1. Introduction.mp4 (29.39 MB)
10. Working with Numpy Library of Python.mp4 (43.87 MB)
100. Evaluating model performance in Python.mp4 (9.01 MB)
101. Predicting probabilities, assigning classes and making Confusion Matrix in R.mp4 (55.69 MB)
102. Linear Discriminant Analysis.mp4 (40.95 MB)
103. LDA in Python.mp4 (11.4 MB)
104. Linear Discriminant Analysis in R.mp4 (74.35 MB)
105. Test Train Split.mp4 (39.29 MB)
106. Test Train Split in Python.mp4 (33.1 MB)
107. Test Train Split in R.mp4 (74.23 MB)
108. K Nearest Neighbors classifier.mp4 (75.42 MB)
109. K Nearest Neighbors in Python Part 1.mp4 (37.23 MB)
11. Working with Pandas Library of Python.mp4 (46.88 MB)
110. K Nearest Neighbors in Python Part 2.mp4 (42.35 MB)
111. K Nearest Neighbors in R.mp4 (64.85 MB)
112. Understanding the results of classification models.mp4 (41.64 MB)
113. Summary of the three models.mp4 (22.21 MB)
114. Basics of Decision Trees.mp4 (42.64 MB)
115. Understanding a Regression Tree.mp4 (43.72 MB)
116. The stopping criteria for controlling tree growth.mp4 (13.97 MB)
117. The Data set for this part.mp4 (37.26 MB)
118. Importing the Data set into Python.mp4 (25.84 MB)
119. Importing the Data set into R.mp4 (43.7 MB)
12. Working with Seaborn Library of Python.mp4 (40.36 MB)
120. Missing value treatment in Python.mp4 (17.92 MB)
121. Dummy Variable creation in Python.mp4 (24.94 MB)
122. Dependent Independent Data split in Python.mp4 (15.18 MB)
123. Test Train split in Python.mp4 (24.87 MB)
124. Splitting Data into Test and Train Set in R.mp4 (43.97 MB)
125. Creating Decision tree in Python.mp4 (17.87 MB)
126. Building a Regression Tree in R.mp4 (103.33 MB)
127. Evaluating model performance in Python.mp4 (16.44 MB)
128. Plotting decision tree in Python.mp4 (21.47 MB)
129. Pruning a tree.mp4 (18.46 MB)
13. Installing R and R studio.mp4 (35.71 MB)
130. Pruning a tree in Python.mp4 (73.5 MB)
131. Pruning a Tree in R.mp4 (82.09 MB)
132. Classification tree.mp4 (28.2 MB)
133. The Data set for Classification problem.mp4 (18.57 MB)
134. Classification tree in Python Preprocessing.mp4 (45.38 MB)
135. Classification tree in Python Training.mp4 (82.71 MB)
136. Building a classification Tree in R.mp4 (85.1 MB)
137. Advantages and Disadvantages of Decision Trees.mp4 (6.86 MB)
138. Ensemble technique 1 Bagging.mp4 (28.14 MB)
139. Ensemble technique 1 Bagging in Python.mp4 (77.3 MB)
14. Basics of R and R studio.mp4 (38.84 MB)
140. Bagging in R.mp4 (58.95 MB)
141. Ensemble technique 2 Random Forests.mp4 (18.19 MB)
142. Ensemble technique 2 Random Forests in Python.mp4 (46.7 MB)
143. Using Grid Search in Python.mp4 (80.66 MB)
144. Random Forest in R.mp4 (30.72 MB)
145. Boosting.mp4 (30.58 MB)
146. Ensemble technique 3a Boosting in Python.mp4 (39.87 MB)
147. Gradient Boosting in R.mp4 (69.09 MB)
148. Ensemble technique 3b AdaBoost in Python.mp4 (30.53 MB)
149. AdaBoosting in R.mp4 (88.67 MB)
15. Packages in R.mp4 (82.94 MB)
150. Ensemble technique 3c XGBoost in Python.mp4 (75 MB)
151. XGBoosting in R.mp4 (161.3 MB)
152. Content flow.mp4 (8.64 MB)
153. The Concept of a Hyperplane.mp4 (29.42 MB)
154. Maximum Margin Classifier.mp4 (22.48 MB)
155. Limitations of Maximum Margin Classifier.mp4 (10.6 MB)
156. Support Vector classifiers.mp4 (56.16 MB)
157. Limitations of Support Vector Classifiers.mp4 (10.8 MB)
158. Kernel Based Support Vector Machines.mp4 (40.12 MB)
159. Regression and Classification Models.mp4 (4.03 MB)
16. Inputting data part 1 Inbuilt datasets of R.mp4 (40.74 MB)
