Coding Blocks - Data Science Master Course Courses Ghar

1. 1. Course Introduction/1.Data Science Course Introduction.mp4128.4MB
2. 10. Asynchronous Programming in Python (Optional)/1. Async Programming Python - 1.mp473.28MB
3. 10. Asynchronous Programming in Python (Optional)/2. Async Programming Python - 2.mp457.87MB
4. 10. Asynchronous Programming in Python (Optional)/3. Async Programming Python - 3.mp473.64MB
5. 10. Asynchronous Programming in Python (Optional)/4. Python - Coroutines in Python.mp443.75MB
6. 10. Asynchronous Programming in Python (Optional)/5. Python - AsyncIO 1.mp4131.5MB
7. 10. Asynchronous Programming in Python (Optional)/6. Python - AsyncIO 2.mp488.54MB
8. 11. Basics of Git & Github/1.Introduction_to_Git_and_Github.mp4106.24MB
9. 11. Basics of Git & Github/2.Making_a_Repository_on_Github.mp473.74MB
10. 11. Basics of Git & Github/3.Cloning_a_Repository_from_Github.mp440.63MB
11. 12. Data Acquisition - Web Scrapping/1.Web_Scraping_01-Fetching_Data.mp468.8MB
12. 12. Data Acquisition - Web Scrapping/2.Web_Scraping_02-Using_Beautiful_Soup.mp470.28MB
13. 12. Data Acquisition - Web Scrapping/3.Web_Scraping_03-Parsing_HTML_Tables.mp450.29MB
14. 12. Data Acquisition - Web Scrapping/4.Web_Scraping_04-Creating_CSV.mp416.85MB
15. 12. Data Acquisition - Web Scrapping/5.Web_Scraping_05-Cleaning_Data.mp458.39MB
16. 12. Data Acquisition - Web Scrapping/6.Web_Scraping_06-Scraping_Loca_Files.mp49.17MB
17. 13. Data Acquisition - Using Web APIs/1.Web_APIs_01-OpenWeatherMap.mp447.97MB
18. 13. Data Acquisition - Using Web APIs/2.Web_API's_03-Goolge_API-Authentication.mp436.9MB
19. 13. Data Acquisition - Using Web APIs/3.Web_API's_2-Using_Facebook_API.mp449.42MB
20. 13. Data Acquisition - Using Web APIs/4.Web_Scraping-Image_Scrapping-1.mp418.12MB
21. 13. Data Acquisition - Using Web APIs/5.Web Scraping - Scraping Images II.mp416.03MB
22. 15. Data Acquisition - Web Crawler using Scrapy/1.Scrapy-Getting_Started.mp423.71MB
23. 15. Data Acquisition - Web Crawler using Scrapy/3.Scrapy-Creating_our_first_Spider.mp431.37MB
24. 15. Data Acquisition - Web Crawler using Scrapy/4.Scrapy-Using_Shell__Selectors.mp4109.96MB
25. 15. Data Acquisition - Web Crawler using Scrapy/5.Scrapy-Parsing_response_as_JSON.mp439.55MB
26. 15. Data Acquisition - Web Crawler using Scrapy/6.Scrapy-Recursive_Crawler.mp496.23MB
27. 16. 🚀 Challenge - Scrape a Shopping Website/2. Scrape Pepperfry Challenge.mp48.49MB
28. 16. 🚀 Challenge - Scrape a Shopping Website/3. Scrape Pepperfry Hint Video.mp458.58MB
29. 17. Project - Automating Codechef Submissions (Selenium)/1.Web Automation with Selenium.mp444.63MB
30. 18. Project - Creating a Telegram Bot/1. Registering a Telegram Bot.mp49.56MB
31. 18. Project - Creating a Telegram Bot/2. Creating an Echo Bot.mp452.67MB
32. 18. Project - Creating a Telegram Bot/3. Setting Up Webhook for Telegram Bot.mp438.53MB
33. 18. Project - Creating a Telegram Bot/4. Introduction to Dialogflow.mp436.95MB
34. 18. Project - Creating a Telegram Bot/5. Making a Conversational Bot.mp445.81MB
35. 18. Project - Creating a Telegram Bot/6. Setting up Custom Keyboard for Telegram Bot.mp420MB
36. 18. Project - Creating a Telegram Bot/7. Deploying Flask App for Telegram Bot on Heroku.mp430.24MB
37. 19. Getting started with Machine Learning/1. Machine Learning Pipeline.mp446.8MB
38. 19. Getting started with Machine Learning/2. Supervised Learning Introduction.mp436.6MB
39. 19. Getting started with Machine Learning/3. Unsupervised Learning Introduction.mp420.14MB
40. 19. Getting started with Machine Learning/4. RL1 - Reinforcement Learning Introduction.mp446.15MB
41. 2 .Data Science Quickstart Mode/2. Quickstart Python.mp445.84MB
42. 2 .Data Science Quickstart Mode/3. Quickstart Numpy.mp420.85MB
43. 2 .Data Science Quickstart Mode/4. Quickstart - Data Visualization.mp415.06MB
44. 2 .Data Science Quickstart Mode/5. Quickstart - OpenCV.mp428.72MB
45. 2 .Data Science Quickstart Mode/6. Quickstart - Pandas.mp465.44MB
46. 20. Numpy/1.Python - NumPy Basics.mp495.47MB
47. 20. Numpy/2.Python - Random Generators NumPy.mp432.91MB
48. 20. Numpy/3.Python - Statistical Computation using.mp453.83MB
49. 21. Linear Algebra/2.Linear Algebra - Matrices, Tensors, Transpose.mp449.19MB
50. 21. Linear Algebra/3.Linear Algebra - Broadcasting, Matrix, Hadamard product.mp422.81MB
