Udemy Machine Learning in Python with 5 Machine Learning Projects 04 2021

1. 1. Python Fundamentals/1. Why should you learn Python.mp465.68MB
2. 1. Python Fundamentals/10. Identity and Membership Operators.mp439.22MB
3. 1. Python Fundamentals/12. Quiz Solution.mp434.21MB
4. 1. Python Fundamentals/13. String Formatting.mp451.35MB
5. 1. Python Fundamentals/14. String Methods.mp443.29MB
6. 1. Python Fundamentals/15. User Input.mp441.04MB
7. 1. Python Fundamentals/17. Quiz Solution.mp453.11MB
8. 1. Python Fundamentals/18. If, elif, and else.mp465.9MB
9. 1. Python Fundamentals/19. For and While.mp453.07MB
10. 1. Python Fundamentals/2. Installing Python and Jupyter Notebook.mp433.48MB
11. 1. Python Fundamentals/20. Break and Continue.mp440.72MB
12. 1. Python Fundamentals/22. Quiz Solution.mp449.04MB
13. 1. Python Fundamentals/3. Naming Convention for Variables.mp4102.24MB
14. 1. Python Fundamentals/4. Built in Data Types and Type Casting.mp4119.86MB
15. 1. Python Fundamentals/5. Scope of Variables.mp477.16MB
16. 1. Python Fundamentals/7. Quiz Solution.mp446.52MB
17. 1. Python Fundamentals/8. Arithmetic and Assignment Operators.mp478.04MB
18. 1. Python Fundamentals/9. Comparison, Logical, and Bitwise Operators.mp462.41MB
19. 10. Logistic Regression/1. Introduction to Logistic Regression.mp4106.4MB
20. 10. Logistic Regression/10. Industry Relevance of Logistic Regression.mp459.89MB
21. 10. Logistic Regression/2. Implementing Logistic Regression using Sklearn.mp487.01MB
22. 10. Logistic Regression/3. Feature Selection using RFECV.mp442.15MB
23. 10. Logistic Regression/4. Hyperparameter tuning using Grid search.mp458.75MB
24. 10. Logistic Regression/5. Applying Cross Validation.mp456.73MB
25. 10. Logistic Regression/6. How to analyze performance of a classification model.mp4146.18MB
26. 10. Logistic Regression/7. Using accuracy score to analyze the performance of model.mp455.54MB
27. 10. Logistic Regression/8. Using ROC-AUC score to analyze the performance of model.mp4147.63MB
28. 10. Logistic Regression/9. Real time prediction using logistic regression.mp474.65MB
29. 11. Introduction to KNN, SVM, Naive Bayes/1. Introduction to Support Vector machines.mp4108.17MB
30. 11. Introduction to KNN, SVM, Naive Bayes/2. The kermel trick for support vector machine.mp470.38MB
31. 11. Introduction to KNN, SVM, Naive Bayes/3. Implementing support vector machine using sklearn.mp467.43MB
32. 11. Introduction to KNN, SVM, Naive Bayes/4. Introduction to K nearest neighbors.mp4104.32MB
33. 11. Introduction to KNN, SVM, Naive Bayes/5. Implementing KNN using Sklearn.mp433.23MB
34. 11. Introduction to KNN, SVM, Naive Bayes/6. Introduction to Naive Bayes.mp4174.72MB
35. 11. Introduction to KNN, SVM, Naive Bayes/7. Implementing Naive Bayes using sklearn.mp461.96MB
36. 11. Introduction to KNN, SVM, Naive Bayes/8. When should we apply SVM, KNN and Naive bayes.mp469.77MB
37. 12. Tree Based Models/1. Intuition for decision trees.mp481.99MB
38. 12. Tree Based Models/2. Attribute selection method- Gini Index and Entropy.mp4218.66MB
39. 12. Tree Based Models/3. Advantages and Issues with Decision trees.mp453.37MB
40. 12. Tree Based Models/4. Implementing Decision tree using Sklearn.mp435.8MB
41. 12. Tree Based Models/5. Understanding the concept of Bagging.mp465.99MB
42. 12. Tree Based Models/6. Introduction to Random forest.mp468.09MB
43. 12. Tree Based Models/7. Understanding the parameters of Random forest.mp453.66MB
44. 12. Tree Based Models/8. Implementing random forest using Sklearn.mp447.88MB
45. 13. Boosting Models/1. Understading the concept of boosting.mp457.14MB
46. 13. Boosting Models/2. Intuition for Adaboost and Gradient Boosting.mp4153.3MB
47. 13. Boosting Models/3. Implementing AdaBoost using sklearn.mp490.82MB