160. The Data set for the Regression problem.mp4 (37.2 MB)
161. Importing data for regression model.mp4 (25.84 MB)
162. X y Split.mp4 (15.18 MB)
163. Test Train Split.mp4 (24.86 MB)
164. Standardizing the data.mp4 (38.41 MB)
165. SVM based Regression Model in Python.mp4 (67.63 MB)
166. The Data set for the Classification problem.mp4 (18.55 MB)
167. Classification model Preprocessing.mp4 (45.37 MB)
168. Classification model Standardizing the data.mp4 (9.72 MB)
169. SVM Based classification model.mp4 (64.12 MB)
17. Inputting data part 2 Manual data entry.mp4 (25.52 MB)
170. Hyper Parameter Tuning.mp4 (57.74 MB)
171. Polynomial Kernel with Hyperparameter Tuning.mp4 (22.92 MB)
172. Radial Kernel with Hyperparameter Tuning.mp4 (37.21 MB)
173. Importing Data into R.mp4 (53.67 MB)
174. Test Train Split.mp4 (50.48 MB)
176. Classification SVM model using Linear Kernel.mp4 (139.16 MB)
177. Hyperparameter Tuning for Linear Kernel.mp4 (60.5 MB)
178. Polynomial Kernel with Hyperparameter Tuning.mp4 (83.14 MB)
179. Radial Kernel with Hyperparameter Tuning.mp4 (56.68 MB)
18. Inputting data part 3 Importing from CSV or Text files.mp4 (60.1 MB)
180. SVM based Regression Model in R.mp4 (106.12 MB)
181. Introduction to Neural Networks and Course flow.mp4 (29.07 MB)
182. Perceptron.mp4 (44.75 MB)
183. Activation Functions.mp4 (34.61 MB)
184. Python Creating Perceptron model.mp4 (86.55 MB)
185. Basic Terminologies.mp4 (40.42 MB)
186. Gradient Descent.mp4 (60.34 MB)
187. Back Propagation.mp4 (122.2 MB)
188. Some Important Concepts.mp4 (62.18 MB)
189. Hyperparameter.mp4 (45.35 MB)
19. Creating Barplots in R.mp4 (96.73 MB)
190. Keras and Tensorflow.mp4 (14.91 MB)
191. Installing Tensorflow and Keras.mp4 (20.06 MB)
192. Dataset for classification.mp4 (56.19 MB)
193. Normalization and Test Train split.mp4 (44.2 MB)
194. Different ways to create ANN using Keras.mp4 (10.81 MB)
195. Building the Neural Network using Keras.mp4 (79.11 MB)
196. Compiling and Training the Neural Network model.mp4 (81.63 MB)
197. Evaluating performance and Predicting using Keras.mp4 (69.91 MB)
198. Building Neural Network for Regression Problem.mp4 (155.9 MB)
199. Using Functional API for complex architectures.mp4 (92.1 MB)
20. Creating Histograms in R.mp4 (42.02 MB)
200. Saving Restoring Models and Using Callbacks.mp4 (151.58 MB)
201. Hyperparameter Tuning.mp4 (60.63 MB)
202. Installing Keras and Tensorflow.mp4 (22.78 MB)
203. Data Normalization and Test Train Split.mp4 (111.78 MB)
204. Building,Compiling and Training.mp4 (130.73 MB)
205. Evaluating and Predicting.mp4 (99.28 MB)
206. ANN with NeuralNets Package.mp4 (84.42 MB)
207. Building Regression Model with Functional API.mp4 (131.12 MB)
208. Complex Architectures using Functional API.mp4 (79.57 MB)
209. Saving Restoring Models and Using Callbacks.mp4 (216.03 MB)
21. Types of Data.mp4 (21.76 MB)
210. CNN Introduction.mp4 (51.15 MB)
211. Stride.mp4 (16.58 MB)
212. Padding.mp4 (31.63 MB)
213. Filters and Feature maps.mp4 (52.71 MB)
214. Channels.mp4 (67.77 MB)
215. PoolingLayer.mp4 (46.87 MB)
216. CNN model in Python Preprocessing.mp4 (40.63 MB)
217. CNN model in Python structure and Compile.mp4 (43.25 MB)
218. CNN model in Python Training and results.mp4 (55.15 MB)
219. Comparison Pooling vs Without Pooling in Python.mp4 (57.97 MB)
22. Types of Statistics.mp4 (10.93 MB)
220. CNN on MNIST Fashion Dataset Model Architecture.mp4 (7.35 MB)
221. Data Preprocessing.mp4 (67.02 MB)
222. Creating Model Architecture.mp4 (71.6 MB)
