Packt Publishing - Deep Dive into Python Machine Learning

1. 01 - The Course Overview.mp414.93MB
2. 02 - Python Basic Syntax and Block Structure.mp422.54MB
3. 03 - Built-in Data Structures and Comprehensions.mp417.79MB
4. 04 - First-Class Functions and Classes.mp412.33MB
5. 05 - Extensive Standard Library.mp431.14MB
6. 06 - New in Python 3.5.mp421.01MB
7. 07 - Downloading and Installing Python.mp415.34MB
8. 08 - Using the Command-Line and the Interactive Shell.mp47.1MB
9. 09 - Installing Packages with pip.mp411.04MB
10. 10 - Finding Packages in the Python Package Index.mp421.78MB
11. 100 - Compressing an Image Using Vector Quantization.mp416.33MB
12. 101 - Building a Mean Shift Clustering.mp411.26MB
13. 102 - Grouping Data Using Agglomerative Clustering.mp413.54MB
14. 103 - Evaluating the Performance of Clustering Algorithms.mp412.74MB
15. 104 - Automatically Estimating the Number of Clusters Using DBSCAN.mp414.94MB
16. 105 - Finding Patterns in Stock Market Data.mp411.34MB
17. 106 - Building a Customer Segmentation Model.mp49.78MB
18. 107 - Building Function Composition for Data Processing.mp413.67MB
19. 108 - Building Machine Learning Pipelines.mp415.17MB
20. 109 - Finding the Nearest Neighbors.mp48.05MB
21. 11 - Creating an Empty Package.mp411.59MB
22. 110 - Constructing a k-nearest Neighbors Classifier.mp419.77MB
23. 111 - Constructing a k-nearest Neighbors Regressor.mp49.75MB
24. 112 - Computing the Euclidean Distance Score.mp49.21MB
25. 113 - Computing the Pearson Correlation Score.mp48.32MB
26. 114 - Finding Similar Users in a Dataset.mp46.89MB
27. 115 - Generating Movie Recommendations.mp410.2MB
28. 116 - Preprocessing Data Using Tokenization.mp412.67MB
29. 117 - Stemming Text Data.mp48.77MB
30. 118 - Converting Text to Its Base Form Using Lemmatization.mp48.25MB
31. 119 - Dividing Text Using Chunking.mp47.42MB
32. 12 - Adding Modules to the Package.mp47.99MB
33. 120 - Building a Bag-of-Words Model.mp411.71MB
34. 121 - Building a Text Classifier.mp417.97MB
35. 122 - Identifying the Gender.mp410MB
36. 123 - Analyzing the Sentiment of a Sentence.mp414.39MB
37. 124 - Identifying Patterns in Text Using Topic Modelling.mp419.76MB
38. 125 - Reading and Plotting Audio Data.mp49.35MB
39. 126 - Transforming Audio Signals into the Frequency Domain.mp49.32MB
40. 127 - Generating Audio Signals with Custom Parameters.mp47.64MB
41. 128 - Synthesizing Music.mp49.81MB
42. 129 - Extracting Frequency Domain Features.mp48.13MB
43. 13 - Importing One of the Package's Modules from Another.mp49.29MB
44. 130 - Building Hidden Markov Models.mp49.6MB
45. 131 - Building a Speech Recognizer.mp412.94MB
46. 132 - Transforming Data into the Time Series Format.mp413.23MB
47. 133 - Slicing Time Series Data.mp45.32MB
48. 134 - Operating on Time Series Data.mp46.79MB
49. 135 - Extracting Statistics from Time Series.mp410.76MB
50. 136 - Building Hidden Markov Models for Sequential Data.mp417.7MB
51. 137 - Building Conditional Random Fields for Sequential Text Data.mp419.05MB
52. 138 - Analyzing Stock Market Data with Hidden Markov Models.mp411.84MB
53. 139 - Operating on Images Using OpenCV-Python.mp416.06MB
54. 14 - Adding Static Data Files to the Package.mp44.54MB
55. 140 - Detecting Edges.mp413.63MB
56. 141 - Histogram Equalization.mp411.46MB
57. 142 - Detecting Corners and SIFT Feature Points.mp416.86MB
58. 143 - Building a Star Feature Detector.mp47.35MB
59. 144 - Creating Features Using Visual Codebook and Vector Quantization.mp419.96MB
60. 145 - Training an Image Classifier Using Extremely Random Forests.mp411.41MB
61. 146 - Building an object recognizer.mp47.72MB