48. 13. Boosting Models/4. Implementing Gradient Boosting using sklearn.mp466.93MB
49. 13. Boosting Models/5. Getting High level intuition for XGBoost.mp441.07MB
50. 13. Boosting Models/6. Implementing XGBoost using sklearn.mp465.14MB
51. 13. Boosting Models/7. Introudction to Ensembling techniques.mp4134.02MB
52. 14. Imbalanced Machine Learning/1. Why Imbalanced Data needs extra attention.mp453.62MB
53. 14. Imbalanced Machine Learning/10. Implementing Synthetic Sampling using Imblearn.mp457.45MB
54. 14. Imbalanced Machine Learning/11. Implementing Neighbors based Sampling using Imblearn.mp464.02MB
55. 14. Imbalanced Machine Learning/12. Combination of Oversampling and Under sampling.mp455.76MB
56. 14. Imbalanced Machine Learning/13. Implementing Ensemble Models for Imbalanced Data.mp454.88MB
57. 14. Imbalanced Machine Learning/14. Introduction to XG Boost for Imbalanced Data.mp443.54MB
58. 14. Imbalanced Machine Learning/15. Comparing the Results.mp441.5MB
59. 14. Imbalanced Machine Learning/2. Using Resampling Techniques to Balance the Data.mp470.55MB
60. 14. Imbalanced Machine Learning/3. Solving a Real World Problem.mp456.98MB
61. 14. Imbalanced Machine Learning/4. Preparing the Data for Predictive Modelling.mp457.93MB
62. 14. Imbalanced Machine Learning/5. Applying Logistic Regression using Sklearn.mp471.14MB
63. 14. Imbalanced Machine Learning/6. Applying Random Forest using Sklearn.mp442.65MB
64. 14. Imbalanced Machine Learning/8. Implementing Random Over Sampling using Imblearn.mp454.41MB
65. 14. Imbalanced Machine Learning/9. Implementing Random Under Sampling using Imblearn.mp457.54MB
66. 15. Introduction to Clustering Analysis/1. Introduction to Clustering.mp457.84MB
67. 15. Introduction to Clustering Analysis/10. Clustering Multiple Dimensions.mp450.01MB
68. 15. Introduction to Clustering Analysis/12. Introduction to Hierarchal Clustering.mp488.49MB
69. 15. Introduction to Clustering Analysis/13. Introduction to Dendrograms.mp441.78MB
70. 15. Introduction to Clustering Analysis/14. Implementing Hierarchial Clustering.mp452.35MB
71. 15. Introduction to Clustering Analysis/15. Introduction to DBSCAN Clustering.mp452.38MB
72. 15. Introduction to Clustering Analysis/16. Implementing DBSCAN Clustering.mp447.87MB
73. 15. Introduction to Clustering Analysis/2. Types of Clustering.mp465.18MB
74. 15. Introduction to Clustering Analysis/3. Applications of Clustering.mp455.95MB
75. 15. Introduction to Clustering Analysis/5. Using the Elbow Method for Choosing the Best Value for K.mp467.06MB
76. 15. Introduction to Clustering Analysis/6. Introduction to K Means Clustering.mp449.29MB
77. 15. Introduction to Clustering Analysis/7. Solving a Real World Problem.mp471MB
78. 15. Introduction to Clustering Analysis/8. Implementing K Means on the Mall Dataset.mp471.57MB
79. 15. Introduction to Clustering Analysis/9. Using Silhouette Score to analyze the clusters.mp496.34MB
80. 16. Dimensionality Reduction/1. Why High Dimensional Datasets are a Problem.mp479.22MB
81. 16. Dimensionality Reduction/11. Introduction to Recursive Feature Selection.mp456.62MB
82. 16. Dimensionality Reduction/12. Implementing Recursive Feature Selection.mp450.92MB
83. 16. Dimensionality Reduction/13. Introduction the Boruta Algorithm.mp452.48MB
84. 16. Dimensionality Reduction/14. Implementing the Boruta Algorithm.mp443.2MB
85. 16. Dimensionality Reduction/16. Introduction to Principal Component Analysis.mp473.79MB
86. 16. Dimensionality Reduction/17. Implementing PCA.mp455.52MB
87. 16. Dimensionality Reduction/18. Introduction to t-SNE.mp481.27MB
88. 16. Dimensionality Reduction/19. Implementing t-SNE.mp436.11MB
89. 16. Dimensionality Reduction/2. Methods to solve the problem of High Dimensionality.mp457.16MB