51. 21. Linear Algebra/4.Linear Algebra - Norm, Det, Inverse, Linear Equations.mp444.65MB
52. 22. Data Visualisation/DV 01 - Line Plots.mp427.16MB
53. 22. Data Visualisation/DV 02 - Scatter Plots.mp412.38MB
54. 22. Data Visualisation/DV 03 - Bar Graphs.mp422.72MB
55. 22. Data Visualisation/DV 04 - Pie Charts.mp418.67MB
56. 22. Data Visualisation/DV 05 - Normal Distribution.mp428.1MB
57. 22. Data Visualisation/DV 06 - Histograms.mp412.04MB
58. 22. Data Visualisation/DV 07 - Movie Data Visualization.mp432.66MB
59. 23. Seaborn/Seaborn 1.mp428.8MB
60. 23. Seaborn/Seaborn 2.mp422.33MB
61. 23. Seaborn/Seaborn 3.mp423.8MB
62. 24. Pandas/1.Pandas Basics - 1.mp428.91MB
63. 24. Pandas/2.Pandas Basics - 2.mp452.34MB
64. 24. Pandas/3.Pandas - MNIST Dataset.mp447.88MB
65. 24. Pandas/4.Pandas - Movie Dataset.mp422.54MB
66. 25. Project - Movie Recommendation System/Movie Recommendation - 1.mp412.57MB
67. 25. Project - Movie Recommendation System/Movie Recommendation - 2.mp421.5MB
68. 25. Project - Movie Recommendation System/Movie Recommendation - 3.mp427.52MB
69. 25. Project - Movie Recommendation System/Movie Recommendation - 4.mp445.37MB
70. 26. Probability Distribution & Statistics/1.Data Visualisation - Normal Distribution and Histogram.mp487.47MB
71. 26. Probability Distribution & Statistics/2.Data Visualisation - Normal Distribution - II.mp443.23MB
72. 26. Probability Distribution & Statistics/3.Multivariate NormalGaussian Distribution Function.mp481.62MB
73. 26. Probability Distribution & Statistics/4.Data Visualisation - Multivariate NormalGaussian Distribution Using Numpy.mp461.72MB
74. 26. Probability Distribution & Statistics/5.ML Interview Question - Std Deviation in a Running Stream.mp441.8MB
75. 27. K-Nearest Neighbours/1.K-Nearest Neigbours Introduction.mp410.59MB
76. 27. K-Nearest Neighbours/2.K-Nearest Neighbours Implementation.mp466.29MB
77. 27. K-Nearest Neighbours/3.Project Recognizing MNIST Handwritten Digits using KNN.mp467.44MB
78. 29. Project - Face Recognition/2.OpenCV - Working with Images I.mp426.61MB
79. 29. Project - Face Recognition/3.OpenCV - Working with Images - II.mp426.09MB
80. 29. Project - Face Recognition/4.OpenCV - Face Detection using HaarCascades.mp492.96MB
81. 29. Project - Face Recognition/5.OpenCV - Working with Video Stream from WebCam.mp438.49MB
82. 29. Project - Face Recognition/6.Face Recognition Project - Generating Selfie Training Data using WebCam.mp4150.37MB
83. 29. Project - Face Recognition/7.Face Recognition - Building Face Classsifier.mp4155.92MB
84. 29. Project - Face Recognition/8.Face Recognition - Testing our Classifier.mp428.69MB
85. 3. Python 01 - Basics/1. Python 3 Installation [Windows].mp415.76MB
86. 3. Python 01 - Basics/10. Control Flow II.mp414.9MB
87. 3. Python 01 - Basics/11. Operator and Expression I.mp420.33MB
88. 3. Python 01 - Basics/12. Operator and Expression II.mp428.37MB
89. 3. Python 01 - Basics/13. Operator and Expression III.mp436.08MB
90. 3. Python 01 - Basics/2. Getting Started I.mp416.26MB
91. 3. Python 01 - Basics/3. Getting Started II.mp411.68MB
92. 3. Python 01 - Basics/4. Working with Jupyter Notebooks.mp428.8MB
93. 3. Python 01 - Basics/5. Set up Jupyter-Themes.mp411.99MB
94. 3. Python 01 - Basics/6. Python Basics..mp419.23MB
95. 3. Python 01 - Basics/7. Python - Variables And Arithmetic Operators.mp440.76MB
96. 3. Python 01 - Basics/9. Control Flow I.mp427.37MB
97. 31. Linear Regression/1.Linear Regression.mp432.09MB
98. 31. Linear Regression/10.Linear Regression - Visualising Loss Function & Gradient Descent Trajectory.mp434.23MB
99. 31. Linear Regression/11.Interactive Plots using Matplotlib.mp412.61MB
100. 31. Linear Regression/2.Gradient Descent Implementation.mp417.2MB
101. 31. Linear Regression/3.Gradient Descent Algorithm.mp415.95MB
102. 31. Linear Regression/4.Gradient Descent Update Rule for Regression.mp414.37MB
103. 31. Linear Regression/5.Linear Regression - Data Preparation.mp410.32MB
104. 31. Linear Regression/6.Linear Regression - Implementing Gradient Descent.mp418.32MB
105. 31. Linear Regression/7.Linear Regression - Making Predictions & Submitting Online Challenge.mp411.98MB
106. 31. Linear Regression/8.Linear Regression Code - Scoring.mp47.42MB
107. 31. Linear Regression/9.Surface Plots and Contours.mp426.52MB
