Packt Publishing Deep Dive into Python Machine Learning | 8D48EBB15A1AF945BF781071ADB6BE1AA5602953

Packt Publishing Deep Dive into Python Machine Learning

Name	Packt Publishing - Deep Dive into Python Machine Learning	DOWNLOAD Copy Link
Total Size	2.8 GB
Total Files	191
Hash	8D48EBB15A1AF945BF781071ADB6BE1AA5602953

/Project_Files/
Data Mining with Python- Implementing Classification and Regression.zip	17.2 KB
Deep Learning with Python [Video].zip	605.0 KB
Mastering Python - Second Edition [Video].zip	36.7 KB
Python Machine Learning Solutions [Video].zip	60.6 MB
/
01 - The Course Overview.mp4	15.7 MB
02 - Python Basic Syntax and Block Structure.mp4	23.6 MB
03 - Built-in Data Structures and Comprehensions.mp4	18.7 MB
04 - First-Class Functions and Classes.mp4	12.9 MB
05 - Extensive Standard Library.mp4	32.7 MB
06 - New in Python 3.5.mp4	22.0 MB
07 - Downloading and Installing Python.mp4	16.1 MB
08 - Using the Command-Line and the Interactive Shell.mp4	7.4 MB
09 - Installing Packages with pip.mp4	11.6 MB
10 - Finding Packages in the Python Package Index.mp4	22.8 MB
100 - Compressing an Image Using Vector Quantization.mp4	17.1 MB
101 - Building a Mean Shift Clustering.mp4	11.8 MB
102 - Grouping Data Using Agglomerative Clustering.mp4	14.2 MB
103 - Evaluating the Performance of Clustering Algorithms.mp4	13.4 MB
104 - Automatically Estimating the Number of Clusters Using DBSCAN.mp4	15.7 MB
105 - Finding Patterns in Stock Market Data.mp4	11.9 MB
106 - Building a Customer Segmentation Model.mp4	10.3 MB
107 - Building Function Composition for Data Processing.mp4	14.3 MB
108 - Building Machine Learning Pipelines.mp4	15.9 MB
109 - Finding the Nearest Neighbors.mp4	8.4 MB
11 - Creating an Empty Package.mp4	12.2 MB
110 - Constructing a k-nearest Neighbors Classifier.mp4	20.7 MB
111 - Constructing a k-nearest Neighbors Regressor.mp4	10.2 MB
112 - Computing the Euclidean Distance Score.mp4	9.7 MB
113 - Computing the Pearson Correlation Score.mp4	8.7 MB
114 - Finding Similar Users in a Dataset.mp4	7.2 MB
115 - Generating Movie Recommendations.mp4	10.7 MB
116 - Preprocessing Data Using Tokenization.mp4	13.3 MB
117 - Stemming Text Data.mp4	9.2 MB
118 - Converting Text to Its Base Form Using Lemmatization.mp4	8.6 MB
119 - Dividing Text Using Chunking.mp4	7.8 MB
12 - Adding Modules to the Package.mp4	8.4 MB
120 - Building a Bag-of-Words Model.mp4	12.3 MB
121 - Building a Text Classifier.mp4	18.8 MB
122 - Identifying the Gender.mp4	10.5 MB
123 - Analyzing the Sentiment of a Sentence.mp4	15.1 MB
124 - Identifying Patterns in Text Using Topic Modelling.mp4	20.7 MB
125 - Reading and Plotting Audio Data.mp4	9.8 MB
126 - Transforming Audio Signals into the Frequency Domain.mp4	9.8 MB
127 - Generating Audio Signals with Custom Parameters.mp4	8.0 MB
128 - Synthesizing Music.mp4	10.3 MB
129 - Extracting Frequency Domain Features.mp4	8.5 MB
13 - Importing One of the Package's Modules from Another.mp4	9.7 MB
130 - Building Hidden Markov Models.mp4	10.1 MB
131 - Building a Speech Recognizer.mp4	13.6 MB
132 - Transforming Data into the Time Series Format.mp4	13.9 MB
133 - Slicing Time Series Data.mp4	5.6 MB
134 - Operating on Time Series Data.mp4	7.1 MB
135 - Extracting Statistics from Time Series.mp4	11.3 MB
136 - Building Hidden Markov Models for Sequential Data.mp4	18.6 MB
137 - Building Conditional Random Fields for Sequential Text Data.mp4	20.0 MB
138 - Analyzing Stock Market Data with Hidden Markov Models.mp4	12.4 MB
139 - Operating on Images Using OpenCV-Python.mp4	16.8 MB
14 - Adding Static Data Files to the Package.mp4	4.8 MB