90. 16. Dimensionality Reduction/20. Introduction to Linear Discriminant Analysis.mp448.9MB
91. 16. Dimensionality Reduction/21. Implementing LDA.mp436.74MB
92. 16. Dimensionality Reduction/22. Difference between PCA, t-SNE, and LDA.mp464.79MB
93. 16. Dimensionality Reduction/3. Solving a Real World Problem.mp498.82MB
94. 16. Dimensionality Reduction/5. Introduction to Correlation using Heatmap.mp471.4MB
95. 16. Dimensionality Reduction/6. Removing Highly Correlated Columns using Correlation.mp448.87MB
96. 16. Dimensionality Reduction/8. Introduction to Variance Inflation Filtering.mp448.66MB
97. 16. Dimensionality Reduction/9. Implementing VIF using statsmodel.mp447.84MB
98. 17. Recommendation Engines/1. Introduction to Recommender systems.mp440.53MB
99. 17. Recommendation Engines/11. Quiz Solution.mp448.5MB
100. 17. Recommendation Engines/12. Introduction to Collaborative Filtering.mp480.86MB
101. 17. Recommendation Engines/13. Preprocessing the Data for Collaborative Filtering.mp472.39MB
102. 17. Recommendation Engines/14. Implementation of User Based Collaborative Filtering.mp462.15MB
103. 17. Recommendation Engines/15. Interpreting the Results obtained from User Based Filtering.mp463.59MB
104. 17. Recommendation Engines/16. Implementation of Item Based Collaborative Filtering.mp463.55MB
105. 17. Recommendation Engines/18. Quiz Solution.mp455.62MB
106. 17. Recommendation Engines/19. Introduction to SVD.mp4112.02MB
107. 17. Recommendation Engines/2. What are it's Use Cases.mp445.05MB
108. 17. Recommendation Engines/20. Implementing SVD using Surprise.mp440.63MB
109. 17. Recommendation Engines/21. Interpreting Results Obtained from SVD.mp446.01MB
110. 17. Recommendation Engines/22. Comparing Content, and Collaborative Based Filtering.mp461.99MB
111. 17. Recommendation Engines/24. Quiz Solution.mp447.94MB
112. 17. Recommendation Engines/25. Case Study for Netflix.mp456.38MB
113. 17. Recommendation Engines/26. Case Study for Youtube.mp458.14MB
114. 17. Recommendation Engines/3. Types of Recommender Systems.mp456.54MB
115. 17. Recommendation Engines/4. Evaluating Recommender Systems.mp453.15MB
116. 17. Recommendation Engines/5. Introduction to Content Based Filtering.mp459MB
117. 17. Recommendation Engines/6. Preprocessing the Data for Content Based Filtering.mp476.67MB
118. 17. Recommendation Engines/7. Filtering Movies Based on Genres.mp458.73MB
119. 17. Recommendation Engines/8. Introduction to Transactional Encoder.mp463.39MB
120. 17. Recommendation Engines/9. Recommending Similar Movies to Watch.mp456.31MB
121. 18. Time Series Forecasting/1. What is a Time Series Data.mp434.91MB
122. 18. Time Series Forecasting/10. Time Series Decomposition.mp489.93MB
123. 18. Time Series Forecasting/11. Splitting Time Series Data.mp463.5MB
124. 18. Time Series Forecasting/13. Basic Forecasting Techniques.mp455.48MB
125. 18. Time Series Forecasting/14. Metrics for Time series Forecasting.mp478.7MB
126. 18. Time Series Forecasting/15. Simple Moving Averages.mp450.11MB
127. 18. Time Series Forecasting/16. Simple Exponential Smoothing.mp466.62MB
128. 18. Time Series Forecasting/17. Holt and Holt Winter Exponential Smoothing.mp473.13MB
129. 18. Time Series Forecasting/19. Introduction to Auto Regressive Models.mp434.71MB
130. 18. Time Series Forecasting/2. Types of Forecasting.mp445.3MB
131. 18. Time Series Forecasting/20. Checking for Stationarity Part 1.mp465MB
132. 18. Time Series Forecasting/21. Checking for Stationarity using Statistical Methods Part 2.mp475.44MB
133. 18. Time Series Forecasting/22. Checking for Stationary Implementation.mp438.1MB
134. 18. Time Series Forecasting/23. Converting Non-Stationary Series into Stationary.mp448.1MB
135. 18. Time Series Forecasting/24. Converting Non-Stationary Series into Stationary Implementation.mp448.17MB