108. 33. Linear Regression - II Multiple Features/1.Linear Regression - Maths for Multiple Features.mp429.7MB
109. 33. Linear Regression - II Multiple Features/2.Boston Housing Dataset.mp419.39MB
110. 33. Linear Regression - II Multiple Features/3.Linear Regression - Loop Based Implementation for Multiple Features.mp422.79MB
111. 33. Linear Regression - II Multiple Features/4.Linear Regression - Efficient Code using Vectorization.mp422.91MB
112. 34. Sci-kit Learn Introduction/Sklearn 01 - Generating Regression Data.mp417.33MB
113. 34. Sci-kit Learn Introduction/Sklearn 02 - Implementation Regression Model.mp46.62MB
114. 35 .Optimisation Algorithms/01 GD vs Mini Batch vs SGD.mp415.63MB
115. 35 .Optimisation Algorithms/02 Mini Batch GD.mp414.44MB
116. 35 .Optimisation Algorithms/03 Mini Batch GD Implementation & Advantanges.mp446.12MB
117. 36 .Locally Weighted Regression (LOWESS)/1.Closed Form Solution of Linear Regression.mp454.06MB
118. 36 .Locally Weighted Regression (LOWESS)/2.Closed Form Solution - Code Tutorial.mp429.82MB
119. 36 .Locally Weighted Regression (LOWESS)/4.Locally Weighted Regression (LOWESS).mp457.67MB
120. 36 .Locally Weighted Regression (LOWESS)/5.LOWESS - Deriving Closed Form Solution.mp452.6MB
121. 36 .Locally Weighted Regression (LOWESS)/6.LOWESS Implementation 1 - Data Preparation.mp451.9MB
122. 36 .Locally Weighted Regression (LOWESS)/7.LOWESS Implementation 2 - Computing W.mp452.76MB
123. 36 .Locally Weighted Regression (LOWESS)/8.LOWESS Implementation 3 - Making Predictions.mp444.06MB
124. 36 .Locally Weighted Regression (LOWESS)/9.LOWESS Implementation 4 - Effect of Bandwidth Parameter.mp429MB
125. 37. Maximum Likelihood Estimate (MLE) [Proof]/1.Linear Regression - Maximum Likelihood Estimation - I (Optional).mp446.32MB
126. 37. Maximum Likelihood Estimate (MLE) [Proof]/2.Linear Regression - Maximum Likelihood Estimation - II (Optional).mp432.71MB
127. 39. Logistic Regression/1.Logistic 01 - Introduction.mp425.15MB
128. 39. Logistic Regression/10.Logistic 10 - Decision Surface Visualisation.mp48.8MB
129. 39. Logistic Regression/11.Logistic 11 - Prediction & Accuracy.mp413.2MB
130. 39. Logistic Regression/12.Logistic Regression Using Sk-Learn.mp49.06MB
131. 39. Logistic Regression/2.Logistic 02 - Loss Function.mp419.04MB
132. 39. Logistic Regression/3.Logistic 03 - Maximum Likelihood Estimates.mp420.52MB
133. 39. Logistic Regression/4.Logistic 04 - Importance of Maximising Likelihood.mp410.71MB
134. 39. Logistic Regression/5.Logistic 05 - Gradient Descent Update.mp415.71MB
135. 39. Logistic Regression/6.Logistic 06 - Data Preparation.mp424.17MB
136. 39. Logistic Regression/7.Logistic 07 - Data Normalisation.mp414.91MB
137. 39. Logistic Regression/8.Logistic 08 - Implementation - I.mp419.21MB
138. 39. Logistic Regression/9.Logistic 09 - Implementation - II.mp428.43MB
139. 4. Python 02 - Functions/1. Introduction to Functions.mp410.67MB
140. 4. Python 02 - Functions/2. Python Functions - Return, Local, Global.mp416.96MB
141. 4. Python 02 - Functions/3. Python Functions - packing arguments.mp418.49MB
142. 4. Python 02 - Functions/4. Python Functions - Lambda Functions.mp48.24MB
143. 4. Python 02 - Functions/5. Python Functions - Decorators.mp417.73MB
144. 4. Python 02 - Functions/6. Python Functions - -args and --kwargs.mp416.82MB
145. 41. Data Prepreprocessing/1.Data Preprocessing - Normalisation - Standardisation.mp431.92MB
146. 42. Feature Selection/1.Feature Selection Intro.mp414.08MB
147. 42. Feature Selection/2.Types of Feature Selection.mp427.38MB
148. 42. Feature Selection/3.Feature Selection Code - I.mp422.22MB
149. 42. Feature Selection/4.Feature Selection Code - II.mp431.65MB
150. 42. Feature Selection/5.Feature Selection Conclusion.mp412.51MB
151. 43 .PCA/1.Intro to PCA.mp46.24MB
152. 43 .PCA/2.Applications of PCA.mp427.32MB
153. 43 .PCA/3.PCA Objective.mp415.22MB
154. 43 .PCA/4.PCA Algorithm.mp438.91MB
155. 43 .PCA/5.PCA Code 1.mp426.01MB
156. 43 .PCA/6.PCA Code 2.mp433.2MB
157. 44. Natural Language Pre-preprocessing/1.Natural Language Processing - Getting started with NLTK.mp425.16MB
158. 44. Natural Language Pre-preprocessing/2.NLTK - Bag of Words Pipeline.mp415.54MB
159. 44. Natural Language Pre-preprocessing/3.NLTK - Tokenization & Stopword Removal.mp418.96MB
160. 44. Natural Language Pre-preprocessing/4.NLTK - Regex Based Tokenization.mp410.19MB