140 - Detecting Edges.mp4	14.3 MB
141 - Histogram Equalization.mp4	12.0 MB
142 - Detecting Corners and SIFT Feature Points.mp4	17.7 MB
143 - Building a Star Feature Detector.mp4	7.7 MB
144 - Creating Features Using Visual Codebook and Vector Quantization.mp4	20.9 MB
145 - Training an Image Classifier Using Extremely Random Forests.mp4	12.0 MB
146 - Building an object recognizer.mp4	8.1 MB
147 - Capturing and Processing Video from a Webcam.mp4	7.3 MB
148 - Building a Face Detector using Haar Cascades.mp4	11.5 MB
149 - Building Eye and Nose Detectors.mp4	8.6 MB
15 - PEP 8 and Writing Readable Code.mp4	24.9 MB
150 - Performing Principal Component Analysis.mp4	8.4 MB
151 - Performing Kernel Principal Component Analysis.mp4	8.8 MB
152 - Performing Blind Source Separation.mp4	10.5 MB
153 - Building a Face Recognizer Using a Local Binary Patterns Histogram.mp4	21.5 MB
154 - Building a Perceptron.mp4	9.6 MB
155 - Building a Single-Layer Neural Network.mp4	6.2 MB
156 - Building a deep neural network.mp4	9.6 MB
157 - Creating a Vector Quantizer.mp4	8.8 MB
158 - Building a Recurrent Neural Network for Sequential Data Analysis.mp4	10.7 MB
159 - Visualizing the Characters in an Optical Character Recognition Database.mp4	5.4 MB
16 - Using Version Control.mp4	17.6 MB
160 - Building an Optical Character Recognizer Using Neural Networks.mp4	10.9 MB
161 - Plotting 3D Scatter plots.mp4	8.4 MB
162 - Plotting Bubble Plots.mp4	3.8 MB
163 - Animating Bubble Plots.mp4	9.9 MB
164 - Drawing Pie Charts.mp4	5.8 MB
165 - Plotting Date-Formatted Time Series Data.mp4	6.3 MB
166 - Plotting Histograms.mp4	3.8 MB
167 - Visualizing Heat Maps.mp4	4.2 MB
168 - Animating Dynamic Signals.mp4	7.1 MB
169 - The Course Overview.mp4	18.7 MB
17 - Using venv to Create a Stable and Isolated Work Area.mp4	8.5 MB
170 - What Is Deep Learning.mp4	7.7 MB
171 - Open Source Libraries for Deep Learning.mp4	22.4 MB
172 - Deep Learning Hello World! Classifying the MNIST Data.mp4	36.4 MB
173 - Introduction to Backpropagation.mp4	9.8 MB
174 - Understanding Deep Learning with Theano.mp4	20.2 MB
175 - Optimizing a Simple Model in Pure Theano.mp4	35.2 MB
176 - Keras Behind the Scenes.mp4	25.6 MB
177 - Fully Connected or Dense Layers.mp4	22.9 MB
178 - Convolutional and Pooling Layers.mp4	26.6 MB
179 - Large Scale Datasets, ImageNet, and Very Deep Neural Networks.mp4	21.3 MB
18 - Getting the Most Out of docstrings 1 - PEP 257 and docutils.mp4	40.5 MB
180 - Loading Pre-trained Models with Theano.mp4	24.7 MB
181 - Reusing Pre-trained Models in New Applications.mp4	33.4 MB
182 - Theano for Loops – the scan Module.mp4	20.4 MB
183 - Recurrent Layers.mp4	26.0 MB
184 - Recurrent Versus Convolutional Layers.mp4	6.9 MB
185 - Recurrent Networks –Training a Sentiment Analysis Model for Text.mp4	31.2 MB
186 - Bonus Challenge – Automatic Image Captioning.mp4	22.3 MB
187 - Captioning TensorFlow – Google's Machine Learning Library.mp4	22.7 MB
19 - Getting the Most Out of docstrings 2 - doctest.mp4	7.8 MB
20 - Making a Package Executable via python -m.mp4	9.6 MB
21 - Handling Command-Line Arguments with argparse.mp4	12.8 MB
22 - Interacting with the User.mp4	9.1 MB
23 - Executing Other Programs with Subprocess.mp4	47.7 MB
24 - Using Shell Scripts or Batch Files to Run Our Programs.mp4	4.8 MB
25 - Using concurrent.futures.mp4	49.0 MB
26 - Using Multiprocessing.mp4	23.0 MB
27 - Understanding Why This Isn't Like Parallel Processing.mp4	18.2 MB
28 - Using the asyncio Event Loop and Coroutine Scheduler.mp4	14.0 MB
29 - Waiting for Data to Become Available.mp4	7.0 MB