136. 18. Time Series Forecasting/25. Auto Correlation and Partial Correlation.mp476.85MB
137. 18. Time Series Forecasting/26. Auto Correlation and Partial Correlation Implementation.mp438.48MB
138. 18. Time Series Forecasting/27. The Simple Auto Regressive Model.mp463.42MB
139. 18. Time Series Forecasting/28. The Simple Auto Regressive Model Implementation.mp464.98MB
140. 18. Time Series Forecasting/29. Moving Average Model.mp435.3MB
141. 18. Time Series Forecasting/3. Regression Vs Time Series.mp482.95MB
142. 18. Time Series Forecasting/30. Moving Average Model Implementation.mp423.23MB
143. 18. Time Series Forecasting/32. Understanding ARMA Model.mp456.79MB
144. 18. Time Series Forecasting/33. Implementing ARMA Model.mp448.21MB
145. 18. Time Series Forecasting/34. Understanding ARIMA Model.mp455.87MB
146. 18. Time Series Forecasting/35. Implementing ARIMA Model.mp433.2MB
147. 18. Time Series Forecasting/36. Understanding SARIMA Model.mp469.94MB
148. 18. Time Series Forecasting/37. Implementing SARIMA Model.mp438.13MB
149. 18. Time Series Forecasting/39. Understanding ARIMAX Model.mp466.51MB
150. 18. Time Series Forecasting/4. Applications of Time Series.mp447.29MB
151. 18. Time Series Forecasting/40. Implementing ARIMAX Model.mp444.76MB
152. 18. Time Series Forecasting/41. Understanding SARIMAX Model.mp443.84MB
153. 18. Time Series Forecasting/42. Implementing SARIMAX Model.mp459.96MB
154. 18. Time Series Forecasting/44. How to Choose the Right Model.mp435.14MB
155. 18. Time Series Forecasting/45. Choosing the Right for Model Smaller Datasets.mp452.3MB
156. 18. Time Series Forecasting/46. Choosing the Right Model for Larger Datasets.mp436.31MB
157. 18. Time Series Forecasting/47. Best Practices while Choosing a Time series Model..mp443.02MB
158. 18. Time Series Forecasting/49. Why do we Evaluate Performance.mp431.77MB
159. 18. Time Series Forecasting/5. Components of Time Series.mp451.96MB
160. 18. Time Series Forecasting/50. Mean Forecast Error.mp452.91MB
161. 18. Time Series Forecasting/51. Mean Absolute Error.mp435.56MB
162. 18. Time Series Forecasting/52. Mean Absolute Percentage Error.mp429.76MB
163. 18. Time Series Forecasting/53. Root Mean Squared Error.mp429.34MB
164. 18. Time Series Forecasting/7. Getting Time Series data.mp471.08MB
165. 18. Time Series Forecasting/8. Handling Missing Values.mp4116.47MB
166. 18. Time Series Forecasting/9. Handling Outlier Values.mp464.43MB
167. 19. Employee Promotion Prediction/1. Setting up the Environment.mp441.71MB
168. 19. Employee Promotion Prediction/10. Feature Engineering.mp450.43MB
169. 19. Employee Promotion Prediction/11. Categorical Encoding.mp437.44MB
170. 19. Employee Promotion Prediction/12. Data Processing.mp467.65MB
171. 19. Employee Promotion Prediction/13. Feature Scaling.mp442.28MB
172. 19. Employee Promotion Prediction/14. Predictive Modelling.mp444.65MB
173. 19. Employee Promotion Prediction/15. Performance Analysis.mp477.16MB
174. 19. Employee Promotion Prediction/16. Improvements Possible.mp441.87MB
175. 19. Employee Promotion Prediction/17. Major Takeaways from the Project.mp428.99MB
176. 19. Employee Promotion Prediction/2. Understanding the Dataset.mp495.88MB
177. 19. Employee Promotion Prediction/3. Understanding the Problem Statement.mp459.78MB
178. 19. Employee Promotion Prediction/4. Performing Descriptive Statistics.mp461.68MB
179. 19. Employee Promotion Prediction/5. Missing Values Treatment.mp438.66MB
180. 19. Employee Promotion Prediction/6. Outlier Values Treatment.mp442.49MB
181. 19. Employee Promotion Prediction/7. Univariate Analysis.mp453.13MB
182. 19. Employee Promotion Prediction/8. Bivariate Analysis.mp437.16MB
183. 19. Employee Promotion Prediction/9. Multivariate Analysis.mp439.94MB
