Video Description

Confidently take your data mining and machine learning skills to your work

In Detail
The world is emitting data at an enormous rate. There is a need for professionals who can confidently work with data and output meaningful insight. Data Science is a rewarding career field that allows you to solve some of the world’s most interesting problems. This Learning Path will give you hands-on experience with popular Python data mining and machine learning algorithms. First, we’ll expand your knowledge base by covering basic to advanced concepts of Python. Then, we’ll give you hands-on experience with the popular Python data mining algorithms. Going forward, we’ll learn how to perform various machine learning tasks in the real world. Finally, we’ll dive into the future of data science and implement intelligent systems using deep learning with Python.

By the end of the Learning path, you can start working with machine learning right away.

Prerequisites: Basic knowledge on Python. Aimed at Python programmers and data scientists who are willing to learn data mining and machine learning algorithms.

PROJECT_FILES
Python Machine Learning Solutions [Video].zip57.83 MB
Deep Learning with Python [Video].zip590.78 KB
Mastering Python - Second Edition [Video].zip35.83 KB
Data Mining with Python- Implementing Classification and Regression.zip16.77 KB
71 - Logistic Regression Model Implementation.mp447.17 MB
25 - Using concurrent.futures.mp446.73 MB
23 - Executing Other Programs with Subprocess.mp445.53 MB
67 - Regression Model Implementation to Predict Television Show Viewers.mp440.35 MB
18 - Getting the Most Out of docstrings 1 - PEP 257 and docutils.mp438.58 MB
72 - K – Nearest Neighbor Classifier Implementation.mp438.31 MB
66 - Basic Regression Model Implementation to Predict House Prices.mp435.83 MB
172 - Deep Learning Hello World! Classifying the MNIST Data.mp434.69 MB
45 - Using the Reactive Extensions for Python (RxPY).mp433.64 MB
175 - Optimizing a Simple Model in Pure Theano.mp433.58 MB
181 - Reusing Pre-trained Models in New Applications.mp431.83 MB
05 - Extensive Standard Library.mp431.14 MB
185 - Recurrent Networks –Training a Sentiment Analysis Model for Text.mp429.72 MB
51 - Interfacing with C Code Using Cython.mp427.33 MB
73 - Preprocessing Data Using Different Techniques.mp426.46 MB
178 - Convolutional and Pooling Layers.mp425.35 MB
183 - Recurrent Layers.mp424.84 MB
47 - Building a High-Level Microservice with Flask.mp424.79 MB
176 - Keras Behind the Scenes.mp424.43 MB
15 - PEP 8 and Writing Readable Code.mp423.79 MB
180 - Loading Pre-trained Models with Theano.mp423.52 MB
88 - Evaluating Cars based on Their Characteristics.mp423.16 MB
02 - Python Basic Syntax and Block Structure.mp422.54 MB
26 - Using Multiprocessing.mp421.90 MB
177 - Fully Connected or Dense Layers.mp421.89 MB
10 - Finding Packages in the Python Package Index.mp421.78 MB
187 - Captioning TensorFlow – Google's Machine Learning Library.mp421.61 MB
171 - Open Source Libraries for Deep Learning.mp421.33 MB
186 - Bonus Challenge – Automatic Image Captioning.mp421.25 MB
06 - New in Python 3.5.mp421.01 MB
153 - Building a Face Recognizer Using a Local Binary Patterns Histogram.mp420.53 MB
179 - Large Scale Datasets, ImageNet, and Very Deep Neural Networks.mp420.32 MB
92 - Building a Linear Classifier Using Support Vector Machine.mp420.20 MB
83 - Building a Logistic Regression Classifier.mp420.20 MB
144 - Creating Features Using Visual Codebook and Vector Quantization.mp419.96 MB
110 - Constructing a k-nearest Neighbors Classifier.mp419.77 MB
124 - Identifying Patterns in Text Using Topic Modelling.mp419.76 MB
75 - Building a Linear Regressor.mp419.66 MB
37 - Descriptors.mp419.63 MB
182 - Theano for Loops – the scan Module.mp419.47 MB
174 - Understanding Deep Learning with Theano.mp419.26 MB
