations (8:59)

More useful modules

71. Python random class (15:44)
72. Random under numpy and Arange (15:22)
73. Python collections (14:49)
74. Python counter from collections (13:15)
75. Math Matrix multiplication (7:18)

Python: Matplotlib

76. Matplotlib simple plot, line graphs (12:05)
77. Matplotlib Bar-graph and multiple plotting (12:22)
78. Matplotlib Subplot and histogram (11:24)
79. Matplotlib Scatter plots and Pie charts (21:40)
80. Matplotlib 3D scatter and simple plot (9:46)
81. Matpotlib Wireframe surface plotting

ML: Before we start

82. Introduction to ML & Supervised learning (10:53)
83. Unsupervised learning (7:29)
84. Type of data (7:20)
85. Mean Mode median (5:16)
86. Standard deviation (11:41)
87. Most common data distributions, PDF and PMF (9:16)
88. Percentiles, moment and Quantiles (12:54)

Visualisation ( Exploratory Data Analysis) with Seaborn

89. Autocomplete on jupyter notebook (5:21)
90. Scatter plot on Iris dataset (20:18)
91. Pair plot and limitations (11:51)
92. Tips dataset (3:36)
93. Seaborn plots (22:47)
94. Facetgrid plots (9:54)
95. Univariate Analysis using PDF (12:24)
96. Boxplot and Violin Plot (16:00)
97. HeatMap (10:27)

Linear Algebra basics for ML

98. Matrices (8:08)
99. Matrix operations and scalar operations (4:33)
100. Matrix multiplication (7:10)
101. Identity matrix, matrix inverse properties, transpose of matrix (8:39)

Pre-processing

102. Data import (8:39)
103. handling missing data (11:45)
104. Feature selection and Encoding categorical data (8:27)
105. Test and train data split and Feature scaling (16:52)
106. Under and over sampling (14:10)
107. Assignment and tips (3:29)
108. Assignment solution and OneHotEncoding - Part 01 (16:26)
109. Assignment solution and OneHotEncoding - Part 02 (16:22)

Linear Regression

110. Linear regression working and Cost function (11:34)
111. Linear regression implementation in python - Part 1 (17:43)
112. Linear regression implementation in python - Part 2 (6:35)

Multiple linear regression

113. Multiple linear regression in Python (10:20)
114. Multiple linear regression behind the scene - Part 1 (16:35)
115. Multiple linear regression behind the scene - Part 2 (14:25)

Polynomial regression

116. Polynomial regression (19:30)
117. Polynomial regression on multiple feature dataset (23:54)

Before we move forward

118. Bias, Variance and overfitting (10:43)
119. Gradient decent - Background (16:39)
120. Gradient decent in 2D and 3D space (11:42)

Decision Tree regression

121. Measuring Entropy & Gini impurity (19:22)
123. Decision Tree implementation - multiple features (9:13)
122. Decision Tree implementation - 1 feature (13:41)
124. Visualization of decision tree model (14:45)

Random forest regression

125. Ensemble Learning (8:57)
126. Random Forest (9:13)

Bagging and boosting

127. Bagging (10:49)
128. Boosting (17:02)

AdaBoost and XGBoost regressor

129. AdaBoost and XGBoost regressor (10:16)

SVM (regression)

130. SVM (regression) Background (6:47)
131. SVR under Python (3:42)

Evaluation technique background (Regression)

132. R-square (15:43)
133. Adjusted R-Square (6:13)

Regression models master template

134. Data_creation (19:02)
135. Models_and_evaluation (5:03)

K-Fold validation, GridSearch

136. K Fold validation GridSearch (7:10)
137. Updated template with GridSearchCV (20:17)
138. K Fold cross validation without GridSearchCV (15:45)
139. K Fold cross validation without GridSearchCV continues (9:37)

Pre-processing revisited

140. Why Co-relation is important (16:45)
141. Co-variance (14:49)
142. Co-relation (11:38)
143. Curse of dimensionality (8:02)
144. Pre-processing re-visited (16:39)
145. N-Pre-processing re-visited continues (11:49)
146. Feature selection (15:45)
147. N-Short discussion (3:18)

K-nearest neighbors algorithm (KNN)

148. KNN Background (15:48)
149. KNN in python (7:28)
150. Visualization and few more things (8:03)
151. LabelEncoding classes (6:58)
152. KNN on multi class classification (4:40)

Logistic regression classifier

153. Why Logistic regression (11:35)
154. Logistic regression background (11:52)
155. Logistic regression under python (5:17)
156. Logistic regression on multi-class classification (10:17)
157. Logistic regression on multi-class classification under python (3:08)

Naive bayes classification

158. Bayes theorem (18:20)
159. Likelihood vs probability normal distribution (10:10)
160. Multinomial naive bayes (13:50)
161. The log scale (21:46)
162. Gaussian naive bayes (18:37)
163. Gaussian naive bayes under Python (12:14)

Few good things to know about ML

164. Euler's number (15:04)
165. Balanced vs imbalanced data (7:02)

Support Vector machines

166. SVM getting started with 1D data (14:27)
167. SVM mapping higher dimension (13:27)
168. SVM in 2D space (10:16)
169. SVM implementation using python (9:05)

Decision Tree and Random forest

170. Decision Tree and Random forest (7:32)

AdaBoost and XGBoost classifier

171. AdaBoost and XGBoost classifier (9:46)

Evaluation techniques (Manual)

172. The accuracy not so accurate (11:38)
173. Confusion matrix (8:17)
174. Accuracy precision recall Specificity F1 Score (12:49)
175. Confusion Matrix 3D (13:03)

Classification model master template

176. Classification model master template (7:19)
177. Classification model master template with evaluation and different dataset (9:24)

GridSearchCV, RandomizedSearchCV and KFold validation

178. Updated template with GridSearchCV (11:43)
179. RandomizedSearchCV (10:45)

Evaluation techniques using curves (ROC,AUC, PR, CAP)

180. ROC,AUC and PR curve background (31:26)
181. ROC,AUC-Evaluating best model (8:17)
182. ROC,AUC-Calculating the optimal threshold(Youdens method) (17:27)
183. ROC,AUC-Calculating the optimal threshold(best Accuracy method) (10:42)
184. CAP curve background (12:39)
185. CAP curve implementation (10:32)
186. CAP curve with multiple models and multi-class (16:43)

Ensemble techniques

187. Voting classifier (16:30)

Model deployment basics

188. Model deployment basics (7:29)
189. Prediction using value (8:21)
More topics coming soon

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64. Numpy operations

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