184. 2. Python for Data Analysis/1. Differences between Lists and Tuples.mp448.66MB
185. 2. Python for Data Analysis/11. Quiz Solution.mp438.27MB
186. 2. Python for Data Analysis/12. Introduction to Stacks and Queues.mp448.69MB
187. 2. Python for Data Analysis/13. Implementing Stacks and Queues using Lists.mp436.5MB
188. 2. Python for Data Analysis/14. Implementing Stacks and Queues using Deque.mp441.6MB
189. 2. Python for Data Analysis/16. Quiz Solution.mp439.51MB
190. 2. Python for Data Analysis/17. Time Complexity.mp4120.13MB
191. 2. Python for Data Analysis/18. Linear Search.mp495.52MB
192. 2. Python for Data Analysis/19. Binary Search.mp4109.54MB
193. 2. Python for Data Analysis/2. Operations on Lists.mp444.4MB
194. 2. Python for Data Analysis/20. Bubble Sort.mp475.55MB
195. 2. Python for Data Analysis/21. Insertion and Selection Sort.mp4120MB
196. 2. Python for Data Analysis/22. Merge Sort.mp4115.44MB
197. 2. Python for Data Analysis/24. Quiz Solution.mp473.24MB
198. 2. Python for Data Analysis/3. Operations on Tuples.mp427.44MB
199. 2. Python for Data Analysis/5. Quiz Solution.mp437.09MB
200. 2. Python for Data Analysis/6. Introduction to Dictionaries.mp466.83MB
201. 2. Python for Data Analysis/7. Nested Dictionaries.mp460.55MB
202. 2. Python for Data Analysis/8. Introduction to Sets.mp475.49MB
203. 2. Python for Data Analysis/9. Set Operations.mp458.59MB
204. 20. Predicting Health Expense of Customers/1. Setting up the Environment.mp450.12MB
205. 20. Predicting Health Expense of Customers/10. Applying Gradient Boosting Model.mp470.38MB
206. 20. Predicting Health Expense of Customers/11. Creating Ensembles of Models.mp457.07MB
207. 20. Predicting Health Expense of Customers/12. Comparing Performance of these Models.mp436.54MB
208. 20. Predicting Health Expense of Customers/13. More things to Try.mp448.69MB
209. 20. Predicting Health Expense of Customers/14. Major Takeaways from the Project.mp457.64MB
210. 20. Predicting Health Expense of Customers/2. Understanding the Dataset.mp4104.05MB
211. 20. Predicting Health Expense of Customers/3. Understanding the Problem Statement.mp461.8MB
212. 20. Predicting Health Expense of Customers/4. Performing Univariate Analysis.mp489.75MB
213. 20. Predicting Health Expense of Customers/5. Performing Bivariate Analysis.mp471.46MB
214. 20. Predicting Health Expense of Customers/6. Performing Multivariate Analysis.mp485.97MB
215. 20. Predicting Health Expense of Customers/7. Preparing the data for Modelling.mp490.86MB
216. 20. Predicting Health Expense of Customers/8. Applying Linear Regression Model.mp4128.08MB
217. 20. Predicting Health Expense of Customers/9. Applying Random Forest Model.mp454.39MB
218. 21. Determining Whether a Person should be Granted Loan/1. Understanding the Problem Statement.mp445.49MB
219. 21. Determining Whether a Person should be Granted Loan/10. Applying Logistic Regression.mp452.39MB
220. 21. Determining Whether a Person should be Granted Loan/11. Applying Gradient Boosting.mp438.62MB
221. 21. Determining Whether a Person should be Granted Loan/12. Summary.mp444.17MB
222. 21. Determining Whether a Person should be Granted Loan/2. Setting up the Environment.mp468.6MB
223. 21. Determining Whether a Person should be Granted Loan/3. Understanding the Dataset.mp441.13MB
224. 21. Determining Whether a Person should be Granted Loan/4. Performing Descriptive Statistics.mp475.32MB
225. 21. Determining Whether a Person should be Granted Loan/5. Data Cleaning.mp466.97MB
226. 21. Determining Whether a Person should be Granted Loan/6. Univariate Data Visualizations.mp465.17MB
227. 21. Determining Whether a Person should be Granted Loan/7. Bivariate Data Analysis.mp470.21MB