161. 44. Natural Language Pre-preprocessing/5.NLTK - Stemming & Lemmatization.mp49.65MB
162. 44. Natural Language Pre-preprocessing/6.Bag of Words - Constructing Vocab.mp421.01MB
163. 44. Natural Language Pre-preprocessing/7.Bag of Words - Vectorization with Stopword Removal.mp413.18MB
164. 44. Natural Language Pre-preprocessing/8.Bag of Words Model - Bigrams, Trigrams, Ngrams.mp411.04MB
165. 44. Natural Language Pre-preprocessing/9.Bag of Words - TF-IDF Normalisation.mp417.69MB
166. 45 .Naive Bayes Classifier/1.Bayes Theorem Formula and Proof.mp421.32MB
167. 45 .Naive Bayes Classifier/10.Naive Bayes for Text Classification.mp421.51MB
168. 45 .Naive Bayes Classifier/11.Laplace Smoothing (Multinomial NB).mp424.53MB
169. 45 .Naive Bayes Classifier/12.Multivariate Bernoulli Naive Bayes.mp429.77MB
170. 45 .Naive Bayes Classifier/13.Multinomial Event Model Naive Bayes.mp410.22MB
171. 45 .Naive Bayes Classifier/15.Multivariate Bernoulli vs Multinomial Naive Bayes.mp449.07MB
172. 45 .Naive Bayes Classifier/16.Gaussian Naive Bayes - Handling Continuous Valued Features.mp463.27MB
173. 45 .Naive Bayes Classifier/17.MNIST Classification - Multinomial Vs Gaussian Naive Bayes.mp428.19MB
174. 45 .Naive Bayes Classifier/2.Bayes Example - Spam or Not.mp426.45MB
175. 45 .Naive Bayes Classifier/4.Bayes Examples Disease or not.mp448.12MB
176. 45 .Naive Bayes Classifier/5.Naive Bayes Classifier.mp437.93MB
177. 45 .Naive Bayes Classifier/6.Naive Bayes - Mushroom Classification Example.mp431.82MB
178. 45 .Naive Bayes Classifier/7.Mushroom Classifer - Handling Categorical Data.mp464.51MB
179. 45 .Naive Bayes Classifier/8.Mushroom Classifier - Prior and Conditional Probability.mp443.91MB
180. 45 .Naive Bayes Classifier/9.Mushroom Classification - Prediction using Posterior Prob.mp451.18MB
181. 46 .Project - Movie Review Classification/2.Textual Data Cleaning I - NLP Pipeline.mp459.93MB
182. 46 .Project - Movie Review Classification/3.Textual Data Cleaning II - Working with Files.mp455.11MB
183. 46 .Project - Movie Review Classification/4.Movie Review Prediction - Using Multinomial Naive Bayes.mp4107.29MB
184. 46 .Project - Movie Review Classification/5.Movie Review Prediction - Using Multivariate Bernaulli Event Model.mp468.19MB
185. 46 .Project - Movie Review Classification/6.Precision, Recall and Confusion Matrix.mp434.69MB
186. 46 .Project - Movie Review Classification/7.Confusion Matrix.mp480.96MB
187. 48 .Decision Trees & Random Forests/1.DT 1 - Introduction to Decision Trees.mp437.35MB
188. 48 .Decision Trees & Random Forests/2.DT 2 - Entropy & Information Gain.mp465.84MB
189. 48 .Decision Trees & Random Forests/3.DT 3 - Process Kaggle Titanic Dataset.mp476.03MB
190. 48 .Decision Trees & Random Forests/4.DT 4 - Implementing Information Gain.mp468.28MB
191. 48 .Decision Trees & Random Forests/5.DT 5 - Implementing Decision Tree.mp469.36MB
192. 48 .Decision Trees & Random Forests/6.DT 6 - Making Predictions.mp448.82MB
193. 48 .Decision Trees & Random Forests/7.Decision Trees using Sci-kit Learn.mp439.25MB
194. 48 .Decision Trees & Random Forests/8.Decision Trees Visualisation using Graphviz.mp450.33MB
195. 48 .Decision Trees & Random Forests/9.DT - Random Forests Ensembles.mkv59.5MB
196. 5. Python 03 - Builtin Data Structures/1. Python - Introduction to Data Structures.mp466.75MB
197. 5. Python 03 - Builtin Data Structures/2. Python - Introduction to Strings.mp453.98MB
198. 5. Python 03 - Builtin Data Structures/3. Python - String Operations.mp4106.99MB
199. 5. Python 03 - Builtin Data Structures/4. Python - Introduction to Lists.mp4132.4MB
200. 5. Python 03 - Builtin Data Structures/5. Python - Introduction to Tuples.mp486.85MB
201. 5. Python 03 - Builtin Data Structures/7. Python - Introduction to Dictionaries.mp475.32MB
202. 5. Python 03 - Builtin Data Structures/8. Python - Introduction to Sets.mp442.71MB
203. 5. Python 03 - Builtin Data Structures/9. Comprehension of Data Structure in Python.mp433.05MB
204. 50. Support Vector Machines/1.SVM - Introduction.mp477.49MB
205. 50. Support Vector Machines/10.Handling Non-Linearly Separable Data.mp4233.33MB
206. 50. Support Vector Machines/11.SVM - 'Kernel Trick' Based Formulation.mp4106.7MB
207. 50. Support Vector Machines/12.SVM - Different type of Kernels.mp4111.15MB
208. 50. Support Vector Machines/13.Grid Search - Finding the Right Hyperparameters.mp487.58MB