30 - Synchronizing Multiple Tasks.mp4	14.0 MB
31 - Communicating Across the Network.mp4	11.9 MB
32 - Using Function Decorators.mp4	13.6 MB
33 - Function Annotations.mp4	14.3 MB
34 - Class Decorators.mp4	12.0 MB
35 - Metaclasses.mp4	10.3 MB
36 - Context Managers.mp4	11.9 MB
37 - Descriptors.mp4	20.6 MB
38 - Understanding the Principles of Unit Testing.mp4	8.9 MB
39 - Using the unittest Package.mp4	18.0 MB
40 - Using unittest.mock.mp4	11.1 MB
41 - Using unittest's Test Discovery.mp4	10.2 MB
42 - Using Nose for Unified Test Discover and Reporting.mp4	11.5 MB
43 - What Does Reactive Programming Mean.mp4	5.1 MB
44 - Building a Simple Reactive Programming Framework.mp4	15.4 MB
45 - Using the Reactive Extensions for Python (RxPY).mp4	35.3 MB
46 - Microservices and the Advantages of Process Isolation.mp4	8.6 MB
47 - Building a High-Level Microservice with Flask.mp4	26.0 MB
48 - Building a Low-Level Microservice with nameko.mp4	13.4 MB
49 - Advantages and Disadvantages of Compiled Code.mp4	10.9 MB
50 - Accessing a Dynamic Library Using ctypes.mp4	15.6 MB
51 - Interfacing with C Code Using Cython.mp4	28.7 MB
52 - The Course Overview.mp4	10.2 MB
53 - Brief Introduction to Data Mining.mp4	9.0 MB
54 - Data Mining Basic Concepts and Applications.mp4	14.9 MB
55 - Why Python.mp4	5.5 MB
56 - Basics of Python.mp4	10.0 MB
57 - Installing IPython.mp4	4.1 MB
58 - Installing the Numpy Library.mp4	9.2 MB
59 - Installing the pandas Library.mp4	15.7 MB
60 - Installing Matplotlib.mp4	12.5 MB
61 - Installing scikit-learn.mp4	3.9 MB
62 - Data Cleaning.mp4	9.6 MB
63 - Data Preprocessing Techniques.mp4	8.8 MB
64 - Linear Regression Basic Model Approach.mp4	14.7 MB
65 - Evaluating Regression Models.mp4	9.6 MB
66 - Basic Regression Model Implementation to Predict House Prices.mp4	37.6 MB
67 - Regression Model Implementation to Predict Television Show Viewers.mp4	42.3 MB
68 - Logistic Regression.mp4	7.3 MB
69 - K – Nearest Neighbors Classifier.mp4	9.3 MB
70 - Support Vector Machine.mp4	9.9 MB
71 - Logistic Regression Model Implementation.mp4	49.5 MB
72 - K – Nearest Neighbor Classifier Implementation.mp4	40.2 MB
73 - Preprocessing Data Using Different Techniques.mp4	27.7 MB
74 - Label Encoding.mp4	11.1 MB
75 - Building a Linear Regressor.mp4	20.6 MB
76 - Regression Accuracy and Model Persistence.mp4	18.4 MB
77 - Building a Ridge Regressor.mp4	12.9 MB
78 - Building a Polynomial Regressor.mp4	12.0 MB
79 - Estimating housing prices.mp4	17.7 MB
80 - Computing relative importance of features.mp4	7.9 MB
81 - Estimating bicycle demand distribution.mp4	18.8 MB
82 - Building a Simple Classifier.mp4	12.8 MB
83 - Building a Logistic Regression Classifier.mp4	21.2 MB
84 - Building a Naive Bayes’ Classifier.mp4	9.2 MB
85 - Splitting the Dataset for Training and Testing.mp4	6.4 MB
86 - Evaluating the Accuracy Using Cross-Validation.mp4	8.6 MB
87 - Visualizing the Confusion Matrix and Extracting the Performance Report.mp4	16.6 MB
88 - Evaluating Cars based on Their Characteristics.mp4	24.3 MB
89 - Extracting Validation Curves.mp4	14.8 MB
90 - Extracting Learning Curves.mp4	7.7 MB
91 - Extracting the Income Bracket.mp4	15.8 MB
92 - Building a Linear Classifier Using Support Vector Machine.mp4	21.2 MB
93 - Building Nonlinear Classifier Using SVMs.mp4	8.4 MB
94 - Tackling Class Imbalance.mp4	13.9 MB
95 - Extracting Confidence Measurements.mp4	12.6 MB
96 - Finding Optimal Hyper-Parameters.mp4	10.9 MB
97 - Building an Event Predictor.mp4	17.8 MB
98 - Estimating Traffic.mp4	11.3 MB
99 - Clustering Data Using the k-means Algorithm.mp4	14.1 MB
Total files 191