228. 21. Determining Whether a Person should be Granted Loan/8. Preparing the Data for Modelling.mp442.83MB
229. 21. Determining Whether a Person should be Granted Loan/9. Applying Resampling.mp456.96MB
230. 22. Optimizing Agricultural Production/1. Setting up the Environment.mp446.43MB
231. 22. Optimizing Agricultural Production/10. Summarizing the Key-Points.mp440.45MB
232. 22. Optimizing Agricultural Production/2. Understanding the Dataset.mp455.18MB
233. 22. Optimizing Agricultural Production/3. Understanding the Problem Statement.mp435.4MB
234. 22. Optimizing Agricultural Production/4. Performing Descriptive Statistics.mp473.57MB
235. 22. Optimizing Agricultural Production/5. Analyzing Agricultural Conditions.mp439.18MB
236. 22. Optimizing Agricultural Production/6. Clustering Similar Crops.mp463.62MB
237. 22. Optimizing Agricultural Production/7. Visualizing the Hidden Patterns.mp427.79MB
238. 22. Optimizing Agricultural Production/8. Predictive Modelling.mp440.38MB
239. 22. Optimizing Agricultural Production/9. Real Time Predictions.mp427.66MB
240. 3. Python Functions Deep Dive/1. Introduction to Functions.mp440.22MB
241. 3. Python Functions Deep Dive/10. List, set, and Dictionary Comprehensions.mp454.58MB
242. 3. Python Functions Deep Dive/12. Quiz Solution.mp440.26MB
243. 3. Python Functions Deep Dive/13. Introduction to Aggregate Functions.mp430.63MB
244. 3. Python Functions Deep Dive/14. Introduction to Analytical Functions.mp434.68MB
245. 3. Python Functions Deep Dive/16. Quiz Solution.mp438.19MB
246. 3. Python Functions Deep Dive/17. Solving the Factorial Problem using Recursion.mp455.38MB
247. 3. Python Functions Deep Dive/18. Solving the Fibonacci Problem using Recursion.mp462.68MB
248. 3. Python Functions Deep Dive/2. Default Parameters in Functions.mp453.96MB
249. 3. Python Functions Deep Dive/20. Quiz Solution.mp438.06MB
250. 3. Python Functions Deep Dive/21. Introduction to Classes and Objects.mp439.53MB
251. 3. Python Functions Deep Dive/22. Inheritance.mp432.49MB
252. 3. Python Functions Deep Dive/23. Encapsulation.mp462.2MB
253. 3. Python Functions Deep Dive/24. Polymorphism.mp446.25MB
254. 3. Python Functions Deep Dive/26. Quiz Solution.mp440.47MB
255. 3. Python Functions Deep Dive/3. Positional Arguments.mp432.11MB
256. 3. Python Functions Deep Dive/4. Keyword Arguments.mp436.24MB
257. 3. Python Functions Deep Dive/5. Python Modules.mp442.7MB
258. 3. Python Functions Deep Dive/7. Quiz Solution.mp447.69MB
259. 3. Python Functions Deep Dive/8. Lambda Functions.mp453.14MB
260. 3. Python Functions Deep Dive/9. Filter, Map, and Zip Functions.mp479.87MB
261. 4. Python for Data Science/1. Introduction to datetime.mp437.49MB
262. 4. Python for Data Science/10. Sets for Regular Expressions.mp456.13MB
263. 4. Python for Data Science/12. Quiz Solution.mp432.82MB
264. 4. Python for Data Science/13. Array Creation using Numpy.mp450.91MB
265. 4. Python for Data Science/14. Mathematical Operations using Numpy.mp436.44MB
266. 4. Python for Data Science/15. Built-in Functions in Numpy.mp439.99MB
267. 4. Python for Data Science/17. Quiz Solution.mp457.6MB
268. 4. Python for Data Science/18. Reading Datasets using Pandas.mp465.75MB
269. 4. Python for Data Science/19. Plotting Data in Pandas.mp435.74MB
270. 4. Python for Data Science/2. The date and time class.mp433.55MB
271. 4. Python for Data Science/20. Indexing, Selecting, and Filtering Data using Pandas.mp468.92MB
272. 4. Python for Data Science/21. Merging and Concatenating DataFrames.mp476.57MB
273. 4. Python for Data Science/22. Lambda, Map, and Apply Functions.mp437.2MB
274. 4. Python for Data Science/24. Quiz Solution.mp454.71MB
275. 4. Python for Data Science/3. The datetime class.mp422.57MB
276. 4. Python for Data Science/4. The timedelta class.mp419.36MB