209. 50. Support Vector Machines/2.SVM - Formulating Objective.mp435.19MB
210. 50. Support Vector Machines/3.SVM - Objective as Constrained Convex Optmization.mp4102.85MB
211. 50. Support Vector Machines/4.SVM - Handling Outliers.mp436.06MB
212. 50. Support Vector Machines/5.VM - Pegasos Algorithm for Unconstrained Optimization.mp485.91MB
213. 50. Support Vector Machines/6.SVM - Weight and Bias Update Rule.mp418.62MB
214. 50. Support Vector Machines/7.SVM Implementation 1 - Hinge Loss Function.mp464.19MB
215. 50. Support Vector Machines/8.SVM Implementation 2 - Training using Mini-Batch Gradient Descent.mp4122.52MB
216. 50. Support Vector Machines/9.SVM - Visualizing Hyperplanes, Effect of Penalty Constant.mp447.08MB
217. 51. Project - Image Classification using SVM/1.Multiclass Classification - One Vs Rest and One Vs One.mp451.75MB
218. 51. Project - Image Classification using SVM/2.Data Preparation-I Reading & Processing Images.mp4141.54MB
219. 51. Project - Image Classification using SVM/3.Data Preparation - II Creating One Vs One Data.mp476.57MB
220. 51. Project - Image Classification using SVM/4.Implementing One Vs One Scheme.mp440.11MB
221. 51. Project - Image Classification using SVM/5.Handling Multiclass Predictions using Binary Classifier.mp430.99MB
222. 51. Project - Image Classification using SVM/6.Improving Classification Accuracy.mp463.05MB
223. 53.Clustering Fundamentals (Unsupervised)/1.Introduction to K-Means.mp462.66MB
224. 53.Clustering Fundamentals (Unsupervised)/2.K-Means - Implementing E Step.mp454.67MB
225. 53.Clustering Fundamentals (Unsupervised)/3.K-Means - Implementing M-Step.mp480.33MB
226. 53.Clustering Fundamentals (Unsupervised)/4.K-Means - Understanding Loss, Coordinate Ascent.mp492.62MB
227. 53.Clustering Fundamentals (Unsupervised)/5.K-Means ++ Making Better Initialisation.mp465.93MB
228. 53.Clustering Fundamentals (Unsupervised)/6.K-Means can still Fail!.mp417.62MB
229. 53.Clustering Fundamentals (Unsupervised)/7.K-Means vs DBSCAN.mp460.87MB
230. 54. Project - Extracting Dominant Colors/1.K-Means - Dominant Color Extraction.mp464.41MB
231. 54. Project - Extracting Dominant Colors/2.K-Means - Extracting Color Swatches.mp451.19MB
232. 54. Project - Extracting Dominant Colors/3.K-Means - Image Segmentation.mp442.19MB
233. 56. Deep Learning Introduction/1. Perceptron 01 - Artificial vs Biological Neurons.mkv26.13MB
234. 56. Deep Learning Introduction/2. Perceptron 02 - How does an artificial neuron learn.mkv74.41MB
235. 56. Deep Learning Introduction/3.Perceptron_03-Gradient_Descent_Update.mp4120.77MB
236. 56. Deep Learning Introduction/4.Perceptron_04-Implementation_Part-I.mp4152.63MB
237. 56. Deep Learning Introduction/5.Perceptron_05-Visualising_Decision_Surcace.mp4121.61MB
238. 56. Deep Learning Introduction/6.Neural Network 01 - Introduction.mp4109.62MB
239. 56. Deep Learning Introduction/7.Neural Networks 02 - Gradient Descent.mp4100.15MB
240. 56. Deep Learning Introduction/8.Neural Networks 03 - Backpropagation.mp4111.81MB
241. 56. Deep Learning Introduction/9.Neural Networks 04 - Backprop Calculus.mp425.55MB
242. 57. Neural Networks - MLP's/1.MLP 01 - Multiplayer Perception Architecture.mp4142.87MB
243. 57. Neural Networks - MLP's/10.NN - Implementation Backpropagtion.mp452.87MB
244. 57. Neural Networks - MLP's/11.NN - One Hot Vectors.mp461.6MB
245. 57. Neural Networks - MLP's/12.NN - Training Your Model.mp441.45MB
246. 57. Neural Networks - MLP's/13.NN - Finding Accuracy and Visualising Decison Surface.mp429.92MB
247. 57. Neural Networks - MLP's/14.NN - XOR Classification.mp445.27MB
248. 57. Neural Networks - MLP's/15.NN - Comparing performance on other datasets.mp448.08MB
249. 57. Neural Networks - MLP's/2.MLP 02 - Implementing a 3 Layer Architecture.mp469.85MB
250. 57. Neural Networks - MLP's/3.MLP 03 - Understanding Forward Propagation.mp4135.78MB
251. 57. Neural Networks - MLP's/4.MLP 04 - Vectorization, Implementation and Softmax.mp4261.15MB
252. 57. Neural Networks - MLP's/5.MLP 05 - Backpropagation for Output Neurons.mp4117.63MB
253. 57. Neural Networks - MLP's/6.MLP 06 - Backpropagation for Hidden Neurons.mp486.93MB
254. 57. Neural Networks - MLP's/7.MLP 07 - Backpropagation for Cross Entropy Loss.mp4158.82MB
255. 57. Neural Networks - MLP's/8.MLP 08 - Vectorizing Backpropagation for m examples.mp4105.01MB
