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 handson experience with popular Python data mining and machine learning algorithms. First, well expand your knowledge base by covering basic to advanced concepts of Python. Then, well give you handson experience with the popular Python data mining algorithms. Going forward, well learn how to perform various machine learning tasks in the real world. Finally, well 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.
Resources: Code downloads and errata:
Data Mining with Python: Implementing Classification and Regression
PATH PRODUCTS
This path navigates across the following products (in sequential order):
Mastering Python  Second Edition (5h 21m)
Data Mining with Python: Implementing Classification and Regression (2h 3m)
Python Machine Learning Solutions (4h 27m)
Deep Learning with Python (1h 45m)
Table of Contents

Chapter 1 : Mastering Python  Second Edition
 The Course Overview 00:03:25
 Python Basic Syntax and Block Structure 00:11:54
 Builtin Data Structures and Comprehensions 00:08:55
 FirstClass Functions and Classes 00:05:50
 Extensive Standard Library 00:05:56
 New in Python 3.5 00:06:02
 Downloading and Installing Python 00:05:17
 Using the CommandLine and the Interactive Shell 00:04:01
 Installing Packages with pip 00:03:16
 Finding Packages in the Python Package Index 00:04:29
 Creating an Empty Package 00:05:50
 Adding Modules to the Package 00:05:31
 Importing One of the Package's Modules from Another 00:05:26
 Adding Static Data Files to the Package 00:02:53
 PEP 8 and Writing Readable Code 00:07:51
 Using Version Control 00:04:48
 Using venv to Create a Stable and Isolated Work Area 00:04:41
 Getting the Most Out of docstrings 1: PEP 257 and docutils 00:08:00
 Getting the Most Out of docstrings 2: doctest 00:04:04
 Making a Package Executable via python m 00:05:52
 Handling CommandLine Arguments with argparse 00:06:22
 Interacting with the User 00:04:39
 Executing Other Programs with Subprocess 00:09:10
 Using Shell Scripts or Batch Files to Run Our Programs 00:03:01
 Using concurrent.futures 00:13:53
 Using Multiprocessing 00:11:22
 Understanding Why This Isn't Like Parallel Processing 00:08:02
 Using the asyncio Event Loop and Coroutine Scheduler 00:06:52
 Waiting for Data to Become Available 00:03:30
 Synchronizing Multiple Tasks 00:06:18
 Communicating Across the Network 00:03:45
 Using Function Decorators 00:06:45
 Function Annotations 00:07:09
 Class Decorators 00:05:53
 Metaclasses 00:05:35
 Context Managers 00:05:52
 Descriptors 00:05:38
 Understanding the Principles of Unit Testing 00:05:07
 Using the unittest Package 00:07:28
 Using unittest.mock 00:06:12
 Using unittest's Test Discovery 00:04:30
 Using Nose for Unified Test Discover and Reporting 00:03:42
 What Does Reactive Programming Mean? 00:02:50
 Building a Simple Reactive Programming Framework 00:07:22
 Using the Reactive Extensions for Python (RxPY) 00:10:22
 Microservices and the Advantages of Process Isolation 00:04:13
 Building a HighLevel Microservice with Flask 00:09:59
 Building a LowLevel Microservice with nameko 00:06:25
 Advantages and Disadvantages of Compiled Code 00:04:42
 Accessing a Dynamic Library Using ctypes 00:07:59
 Interfacing with C Code Using Cython 00:12:35

Chapter 2 : Data Mining with Python: Implementing Classification and Regression
 The Course Overview 00:03:55
 A Brief Introduction to Data Mining 00:04:37
 Data Mining Basic Concepts and Applications 00:07:06
 Why Python? 00:03:31
 Basics of Python 00:05:55
 Installing IPython 00:02:10
 Installing the Numpy Library 00:04:33
 Installing the pandas Library 00:05:32
 Installing Matplotlib 00:02:42
 Installing scikitlearn 00:02:37
 Data Cleaning 00:05:31
 Data Preprocessing Techniques 00:05:08
 Linear Regression Basic Model Approach 00:08:24
 Evaluating Regression Models 00:05:31
 Basic Regression Model Implementation to Predict House Prices 00:09:20
 Regression Model Implementation to Predict Television Show Viewers 00:09:46
 Logistic Regression 00:04:02
 K – Nearest Neighbors Classifier 00:05:51
 Support Vector Machine 00:05:42
 Logistic Regression Model Implementation 00:10:45
 K – Nearest Neighbor Classifier Implementation 00:10:44