277. 4. Python for Data Science/6. Quiz Solution.mp444.07MB
278. 4. Python for Data Science/7. Meta Characters for Regular Expressions.mp474.03MB
279. 4. Python for Data Science/8. Built-in Functions for Regular Expressions.mp437.57MB
280. 4. Python for Data Science/9. Special Characters for Regular Expressions.mp440.92MB
281. 5. Data Cleaning/1. Causes and Impact of Missing Values.mp464.37MB
282. 5. Data Cleaning/10. Finding out Outliers from the Data.mp463.24MB
283. 5. Data Cleaning/11. Using Winsorization to deal with Outliers.mp450.55MB
284. 5. Data Cleaning/12. Deleting and Capping the Outliers.mp460.76MB
285. 5. Data Cleaning/13. Dealing with Outliers in a real-world scenario.mp450.9MB
286. 5. Data Cleaning/15. Quiz Solution.mp456.09MB
287. 5. Data Cleaning/16. Introduction to reindex, set_index, reset_index, and sort_index Functions.mp444.7MB
288. 5. Data Cleaning/17. Introduction to Replace and Droplevel Function.mp432.98MB
289. 5. Data Cleaning/18. Introduction to Split and Strip Function.mp437.82MB
290. 5. Data Cleaning/19. Introduction to Stack, and Unstack Functions.mp425.39MB
291. 5. Data Cleaning/2. Types of Missing Values.mp461.82MB
292. 5. Data Cleaning/20. Introduction to Melt, Explode, and Squeeze Functions.mp441.38MB
293. 5. Data Cleaning/21. Data Cleaning on Big Mart Dataset.mp438.3MB
294. 5. Data Cleaning/22. Data Cleaning on Movie Dataset.mp437.3MB
295. 5. Data Cleaning/23. Data Cleaning on Melbourne Housing Dataset.mp442.14MB
296. 5. Data Cleaning/24. Data Cleaning on Naukri Dataset.mp4106.25MB
297. 5. Data Cleaning/3. When should we delete the Missing values.mp479.62MB
298. 5. Data Cleaning/4. Imputing the Missing Values using the Business Logic.mp473.91MB
299. 5. Data Cleaning/5. Imputing Missing Values using MeanMedianMode.mp455.96MB
300. 5. Data Cleaning/6. Imputing Missing Values in a real-time scenario.mp482.55MB
301. 5. Data Cleaning/8. Quiz Solution.mp449.16MB
302. 5. Data Cleaning/9. How Outliers can be harmful for Machine Learning Models.mp469.04MB
303. 6. Data Visualizations/1. Univariate Analysis.mp457.06MB
304. 6. Data Visualizations/10. Statistical Charts.mp438.38MB
305. 6. Data Visualizations/11. Polar Charts.mp429.3MB
306. 6. Data Visualizations/12. Subplots.mp434.8MB
307. 6. Data Visualizations/13. 3D Charts.mp424.57MB
308. 6. Data Visualizations/14. Waffle Charts.mp429.36MB
309. 6. Data Visualizations/15. Maps.mp430.72MB
310. 6. Data Visualizations/17. Quiz Solution.mp448.84MB
311. 6. Data Visualizations/18. Animation with Bubbleplot.mp447.79MB
312. 6. Data Visualizations/19. Animation with Facets.mp426.71MB
313. 6. Data Visualizations/2. Bivariate Analysis.mp445MB
314. 6. Data Visualizations/20. Animation with Scatter Maps.mp422.65MB
315. 6. Data Visualizations/21. Animation with Choropleth Maps.mp430.58MB
316. 6. Data Visualizations/23. Quiz Solution.mp434.58MB
317. 6. Data Visualizations/24. Introduction to Ipywidgets.mp438.56MB
318. 6. Data Visualizations/25. Interactive Univariate Analysis.mp429.89MB
319. 6. Data Visualizations/26. Interactive Bivariate Analysis.mp433.86MB
320. 6. Data Visualizations/27. Interactive Multivariate Analysis.mp429.18MB
321. 6. Data Visualizations/29. Quiz Solution.mp453.83MB
322. 6. Data Visualizations/3. Multivariate Analysis.mp470.84MB
323. 6. Data Visualizations/30. Sunburst Charts.mp433.14MB
324. 6. Data Visualizations/31. Parallel Co-ordinate Charts.mp422.97MB
325. 6. Data Visualizations/32. Funnel Charts.mp439.14MB
326. 6. Data Visualizations/33. Gantt Charts.mp425.09MB
327. 6. Data Visualizations/34. Ternary Charts.mp420.37MB
328. 6. Data Visualizations/35. Tree Maps.mp421.46MB
329. 6. Data Visualizations/36. Network Charts.mp439.75MB
330. 6. Data Visualizations/38. Quiz Solution.mp438.52MB