256. 57. Neural Networks - MLP's/9.NN - Vanishing Gradients.mp490.76MB
257. 58. Project - Image Classsification using Neural Network/1-NN - Pokemon Dataset Preparation.mp453.08MB
258. 58. Project - Image Classsification using Neural Network/2-NN - Pokemon Classfication & Overfitting .mp479.98MB
259. 58. Project - Image Classsification using Neural Network/3-NN - Pokemon Classification Report & Confusion Matrix.mp460.33MB
260. 59. Project - IMDB Sentiment Analysis/Sentiment Analysis 1 - Preparing IMDB Data.mp450.46MB
261. 59. Project - IMDB Sentiment Analysis/Sentiment Analysis 2 - Buidling & Compiling Neural Network.mp425.43MB
262. 59. Project - IMDB Sentiment Analysis/Sentiment Analysis 3 - Evaluation & Early Stopping.mp4113.15MB
263. 6. Python 04 - Object Oriented Programming & Modules/1. Python Class 01.mp4157.97MB
264. 6. Python 04 - Object Oriented Programming & Modules/2. Python Class 02.mp493.45MB
265. 6. Python 04 - Object Oriented Programming & Modules/3. Python Class 03.mp487.82MB
266. 6. Python 04 - Object Oriented Programming & Modules/4. Python Class 04.mp493.72MB
267. 6. Python 04 - Object Oriented Programming & Modules/5. Python Class 05.mp482.77MB
268. 6. Python 04 - Object Oriented Programming & Modules/6. Python Class 06.mp461.66MB
269. 6. Python 04 - Object Oriented Programming & Modules/7. Python Modules 1.mp472.25MB
270. 6. Python 04 - Object Oriented Programming & Modules/8. Python Modules 2.mp455.1MB
271. 6. Python 04 - Object Oriented Programming & Modules/9. Python Modules 3.mp476.96MB
272. 61. Convolutional Neural Networks/CNN 00 - Why do we need them .mp4.mkv19.24MB
273. 61. Convolutional Neural Networks/CNN 01 - What is Convolution .mp443.44MB
274. 61. Convolutional Neural Networks/CNN 02 - Implementing Convolution, Understanding Filters.mp451.73MB
275. 61. Convolutional Neural Networks/CNN 03 - Convolution Layer.mp474.27MB
276. 61. Convolutional Neural Networks/CNN 04 - Strides and Padding.mp486.75MB
277. 61. Convolutional Neural Networks/CNN 05 - Pooling Layers.mp439.53MB
278. 61. Convolutional Neural Networks/CNN 06 Pooling Implementation.mp421.42MB
279. 61. Convolutional Neural Networks/CNN 07 - Dropouts.mp439.23MB
280. 62. Training - Data Loaders, Augmentation, Colab/1. Uploading Data on Google Colab.mp455.58MB
281. 62. Training - Data Loaders, Augmentation, Colab/2. Creating Data Generators (for large datasets).mp4110.36MB
282. 62. Training - Data Loaders, Augmentation, Colab/3. Working with OS Module, creating Val Dir.mp437.83MB
283. 62. Training - Data Loaders, Augmentation, Colab/4. Training using 'fit_generator', Visualizing Results.mp496.05MB
284. 62. Training - Data Loaders, Augmentation, Colab/5. Image Pipelines - 1 Data Augmentation on the 'fly.mp4181.06MB
285. 62. Training - Data Loaders, Augmentation, Colab/6. Image Pipelines 2 - Handling Validation Data.mp489.93MB
286. 62. Training - Data Loaders, Augmentation, Colab/7. Data Augmentation using ImgAug (Webinar).mp4223.14MB
287. 63. Project - COVID Detection using CNN/1.Covid Detection using X-Rays (Webinar).mp4267.22MB
288. 64. CNN Case Studies/1. Case Study 1 - Alexnet.mp4170.18MB
289. 64. CNN Case Studies/2. Case Study - ZF Net, VGG.mp4170.18MB
290. 64. CNN Case Studies/3. Case Study - GoogleNet, Inception Module.mp4101.86MB
291. 64. CNN Case Studies/4. Mobilenets Paper Discussion Webinar (CNN).mp4138.57MB
292. 65. Digging Deeper into Convnets/1. Building Convnets 1 - Filter Sizes, Receptive Fields.mp480.6MB
293. 65. Digging Deeper into Convnets/2. Building Convnets 3 - Effect of 'Pooling' Layers.mp496.81MB
294. 65. Digging Deeper into Convnets/3. Building Convnets 2 - Training CNN in Keras.mp448.59MB
295. 65. Digging Deeper into Convnets/4. Image Data Augmentation.mp456.93MB
296. 66. Transfer Learning/1. Transfer Learning Introduction.mp450.86MB
297. 66. Transfer Learning/2. Transfer Learning - 2 Using Pretrained Models.mp489.65MB
298. 66. Transfer Learning/3. Transfer Learning - Feature Extraction vs Fine-Tuning.mp497.13MB
299. 66. Transfer Learning/4. Transfer Learning Implementation using ResNet-50 Base.mp4115.99MB
300. 68. Markov Chains for Text Generation (NLP)/1. Markov Chains - I Introduction.mp443.1MB
301. 68. Markov Chains for Text Generation (NLP)/2. Markov Chains - 2 Setting up Supervised Learning Problem.mp455.46MB