Chapter 3 : Python Machine Learning Solutions
 Preprocessing Data Using Different Techniques 00:06:39
 Label Encoding 00:02:26
 Building a Linear Regressor 00:04:26
 Regression Accuracy and Model Persistence 00:03:41
 Building a Ridge Regressor 00:02:41
 Building a Polynomial Regressor 00:02:33
 Estimating housing prices 00:03:46
 Computing relative importance of features 00:01:54
 Estimating bicycle demand distribution 00:04:35
 Building a Simple Classifier 00:03:40
 Building a Logistic Regression Classifier 00:04:51
 Building a Naive Bayes’ Classifier 00:02:11
 Splitting the Dataset for Training and Testing 00:01:23
 Evaluating the Accuracy Using CrossValidation 00:04:07
 Visualizing the Confusion Matrix and Extracting the Performance Report 00:04:14
 Evaluating Cars based on Their Characteristics 00:05:12
 Extracting Validation Curves 00:02:49
 Extracting Learning Curves 00:01:37
 Extracting the Income Bracket 00:03:36
 Building a Linear Classifier Using Support Vector Machine 00:04:24
 Building Nonlinear Classifier Using SVMs 00:01:47
 Tackling Class Imbalance 00:02:54
 Extracting Confidence Measurements 00:02:37
 Finding Optimal HyperParameters 00:02:17
 Building an Event Predictor 00:03:45
 Estimating Traffic 00:02:40
 Clustering Data Using the kmeans Algorithm 00:03:08
 Compressing an Image Using Vector Quantization 00:03:38
 Building a Mean Shift Clustering 00:02:36
 Grouping Data Using Agglomerative Clustering 00:03:05
 Evaluating the Performance of Clustering Algorithms 00:02:56
 Automatically Estimating the Number of Clusters Using DBSCAN 00:03:34
 Finding Patterns in Stock Market Data 00:02:35
 Building a Customer Segmentation Model 00:02:22
 Building Function Composition for Data Processing 00:03:26
 Building Machine Learning Pipelines 00:03:55
 Finding the Nearest Neighbors 00:01:56
 Constructing a knearest Neighbors Classifier 00:04:19
 Constructing a knearest Neighbors Regressor 00:02:44
 Computing the Euclidean Distance Score 00:02:09
 Computing the Pearson Correlation Score 00:01:55
 Finding Similar Users in a Dataset 00:01:35
 Generating Movie Recommendations 00:02:35
 Preprocessing Data Using Tokenization 00:03:00
 Stemming Text Data 00:02:23
 Converting Text to Its Base Form Using Lemmatization 00:02:11
 Dividing Text Using Chunking 00:02:03
 Building a BagofWords Model 00:02:59
 Building a Text Classifier 00:04:43
 Identifying the Gender 00:02:18
 Analyzing the Sentiment of a Sentence 00:03:10
 Identifying Patterns in Text Using Topic Modelling 00:04:52
 Reading and Plotting Audio Data 00:02:34
 Transforming Audio Signals into the Frequency Domain 00:02:10
 Generating Audio Signals with Custom Parameters 00:01:46
 Synthesizing Music 00:02:10
 Extracting Frequency Domain Features 00:02:06
 Building Hidden Markov Models 00:02:19
 Building a Speech Recognizer 00:03:12
 Transforming Data into the Time Series Format 00:03:07
 Slicing Time Series Data 00:01:32
 Operating on Time Series Data 00:01:42
 Extracting Statistics from Time Series 00:02:29
 Building Hidden Markov Models for Sequential Data 00:04:16
 Building Conditional Random Fields for Sequential Text Data 00:04:27
 Analyzing Stock Market Data with Hidden Markov Models 00:02:26
 Operating on Images Using OpenCVPython 00:03:08
 Detecting Edges 00:02:47
 Histogram Equalization 00:02:31
 Detecting Corners and SIFT Feature Points 00:03:47
 Building a Star Feature Detector 00:01:35
 Creating Features Using Visual Codebook and Vector Quantization 00:04:11
 Training an Image Classifier Using Extremely Random Forests 00:02:30
 Building an object recognizer 00:01:54
 Capturing and Processing Video from a Webcam 00:01:58
 Building a Face Detector using Haar Cascades 00:02:40
 Building Eye and Nose Detectors 00:01:54
 Performing Principal Component Analysis 00:02:17
 Performing Kernel Principal Component Analysis 00:02:03
 Performing Blind Source Separation 00:02:16
 Building a Face Recognizer Using a Local Binary Patterns Histogram 00:04:14
 Building a Perceptron 00:02:40
 Building a SingleLayer Neural Network 00:01:37
 Building a deep neural network 00:02:19
 Creating a Vector Quantizer 00:01:41
 Building a Recurrent Neural Network for Sequential Data Analysis 00:02:24
 Visualizing the Characters in an Optical Character Recognition Database 00:01:48
 Building an Optical Character Recognizer Using Neural Networks 00:02:28
 Plotting 3D Scatter plots 00:02:43
 Plotting Bubble Plots 00:01:16
 Animating Bubble Plots 00:01:57
 Drawing Pie Charts 00:01:34
 Plotting DateFormatted Time Series Data 00:01:33
 Plotting Histograms 00:01:05
 Visualizing Heat Maps 00:01:15
 Animating Dynamic Signals 00:02:07

Chapter 4 : Deep Learning with Python
 The Course Overview 00:03:52
 What Is Deep Learning? 00:04:09
 Open Source Libraries for Deep Learning 00:04:31
 Deep Learning "Hello World!" Classifying the MNIST Data 00:07:57
 Introduction to Backpropagation 00:05:24
 Understanding Deep Learning with Theano 00:05:04
 Optimizing a Simple Model in Pure Theano 00:07:54
 Keras Behind the Scenes 00:05:24
 Fully Connected or Dense Layers 00:04:46
 Convolutional and Pooling Layers 00:06:40
 Large Scale Datasets, ImageNet, and Very Deep Neural Networks 00:05:17
 Loading Pretrained Models with Theano 00:05:16
 Reusing Pretrained Models in New Applications 00:07:22
 Theano "for" Loops – the "scan" Module 00:05:18
 Recurrent Layers 00:06:28
 Recurrent Versus Convolutional Layers 00:03:43
 Recurrent Networks –Training a Sentiment Analysis Model for Text 00:06:50
 Bonus Challenge – Automatic Image Captioning 00:04:41
 Captioning TensorFlow – Google's Machine Learning Library 00:05:15
Product Information
 Title: Learning Path: Deep Dive into Python Machine Learning
 Author(s):
 Release date: November 2016
 Publisher(s): Packt Publishing
 ISBN: 9781787285880