331. 6. Data Visualizations/5. Quiz Solution.mp447.09MB
332. 6. Data Visualizations/6. Scatter Plots.mp445.16MB
333. 6. Data Visualizations/7. Charts with Colorscale.mp431.82MB
334. 6. Data Visualizations/8. Bar, Line, and Area Charts.mp448.54MB
335. 6. Data Visualizations/9. Facet Grids.mp437.93MB
336. 7. Feature Engineering/1. Introduction to Feature Engineering.mp460.04MB
337. 7. Feature Engineering/10. Finding the Words, Characters, and Punctuation Count.mp436.29MB
338. 7. Feature Engineering/11. Counting Nouns and Verbs in the Text.mp431.42MB
339. 7. Feature Engineering/12. Counting Adjectives, Adverb, and Pronouns.mp423.71MB
340. 7. Feature Engineering/13. Introduction to Assign and Update Functions.mp436.13MB
341. 7. Feature Engineering/14. Introduction to at_time and between_time Functions.mp430.23MB
342. 7. Feature Engineering/15. Introduction to nlargest and nsmallest Functions.mp435.33MB
343. 7. Feature Engineering/16. Introduction to Expanding Function.mp428.43MB
344. 7. Feature Engineering/17. Introduction to Cumulative Functions.mp431.11MB
345. 7. Feature Engineering/19. Quiz Solution.mp451.21MB
346. 7. Feature Engineering/2. Removing Unnecessary Columns.mp456.87MB
347. 7. Feature Engineering/20. Feature Engineering on Employee Data.mp457.14MB
348. 7. Feature Engineering/21. Feature Engineering on FIFA Data.mp444.76MB
349. 7. Feature Engineering/22. Feature Engineering on Hotel Reviews.mp435.06MB
350. 7. Feature Engineering/23. Feature Engineering on Marketing Data.mp458.59MB
351. 7. Feature Engineering/24. Feature Engineering on Titanic Data.mp449.63MB
352. 7. Feature Engineering/26. Quiz Solution.mp464.84MB
353. 7. Feature Engineering/3. Decomposing Time and Date Features.mp438.3MB
354. 7. Feature Engineering/4. Decomposing Categorical Features.mp438.28MB
355. 7. Feature Engineering/5. Binning Numerical Features.mp459.36MB
356. 7. Feature Engineering/6. Aggregating Features.mp456.88MB
357. 7. Feature Engineering/7. Introduction to Feature Engineering on Text Data.mp433.83MB
358. 7. Feature Engineering/8. Reading and Summarizing the Text.mp430.48MB
359. 7. Feature Engineering/9. Finding the Length, Polarity and Subjectivity.mp473.01MB
360. 8. Data Processing/1. Types of Encoding Techniques.mp460.89MB
361. 8. Data Processing/10. Log transformation.mp428.02MB
362. 8. Data Processing/11. BoxCox transformation.mp432.52MB
363. 8. Data Processing/13. Train, Test and Validation Split.mp444.24MB
364. 8. Data Processing/14. Standardization and Normalization.mp439.71MB
365. 8. Data Processing/2. Label Encoding.mp433.54MB
366. 8. Data Processing/3. Feature Mapping for Ordinal Variables.mp429.02MB
367. 8. Data Processing/4. OneHot Encoding.mp434.58MB
368. 8. Data Processing/5. Binary and BaseN Encoding.mp433.22MB
369. 8. Data Processing/6. Mean and Frequency Encoding.mp422.84MB
370. 8. Data Processing/8. Introduction to Skewness and Normal Distribution.mp437.55MB
371. 8. Data Processing/9. Square and Cube Root Transformation.mp439.42MB
372. 9. Linear Regression/1. Introduction to Linear Regression.mp481.22MB
373. 9. Linear Regression/10. Industry relevance of linear regression.mp449.88MB
374. 9. Linear Regression/2. Implementing Linear Regression using Sklearn.mp473.45MB
375. 9. Linear Regression/3. Feature Selection using RFECV.mp485.91MB
376. 9. Linear Regression/4. Data Transformation with Linear Regression.mp457.52MB
377. 9. Linear Regression/5. Applying Cross Validation.mp4105.62MB
378. 9. Linear Regression/6. Analyzing the performance of Regression models.mp4108.97MB
379. 9. Linear Regression/7. R2 score and adjuted R2 score intuition.mp4107.03MB
380. 9. Linear Regression/8. MAE, RMSE, R2 and Adjusted R2 in code.mp449MB
381. 9. Linear Regression/9. Applying real time prediction on our model.mp4107.61MB