302. 68. Markov Chains for Text Generation (NLP)/3. Markov Chains - 3 Training a 'Speech Generator'.mp465.15MB
303. 68. Markov Chains for Text Generation (NLP)/4. Markov Chain 4 - Sampling.mp445.82MB
304. 68. Markov Chains for Text Generation (NLP)/5. Markov Chains 5 - Text Generation.mp450.8MB
305. 7. Python 05 - File and Error Handling/1. Python - File Handling.mp493.81MB
306. 7. Python 05 - File and Error Handling/2. Python - Working with JSON.mp460.44MB
307. 7. Python 05 - File and Error Handling/3. Python - Error Handling 1.mp438.45MB
308. 7. Python 05 - File and Error Handling/4. Python - Error Handling 2.mp430.58MB
309. 7. Python 05 - File and Error Handling/5. Python - Error Handling 3.mp435.78MB
310. 7. Python 05 - File and Error Handling/6. Python - Error Handling 4.mp470.98MB
311. 70. Recurrent Neural Networks/1. Sequence Models Introduction.mp444.1MB
312. 70. Recurrent Neural Networks/2. Understanding RNN Cell.mp445.64MB
313. 70. Recurrent Neural Networks/3. RNN - Different Architectures.mp429.76MB
314. 70. Recurrent Neural Networks/4. RNN - Forward Propagation.mp415.19MB
315. 70. Recurrent Neural Networks/5. RNN - Backpropagation through time.mp480.72MB
316. 70. Recurrent Neural Networks/6. Embedding Layers Word Embeddings.mp450.14MB
317. 70. Recurrent Neural Networks/7. Tutorial - Building a Recurrent Neural Network in Keras.mp4134.99MB
318. 70. Recurrent Neural Networks/8. Keras Callbacks - EarlyStopping, Creating Checkpoints.mp4119.33MB
319. 71. Word Embeddings - Word2Vec/1. Word2Vec - Finding Odd One Out.mp4197.56MB
320. 71. Word Embeddings - Word2Vec/2. Word2Vec - Finding Word Analogies.mp474.55MB
321. 71. Word Embeddings - Word2Vec/3. Word2Vec in NLP Ranveer - Deepika + Priyanka = Nick LIVE Deep Learning Premier [Hindi].mp471.11MB
322. 73. Project - Emoji Prediction/1.Transfer Learning in NLP.mp437.83MB
323. 73. Project - Emoji Prediction/2.Emoji Predictor - Project Overview.mp416.07MB
324. 73. Project - Emoji Prediction/3.Working with the 'emoji' package.mp421.67MB
325. 73. Project - Emoji Prediction/4.Processing Custom 'emoji' dataset.mp426.08MB
326. 73. Project - Emoji Prediction/5.Glove Vectors.mp425.65MB
327. 73. Project - Emoji Prediction/6.Glove Embeddings.mp426.66MB
328. 73. Project - Emoji Prediction/7.Creating a LSTM Architecture.mp4113.27MB
329. 73. Project - Emoji Prediction/8.Training a Stacked LSTM.mp434.81MB
330. 73. Project - Emoji Prediction/9.Outputting Emoji's for Test Data.mp423.81MB
331. 75. Reinforcement Learning/1.RL1 Reinforcement Learning Introduction.mp446.15MB
332. 75. Reinforcement Learning/2.RL2 Understanding OpenAI Gym Interface.mp471.15MB
333. 75. Reinforcement Learning/3.RL3 Playing Cartpole Using Random Technology.mp440.73MB
334. 75. Reinforcement Learning/4. RL4 Q Learning.mp453.61MB
335. 75. Reinforcement Learning/5. RL5 Agent Design Exploration vs Exploitation Tradeoff.mp439.71MB
336. 75. Reinforcement Learning/6. RL6 Understanding DQN Model Architecture.mp434.87MB
337. 75. Reinforcement Learning/7. RL7 Training Using Replay Buffer.mp489.01MB
338. 75. Reinforcement Learning/8. RL8 Training 'Deep Q Learner' for Game Playing.mp477.19MB
339. 76. 🚀 Mountain Car Challenge/2.RL Assignment - Create an RL DQN Agent for Mountain Car.mp44.95MB
340. 77. Generative Adversarial Networks/1. GAN 01 Generative Models.mp4109.96MB
341. 77. Generative Adversarial Networks/2. GAN 02 Generative Models-II.mp449.9MB
342. 77. Generative Adversarial Networks/3. GAN 03 Intuition.mp414MB
343. 77. Generative Adversarial Networks/4. GAN 04 Architecture.mp420.1MB
344. 77. Generative Adversarial Networks/5. GAN 05 Training.mp427.84MB
345. 77. Generative Adversarial Networks/6. GAN 06 Training Tricks.mp411.15MB
346. 77. Generative Adversarial Networks/7. GAN 07 Coding a GAN-I.mp436.03MB
347. 77. Generative Adversarial Networks/8. GAN 08 Coding a GAN-II.mp4253.34MB
348. 78. Deep Convolutional GAN's (DCGANs)/1. DCGAN01 - Deep Convutional GAN's.mp427.98MB
349. 78. Deep Convolutional GAN's (DCGANs)/2. DCGAN02 - Upsampling & Downsampling Architecture.mp415.16MB
350. 78. Deep Convolutional GAN's (DCGANs)/3. DCGAN03 - Building Generator [Code].mp419.25MB
351. 78. Deep Convolutional GAN's (DCGANs)/4. DCGAN04 - Building Discriminator [Code] .mp415.09MB
352. 78. Deep Convolutional GAN's (DCGANs)/5. DCGAN05 - Training & Results.mp46.16MB