62. 147 - Capturing and Processing Video from a Webcam.mp46.95MB
63. 148 - Building a Face Detector using Haar Cascades.mp411.01MB
64. 149 - Building Eye and Nose Detectors.mp48.23MB
65. 15 - PEP 8 and Writing Readable Code.mp423.79MB
66. 150 - Performing Principal Component Analysis.mp47.98MB
67. 151 - Performing Kernel Principal Component Analysis.mp48.42MB
68. 152 - Performing Blind Source Separation.mp410.05MB
69. 153 - Building a Face Recognizer Using a Local Binary Patterns Histogram.mp420.53MB
70. 154 - Building a Perceptron.mp49.19MB
71. 155 - Building a Single-Layer Neural Network.mp45.93MB
72. 156 - Building a deep neural network.mp49.15MB
73. 157 - Creating a Vector Quantizer.mp48.36MB
74. 158 - Building a Recurrent Neural Network for Sequential Data Analysis.mp410.18MB
75. 159 - Visualizing the Characters in an Optical Character Recognition Database.mp45.17MB
76. 16 - Using Version Control.mp416.75MB
77. 160 - Building an Optical Character Recognizer Using Neural Networks.mp410.37MB
78. 161 - Plotting 3D Scatter plots.mp48.03MB
79. 162 - Plotting Bubble Plots.mp43.66MB
80. 163 - Animating Bubble Plots.mp49.43MB
81. 164 - Drawing Pie Charts.mp45.57MB
82. 165 - Plotting Date-Formatted Time Series Data.mp45.96MB
83. 166 - Plotting Histograms.mp43.67MB
84. 167 - Visualizing Heat Maps.mp44MB
85. 168 - Animating Dynamic Signals.mp46.79MB
86. 169 - The Course Overview.mp417.84MB
87. 17 - Using venv to Create a Stable and Isolated Work Area.mp48.15MB
88. 170 - What Is Deep Learning.mp47.37MB
89. 171 - Open Source Libraries for Deep Learning.mp421.33MB
90. 172 - Deep Learning Hello World! Classifying the MNIST Data.mp434.69MB
91. 173 - Introduction to Backpropagation.mp49.32MB
92. 174 - Understanding Deep Learning with Theano.mp419.26MB
93. 175 - Optimizing a Simple Model in Pure Theano.mp433.58MB
94. 176 - Keras Behind the Scenes.mp424.43MB
95. 177 - Fully Connected or Dense Layers.mp421.89MB
96. 178 - Convolutional and Pooling Layers.mp425.35MB
97. 179 - Large Scale Datasets, ImageNet, and Very Deep Neural Networks.mp420.32MB
98. 18 - Getting the Most Out of docstrings 1 - PEP 257 and docutils.mp438.58MB
99. 180 - Loading Pre-trained Models with Theano.mp423.52MB
100. 181 - Reusing Pre-trained Models in New Applications.mp431.83MB
101. 182 - Theano for Loops – the scan Module.mp419.47MB
102. 183 - Recurrent Layers.mp424.84MB
103. 184 - Recurrent Versus Convolutional Layers.mp46.58MB
104. 185 - Recurrent Networks –Training a Sentiment Analysis Model for Text.mp429.72MB
105. 186 - Bonus Challenge – Automatic Image Captioning.mp421.25MB
106. 187 - Captioning TensorFlow – Google's Machine Learning Library.mp421.61MB
107. 19 - Getting the Most Out of docstrings 2 - doctest.mp47.42MB
108. 20 - Making a Package Executable via python -m.mp49.19MB
109. 21 - Handling Command-Line Arguments with argparse.mp412.23MB
110. 22 - Interacting with the User.mp48.64MB
111. 23 - Executing Other Programs with Subprocess.mp445.53MB
112. 24 - Using Shell Scripts or Batch Files to Run Our Programs.mp44.62MB
113. 25 - Using concurrent.futures.mp446.73MB
114. 26 - Using Multiprocessing.mp421.9MB
115. 27 - Understanding Why This Isn't Like Parallel Processing.mp417.4MB
116. 28 - Using the asyncio Event Loop and Coroutine Scheduler.mp413.35MB
117. 29 - Waiting for Data to Become Available.mp46.66MB
118. 30 - Synchronizing Multiple Tasks.mp413.32MB
119. 31 - Communicating Across the Network.mp411.34MB
120. 32 - Using Function Decorators.mp412.98MB
121. 33 - Function Annotations.mp413.61MB
122. 34 - Class Decorators.mp411.44MB