223. Compiling and training.mp4 (32.2 MB)
224. Model Performance.mp4 (68.08 MB)
225. Comparison Pooling vs Without Pooling in R.mp4 (44.6 MB)
226. Project Introduction.mp4 (49.39 MB)
228. Project Data Preprocessing in Python.mp4 (71.83 MB)
229. Project Training CNN model in Python.mp4 (65.98 MB)
23. Describing data Graphically.mp4 (65.39 MB)
230. Project in Python model results.mp4 (21.02 MB)
231. Project in R Data Preprocessing.mp4 (87.76 MB)
232. CNN Project in R Structure and Compile.mp4 (46.11 MB)
233. Project in R Training.mp4 (24.58 MB)
234. Project in R Model Performance.mp4 (23.18 MB)
235. Project in R Data Augmentation.mp4 (56.38 MB)
236. Project in R Validation Performance.mp4 (23.69 MB)
237. Project Data Augmentation Preprocessing.mp4 (41.41 MB)
238. Project Data Augmentation Training and Results.mp4 (53.04 MB)
239. ILSVRC.mp4 (20.92 MB)
24. Measures of Centers.mp4 (38.57 MB)
240. LeNET.mp4 (7 MB)
241. VGG16NET.mp4 (10.35 MB)
242. GoogLeNet.mp4 (21.37 MB)
243. Transfer Learning.mp4 (29.99 MB)
244. Project Transfer Learning VGG16.mp4 (129.09 MB)
245. Project Transfer Learning VGG16 (Implementation).mp4 (101.57 MB)
246. Project Transfer Learning VGG16 (Performance).mp4 (64.11 MB)
247. Introduction.mp4 (12.26 MB)
248. Time Series Forecasting Use cases.mp4 (25.91 MB)
249. Forecasting model creation Steps.mp4 (10.11 MB)
25. Measures of Dispersion.mp4 (22.85 MB)
250. Forecasting model creation Steps 1 (Goal).mp4 (34.5 MB)
251. Time Series Basic Notations.mp4 (62.48 MB)
252. Data Loading in Python.mp4 (108.86 MB)
253. Time Series Visualization Basics.mp4 (63.72 MB)
254. Time Series Visualization in Python.mp4 (165.19 MB)
255. Time Series Feature Engineering Basics.mp4 (59.47 MB)
256. Time Series Feature Engineering in Python.mp4 (112.69 MB)
257. Time Series Upsampling and Downsampling.mp4 (16.95 MB)
258. Time Series Upsampling and Downsampling in Python.mp4 (100.67 MB)
259. Time Series Power Transformation.mp4 (14.85 MB)
26. Introduction to Machine Learning.mp4 (109.17 MB)
260. Moving Average.mp4 (38.7 MB)
261. Exponential Smoothing.mp4 (8.38 MB)
262. White Noise.mp4 (11.37 MB)
263. Random Walk.mp4 (21.16 MB)
264. Decomposing Time Series in Python.mp4 (59.84 MB)
265. Differencing.mp4 (32.35 MB)
266. Differencing in Python.mp4 (113 MB)
267. Test Train Split in Python.mp4 (57.41 MB)
268. Naive (Persistence) model in Python.mp4 (43.37 MB)
269. Auto Regression Model Basics.mp4 (16.88 MB)
27. Building a Machine Learning Model.mp4 (39.48 MB)
270. Auto Regression Model creation in Python.mp4 (53.49 MB)
271. Auto Regression with Walk Forward validation in Python.mp4 (49.59 MB)
272. Moving Average model Basics.mp4 (24.09 MB)
273. Moving Average model in Python.mp4 (56.65 MB)
274. ACF and PACF.mp4 (41.22 MB)
275. ARIMA model Basics.mp4 (21.36 MB)
276. ARIMA model in Python.mp4 (74.43 MB)
277. ARIMA model with Walk Forward Validation in Python.mp4 (32.15 MB)
278. SARIMA model.mp4 (39.02 MB)
279. SARIMA model in Python.mp4 (66.23 MB)
28. Gathering Business Knowledge.mp4 (14.52 MB)
280. Stationary time Series.mp4 (5.58 MB)
281. The final milestone!.mp4 (11.84 MB)
29. Data Exploration.mp4 (20.11 MB)
3. Installing Python and Anaconda.mp4 (16.27 MB)
30. The Dataset and the Data Dictionary.mp4 (69.28 MB)
31. Importing Data in Python.mp4 (27.83 MB)
32. Importing the dataset into R.mp4 (13.11 MB)
33. Univariate analysis and EDD.mp4 (24.18 MB)
34. EDD in Python.mp4 (61.8 MB)
35. EDD in R.mp4 (96.98 MB)