353. 78. Deep Convolutional GAN's (DCGANs)/6. DCGAN06 - Learnable Upsampling.mp414.22MB
354. 78. Deep Convolutional GAN's (DCGANs)/7. DCGAN07 - Transpose Convolutions.mp449.51MB
355. 78. Deep Convolutional GAN's (DCGANs)/8. DCGAN08 - Implementing GAN Generator with Learnable Upsampling.mp449.65MB
356. 8. Iteration Protocol and Generators (Intermediate Python)/1. Python - Iteration Protocol.mp427.68MB
357. 8. Iteration Protocol and Generators (Intermediate Python)/2. Iterators in Python.mp420.78MB
358. 8. Iteration Protocol and Generators (Intermediate Python)/3. Generators in Python.mp423.84MB
359. 80. 🚀 All Challenges/5. Challenge - Scrape PepperFry Challenge/1. Challenge - Scrape PepperFry Challenge.mp48.95MB
360. 81. 🏆 CAPSTONE PROJECT - AI Image Caption Bot 🤖/IC 01 - Image Captioning Project Introduction.mp424.59MB
361. 81. 🏆 CAPSTONE PROJECT - AI Image Caption Bot 🤖/IC 02 - Data Collection.mp435.48MB
362. 81. 🏆 CAPSTONE PROJECT - AI Image Caption Bot 🤖/IC 03 - Text Cleaning.mp431.19MB
363. 81. 🏆 CAPSTONE PROJECT - AI Image Caption Bot 🤖/IC 04 - Creating Vocab.mp455.56MB
364. 81. 🏆 CAPSTONE PROJECT - AI Image Caption Bot 🤖/IC 05 - Prepare Train Test Data.mp434.06MB
365. 81. 🏆 CAPSTONE PROJECT - AI Image Caption Bot 🤖/IC 06 - Image Preprocessing.mp463.09MB
366. 81. 🏆 CAPSTONE PROJECT - AI Image Caption Bot 🤖/IC 07 - Images to Features (Transfer Learning).mp473.87MB
367. 81. 🏆 CAPSTONE PROJECT - AI Image Caption Bot 🤖/IC 08 - Preprocessing Captions.mp434.88MB
368. 81. 🏆 CAPSTONE PROJECT - AI Image Caption Bot 🤖/IC 09 - Image Captioning as Supervised Learning Problem.mp462.11MB
369. 81. 🏆 CAPSTONE PROJECT - AI Image Caption Bot 🤖/IC 10 - Making Custom Data Loader.mp431.14MB
370. 81. 🏆 CAPSTONE PROJECT - AI Image Caption Bot 🤖/IC 11 - Using Glove Embeddings [Transfer Learning].mp478.56MB
371. 81. 🏆 CAPSTONE PROJECT - AI Image Caption Bot 🤖/IC 12 - Caption Bot AI Model.mp425.17MB
372. 81. 🏆 CAPSTONE PROJECT - AI Image Caption Bot 🤖/IC 13 - Model Implementation (Keras Functional API).mp437.94MB
373. 81. 🏆 CAPSTONE PROJECT - AI Image Caption Bot 🤖/IC 14 - Model Training.mp414.75MB
374. 81. 🏆 CAPSTONE PROJECT - AI Image Caption Bot 🤖/IC 15 - Prediction-Inference.mp4125.77MB
375. 82. 🏆 ML + Web Project Creating ML Based Web Service (Django)/Emojifier Web Integration - 1.mp4167.89MB
376. 82. 🏆 ML + Web Project Creating ML Based Web Service (Django)/Emojifier Web Integration - 2.mp4193.67MB
377. 83. Integrating ML Models with Web (Flask)/1. Flask Basics.mp427.51MB
378. 83. Integrating ML Models with Web (Flask)/2. Flask Templates.mp424.69MB
379. 83. Integrating ML Models with Web (Flask)/3. Taking Inputs from Users in Flask.mp435MB
380. 83. Integrating ML Models with Web (Flask)/4. ML model with Flask.mp435.03MB
381. 83. Integrating ML Models with Web (Flask)/5. Image Captioning on Flask - I.mp457.4MB
382. 83. Integrating ML Models with Web (Flask)/6. Image Captioning on Flask - II.mp448.15MB
383. 83. Integrating ML Models with Web (Flask)/7. Image Captioning on Flask - III.mp430.81MB
384. 83. Integrating ML Models with Web (Flask)/8. Heroku Deployment.mp446.86MB
385. 84. 🏆 CAPSTONE PROJECT - AI Music Generation/1. Music Generation 01 - Introduction.mp426.91MB
386. 84. 🏆 CAPSTONE PROJECT - AI Music Generation/2. Music Generation 02 - Parsing MIDI Files.mp428.69MB
387. 84. 🏆 CAPSTONE PROJECT - AI Music Generation/3. Music Generation 03 - Prepare Sequential Data for LSTM.mp427.42MB
388. 84. 🏆 CAPSTONE PROJECT - AI Music Generation/4. Music Generation 04 - Model Architecture.mp419.6MB
389. 84. 🏆 CAPSTONE PROJECT - AI Music Generation/5. Music Generation 05 - Predictions.mp420.39MB
390. 84. 🏆 CAPSTONE PROJECT - AI Music Generation/6. Music Generation 06 - Creating Music Files.mp422.31MB
391. 85. Tensorflow Introduction/1. Tensorflow - Introduction.mp426.55MB
392. 85. Tensorflow Introduction/2. Tensorflow - Basics.mp424.97MB
393. 85. Tensorflow Introduction/3. Tensorflow - Understanding Linear Regression Computation Graph.mp451.83MB
394. 86. Introduction to PyTorch/1. Pytorch - Introduction.mp417.68MB
395. 86. Introduction to PyTorch/2. Pytorch - Linear Regression.mp431.25MB
396. 87. Project Ideas/Machine Learning Project Ideas.mp445.45MB
397. 9. Python Practice Problems/3. Python - Backtracking - Generate Paranthesis.mkv23.88MB