137 - Building Conditional Random Fields for Sequential Text Data.mp419.05 MB
81 - Estimating bicycle demand distribution.mp417.97 MB
121 - Building a Text Classifier.mp417.97 MB
169 - The Course Overview.mp417.84 MB
03 - Built-in Data Structures and Comprehensions.mp417.79 MB
136 - Building Hidden Markov Models for Sequential Data.mp417.70 MB
76 - Regression Accuracy and Model Persistence.mp417.50 MB
27 - Understanding Why This Isn't Like Parallel Processing.mp417.40 MB
39 - Using the unittest Package.mp417.13 MB
97 - Building an Event Predictor.mp416.95 MB
79 - Estimating housing prices.mp416.90 MB
142 - Detecting Corners and SIFT Feature Points.mp416.86 MB
16 - Using Version Control.mp416.75 MB
100 - Compressing an Image Using Vector Quantization.mp416.33 MB
139 - Operating on Images Using OpenCV-Python.mp416.06 MB
87 - Visualizing the Confusion Matrix and Extracting the Performance Report.mp415.79 MB
07 - Downloading and Installing Python.mp415.34 MB
108 - Building Machine Learning Pipelines.mp415.17 MB
91 - Extracting the Income Bracket.mp415.04 MB
59 - Installing the pandas Library.mp414.97 MB
104 - Automatically Estimating the Number of Clusters Using DBSCAN.mp414.94 MB
01 - The Course Overview.mp414.93 MB
50 - Accessing a Dynamic Library Using ctypes.mp414.92 MB
44 - Building a Simple Reactive Programming Framework.mp414.64 MB
123 - Analyzing the Sentiment of a Sentence.mp414.39 MB
54 - Data Mining Basic Concepts and Applications.mp414.24 MB
89 - Extracting Validation Curves.mp414.08 MB
64 - Linear Regression Basic Model Approach.mp414.03 MB
107 - Building Function Composition for Data Processing.mp413.67 MB
140 - Detecting Edges.mp413.63 MB
33 - Function Annotations.mp413.61 MB
102 - Grouping Data Using Agglomerative Clustering.mp413.54 MB
99 - Clustering Data Using the k-means Algorithm.mp413.45 MB
28 - Using the asyncio Event Loop and Coroutine Scheduler.mp413.35 MB
30 - Synchronizing Multiple Tasks.mp413.32 MB
94 - Tackling Class Imbalance.mp413.30 MB
132 - Transforming Data into the Time Series Format.mp413.23 MB
32 - Using Function Decorators.mp412.98 MB
131 - Building a Speech Recognizer.mp412.94 MB
48 - Building a Low-Level Microservice with nameko.mp412.78 MB
103 - Evaluating the Performance of Clustering Algorithms.mp412.74 MB
116 - Preprocessing Data Using Tokenization.mp412.67 MB
04 - First-Class Functions and Classes.mp412.33 MB
77 - Building a Ridge Regressor.mp412.30 MB
21 - Handling Command-Line Arguments with argparse.mp412.23 MB
82 - Building a Simple Classifier.mp412.21 MB
95 - Extracting Confidence Measurements.mp412.01 MB
60 - Installing Matplotlib.mp411.96 MB
138 - Analyzing Stock Market Data with Hidden Markov Models.mp411.84 MB
120 - Building a Bag-of-Words Model.mp411.71 MB
11 - Creating an Empty Package.mp411.59 MB
141 - Histogram Equalization.mp411.46 MB
34 - Class Decorators.mp411.44 MB
78 - Building a Polynomial Regressor.mp411.43 MB
145 - Training an Image Classifier Using Extremely Random Forests.mp411.41 MB
36 - Context Managers.mp411.35 MB
105 - Finding Patterns in Stock Market Data.mp411.34 MB
31 - Communicating Across the Network.mp411.34 MB
101 - Building a Mean Shift Clustering.mp411.26 MB
09 - Installing Packages with pip.mp411.04 MB
148 - Building a Face Detector using Haar Cascades.mp411.01 MB
42 - Using Nose for Unified Test Discover and Reporting.mp411.00 MB
98 - Estimating Traffic.mp410.82 MB
135 - Extracting Statistics from Time Series.mp410.76 MB
40 - Using unittest.mock.mp410.55 MB
74 - Label Encoding.mp410.54 MB
49 - Advantages and Disadvantages of Compiled Code.mp410.42 MB
96 - Finding Optimal Hyper-Parameters.mp410.42 MB
160 - Building an Optical Character Recognizer Using Neural Networks.mp410.37 MB
115 - Generating Movie Recommendations.mp410.20 MB