123. 35 - Metaclasses.mp49.83MB
124. 36 - Context Managers.mp411.35MB
125. 37 - Descriptors.mp419.63MB
126. 38 - Understanding the Principles of Unit Testing.mp48.5MB
127. 39 - Using the unittest Package.mp417.13MB
128. 40 - Using unittest.mock.mp410.55MB
129. 41 - Using unittest's Test Discovery.mp49.72MB
130. 42 - Using Nose for Unified Test Discover and Reporting.mp411MB
131. 43 - What Does Reactive Programming Mean.mp44.82MB
132. 44 - Building a Simple Reactive Programming Framework.mp414.64MB
133. 45 - Using the Reactive Extensions for Python (RxPY).mp433.64MB
134. 46 - Microservices and the Advantages of Process Isolation.mp48.2MB
135. 47 - Building a High-Level Microservice with Flask.mp424.79MB
136. 48 - Building a Low-Level Microservice with nameko.mp412.78MB
137. 49 - Advantages and Disadvantages of Compiled Code.mp410.42MB
138. 50 - Accessing a Dynamic Library Using ctypes.mp414.92MB
139. 51 - Interfacing with C Code Using Cython.mp427.33MB
140. 52 - The Course Overview.mp49.69MB
141. 53 - Brief Introduction to Data Mining.mp48.59MB
142. 54 - Data Mining Basic Concepts and Applications.mp414.24MB
143. 55 - Why Python.mp45.22MB
144. 56 - Basics of Python.mp49.58MB
145. 57 - Installing IPython.mp43.88MB
146. 58 - Installing the Numpy Library.mp48.8MB
147. 59 - Installing the pandas Library.mp414.97MB
148. 60 - Installing Matplotlib.mp411.96MB
149. 61 - Installing scikit-learn.mp43.75MB
150. 62 - Data Cleaning.mp49.19MB
151. 63 - Data Preprocessing Techniques.mp48.41MB
152. 64 - Linear Regression Basic Model Approach.mp414.03MB
153. 65 - Evaluating Regression Models.mp49.14MB
154. 66 - Basic Regression Model Implementation to Predict House Prices.mp435.83MB
155. 67 - Regression Model Implementation to Predict Television Show Viewers.mp440.35MB
156. 68 - Logistic Regression.mp46.92MB
157. 69 - K – Nearest Neighbors Classifier.mp48.89MB
158. 70 - Support Vector Machine.mp49.4MB
159. 71 - Logistic Regression Model Implementation.mp447.17MB
160. 72 - K – Nearest Neighbor Classifier Implementation.mp438.31MB
161. 73 - Preprocessing Data Using Different Techniques.mp426.46MB
162. 74 - Label Encoding.mp410.54MB
163. 75 - Building a Linear Regressor.mp419.66MB
164. 76 - Regression Accuracy and Model Persistence.mp417.5MB
165. 77 - Building a Ridge Regressor.mp412.3MB
166. 78 - Building a Polynomial Regressor.mp411.43MB
167. 79 - Estimating housing prices.mp416.9MB
168. 80 - Computing relative importance of features.mp47.58MB
169. 81 - Estimating bicycle demand distribution.mp417.97MB
170. 82 - Building a Simple Classifier.mp412.21MB
171. 83 - Building a Logistic Regression Classifier.mp420.2MB
172. 84 - Building a Naive Bayes’ Classifier.mp48.74MB
173. 85 - Splitting the Dataset for Training and Testing.mp46.14MB
174. 86 - Evaluating the Accuracy Using Cross-Validation.mp48.21MB
175. 87 - Visualizing the Confusion Matrix and Extracting the Performance Report.mp415.79MB
176. 88 - Evaluating Cars based on Their Characteristics.mp423.16MB
177. 89 - Extracting Validation Curves.mp414.08MB
178. 90 - Extracting Learning Curves.mp47.31MB
179. 91 - Extracting the Income Bracket.mp415.04MB
180. 92 - Building a Linear Classifier Using Support Vector Machine.mp420.2MB
181. 93 - Building Nonlinear Classifier Using SVMs.mp48MB
182. 94 - Tackling Class Imbalance.mp413.3MB
183. 95 - Extracting Confidence Measurements.mp412.01MB
184. 96 - Finding Optimal Hyper-Parameters.mp410.42MB
185. 97 - Building an Event Predictor.mp416.95MB
186. 98 - Estimating Traffic.mp410.82MB
187. 99 - Clustering Data Using the k-means Algorithm.mp413.45MB