36. Outlier Treatment.mp4 (24.49 MB)
37. Outlier Treatment in Python.mp4 (70.25 MB)
38. Outlier Treatment in R.mp4 (30.74 MB)
39. Missing Value Imputation.mp4 (24.99 MB)
4. This is a milestone!.mp4 (20.66 MB)
40. Missing Value Imputation in Python.mp4 (23.42 MB)
41. Missing Value imputation in R.mp4 (26 MB)
42. Seasonality in Data.mp4 (17.01 MB)
43. Bi variate analysis and Variable transformation.mp4 (100.39 MB)
44. Variable transformation and deletion in Python.mp4 (44.11 MB)
45. Variable transformation in R.mp4 (55.42 MB)
46. Non usable variables.mp4 (20.24 MB)
47. Dummy variable creation Handling qualitative data.mp4 (36.8 MB)
48. Dummy variable creation in Python.mp4 (26.53 MB)
49. Dummy variable creation in R.mp4 (43.98 MB)
5. Opening Jupyter Notebook.mp4 (65.19 MB)
50. Correlation Analysis.mp4 (71.59 MB)
51. Correlation Analysis in Python.mp4 (55.3 MB)
52. Correlation Matrix in R.mp4 (83.13 MB)
53. The Problem Statement.mp4 (9.37 MB)
54. Basic Equations and Ordinary Least Squares (OLS) method.mp4 (43.37 MB)
55. Assessing accuracy of predicted coefficients.mp4 (92.11 MB)
56. Assessing Model Accuracy RSE and R squared.mp4 (43.59 MB)
57. Simple Linear Regression in Python.mp4 (63.43 MB)
58. Simple Linear Regression in R.mp4 (40.82 MB)
59. Multiple Linear Regression.mp4 (34.31 MB)
6. Introduction to Jupyter.mp4 (40.91 MB)
60. The F statistic.mp4 (55.98 MB)
61. Interpreting results of Categorical variables.mp4 (22.5 MB)
62. Multiple Linear Regression in Python.mp4 (69.73 MB)
63. Multiple Linear Regression in R.mp4 (62.37 MB)
64. Test train split.mp4 (41.88 MB)
65. Bias Variance trade off.mp4 (25.09 MB)
66. Test train split in Python.mp4 (44.88 MB)
67. Test Train Split in R.mp4 (75.6 MB)
68. Regression models other than OLS.mp4 (16.54 MB)
69. Subset selection techniques.mp4 (79.06 MB)
7. Arithmetic operators in Python Python Basics.mp4 (12.74 MB)
70. Subset selection in R.mp4 (63.53 MB)
71. Shrinkage methods Ridge and Lasso.mp4 (33.34 MB)
72. Ridge regression and Lasso in Python.mp4 (128.84 MB)
73. Ridge regression and Lasso in R.mp4 (103.43 MB)
74. Heteroscedasticity.mp4 (14.49 MB)
75. The Data and the Data Dictionary.mp4 (79 MB)
76. Data Import in Python.mp4 (22.06 MB)
77. Importing the dataset into R.mp4 (13.46 MB)
78. EDD in Python.mp4 (77.62 MB)
79. EDD in R.mp4 (66.52 MB)
8. Strings in Python Python Basics.mp4 (64.43 MB)
80. Outlier treatment in Python.mp4 (47.32 MB)
81. Outlier Treatment in R.mp4 (25.37 MB)
82. Missing Value Imputation in Python.mp4 (22.56 MB)
83. Missing Value imputation in R.mp4 (19.05 MB)
84. Variable transformation and Deletion in Python.mp4 (29.25 MB)
85. Variable transformation in R.mp4 (38.02 MB)
86. Dummy variable creation in Python.mp4 (26.37 MB)
87. Dummy variable creation in R.mp4 (44.35 MB)
88. Three Classifiers and the problem statement.mp4 (20.33 MB)
89. Why can't we use Linear Regression.mp4 (16.93 MB)
9. Lists, Tuples and Directories Python Basics.mp4 (60.32 MB)
90. Logistic Regression.mp4 (32.92 MB)
91. Training a Simple Logistic Model in Python.mp4 (47.87 MB)
92. Training a Simple Logistic model in R.mp4 (25.56 MB)
93. Result of Simple Logistic Regression.mp4 (26.93 MB)
94. Logistic with multiple predictors.mp4 (8.53 MB)
95. Training multiple predictor Logistic model in Python.mp4 (26.25 MB)
96. Training multiple predictor Logistic model in R.mp4 (15.78 MB)
97. Confusion Matrix.mp4 (21.1 MB)
98. Creating Confusion Matrix in Python.mp4 (51.25 MB)
99. Evaluating performance of model.mp4 (35.16 MB)