158 - Building a Recurrent Neural Network for Sequential Data Analysis.mp410.18 MB
152 - Performing Blind Source Separation.mp410.05 MB
122 - Identifying the Gender.mp410.00 MB
35 - Metaclasses.mp49.83 MB
128 - Synthesizing Music.mp49.81 MB
106 - Building a Customer Segmentation Model.mp49.78 MB
111 - Constructing a k-nearest Neighbors Regressor.mp49.75 MB
41 - Using unittest's Test Discovery.mp49.72 MB
52 - The Course Overview.mp49.69 MB
130 - Building Hidden Markov Models.mp49.60 MB
56 - Basics of Python.mp49.58 MB
163 - Animating Bubble Plots.mp49.43 MB
70 - Support Vector Machine.mp49.40 MB
125 - Reading and Plotting Audio Data.mp49.35 MB
126 - Transforming Audio Signals into the Frequency Domain.mp49.32 MB
173 - Introduction to Backpropagation.mp49.32 MB
13 - Importing One of the Package's Modules from Another.mp49.29 MB
112 - Computing the Euclidean Distance Score.mp49.21 MB
154 - Building a Perceptron.mp49.19 MB
62 - Data Cleaning.mp49.19 MB
20 - Making a Package Executable via python -m.mp49.19 MB
156 - Building a deep neural network.mp49.15 MB
65 - Evaluating Regression Models.mp49.14 MB
69 - K – Nearest Neighbors Classifier.mp48.89 MB
58 - Installing the Numpy Library.mp48.80 MB
117 - Stemming Text Data.mp48.77 MB
84 - Building a Naive Bayes’ Classifier.mp48.74 MB
22 - Interacting with the User.mp48.64 MB
53 - Brief Introduction to Data Mining.mp48.59 MB
38 - Understanding the Principles of Unit Testing.mp48.50 MB
151 - Performing Kernel Principal Component Analysis.mp48.42 MB
63 - Data Preprocessing Techniques.mp48.41 MB
157 - Creating a Vector Quantizer.mp48.36 MB
113 - Computing the Pearson Correlation Score.mp48.32 MB
118 - Converting Text to Its Base Form Using Lemmatization.mp48.25 MB
149 - Building Eye and Nose Detectors.mp48.23 MB
86 - Evaluating the Accuracy Using Cross-Validation.mp48.21 MB
46 - Microservices and the Advantages of Process Isolation.mp48.20 MB
17 - Using venv to Create a Stable and Isolated Work Area.mp48.15 MB
129 - Extracting Frequency Domain Features.mp48.13 MB
109 - Finding the Nearest Neighbors.mp48.05 MB
161 - Plotting 3D Scatter plots.mp48.03 MB
93 - Building Nonlinear Classifier Using SVMs.mp48.00 MB
12 - Adding Modules to the Package.mp47.99 MB
150 - Performing Principal Component Analysis.mp47.98 MB
146 - Building an object recognizer.mp47.72 MB
127 - Generating Audio Signals with Custom Parameters.mp47.64 MB
80 - Computing relative importance of features.mp47.58 MB
19 - Getting the Most Out of docstrings 2 - doctest.mp47.42 MB
119 - Dividing Text Using Chunking.mp47.42 MB
170 - What Is Deep Learning.mp47.37 MB
143 - Building a Star Feature Detector.mp47.35 MB
90 - Extracting Learning Curves.mp47.31 MB
08 - Using the Command-Line and the Interactive Shell.mp47.10 MB
147 - Capturing and Processing Video from a Webcam.mp46.95 MB
68 - Logistic Regression.mp46.92 MB
114 - Finding Similar Users in a Dataset.mp46.89 MB
134 - Operating on Time Series Data.mp46.79 MB
168 - Animating Dynamic Signals.mp46.79 MB
29 - Waiting for Data to Become Available.mp46.66 MB
184 - Recurrent Versus Convolutional Layers.mp46.58 MB
85 - Splitting the Dataset for Training and Testing.mp46.14 MB
165 - Plotting Date-Formatted Time Series Data.mp45.96 MB
155 - Building a Single-Layer Neural Network.mp45.93 MB
164 - Drawing Pie Charts.mp45.57 MB
133 - Slicing Time Series Data.mp45.32 MB
55 - Why Python.mp45.22 MB
159 - Visualizing the Characters in an Optical Character Recognition Database.mp45.17 MB
43 - What Does Reactive Programming Mean.mp44.82 MB
24 - Using Shell Scripts or Batch Files to Run Our Programs.mp44.62 MB
14 - Adding Static Data Files to the Package.mp44.54 MB
167 - Visualizing Heat Maps.mp44.00 MB
57 - Installing IPython.mp43.88 MB
61 - Installing scikit-learn.mp43.75 MB
166 - Plotting Histograms.mp43.67 MB
162 - Plotting Bubble Plots.mp43.66 MB