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The professional programmer’s Deitel® video guide to Python development with the powerful IPython and Jupyter Notebooks platforms. Part V focuses on machine-learning, deep learning and big-data case studies, using popular AI and big-data tools in Python.
Python Fundamentals LiveLessons with Paul Deitel is a code-oriented presentation of Python—one of the world’s most popular and fastest growing languages. In the context of scores of real-world code examples ranging from individual snippets to complete scripts, Paul will demonstrate coding with the interactive IPython interpreter and Jupyter Notebooks. You’ll quickly become familiar with the Python language, its popular programming idioms, key Python Standard Library modules and several popular open-source libraries. In the Intro to Data Science videos, Paul lays the groundwork for later lessons in which he’ll introduce some of today’s most compelling, leading-edge computing technologies, including natural language processing, data mining Twitter® for sentiment analysis, cognitive computing with IBM® Watson™, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, sentiment analysis through deep learning with recurrent neural networks, big data with Hadoop®, Spark™ streaming, NoSQL databases and the Internet of Things.
What you will learn in Part V’s case studies:
- Lesson 14–Machine Learning: Classification, Regression and Clustering–Use scikit-learn with popular datasets to perform machine learning studies; Use Seaborn and Matplotlib to visualize and explore data; Perform supervised machine learning with k-nearest neighbors classification and linear regression; Perform multi-classification with Digits dataset; Divide a dataset into training, testing and validation sets; Tune hyperparameters with k-fold cross-validation; Measure model performance; Display a confusion matrix showing classification prediction hits and misses; Perform multiple linear regression with the California Housing dataset; Perform dimensionality reduction with PCA and t-SNE on the Iris and Digits datasets to prepare them for two-dimensional visualizations. Perform unsupervised machine learning with k-means clustering and the Iris dataset.
- Lesson 15–Deep Learning–What a neural network is and how it enables deep learning; Create Keras neural networks;Keras layers, activation functions, loss functions and optimizers; Use a Keras convolutional neural network (CNN) trained on the MNIST dataset to build a computer vision application that recognizes handwritten digits; Use a Keras recurrent neural network (RNN) trained on the IMDb dataset to create a sentiment analysis application that performs binary classification of positive and negative movie reviews.
- Lesson 16–Big Data: Hadoop, Spark, 17–Manipulate a SQLite relational database using SQL; Understand the four major types of NoSQL databases; Store tweets in a MongoDB NoSQL JSON document database and visualize them on a Folium map; Apache Hadoop and how it’s used in big-data batch-processing applications; Build a Hadoop MapReduce application on Microsoft’s Azure HDInsight cloud service; Apache Spark and how it’s used in high-performance, real-time big-data applications; Process mini-batches of data with Spark streaming; Internet of Things (IoT) and the publish/subscribe model; Publish messages from a simulated Internet-connected device and visualize messages in a dashboard; Subscribe to PubNub’s sample live streams and visualize the data.
Table of Contents
1 Lesson overview
2 Introduction to Machine Learning
3 Case Study – Classification with k-Nearest Neighbors and the Digits Dataset, Part 1
4 k-Nearest Neighbors Algorithm
5 k-Nearest Neighbors Algorithm – Hyperparameters and Hyperparameter Tuning
6 Loading the Dataset
7 Loading the Dataset – Displaying the Description
8 Loading the Dataset – Checking the Sample and Target Sizes
9 Loading the Dataset – A Sample Digit Image
10 Loading the Dataset – Preparing the Data for Use with Scikit-Learn
11 Visualizing the Data
12 Splitting the Data for Training and Testing
13 Creating the Model
14 Training the Model
15 Predicting Digit Classes
16 Case Study – Classification with k-Nearest Neighbors and the Digits Dataset, Part 2
17 Metrics for Model Accuracy – Estimator Method score
18 Metrics for Model Accuracy – Confusion Matrix
19 Metrics for Model Accuracy – Classification Report
20 Metrics for Model Accuracy – Visualizing the Confusion Matrix
21 K-Fold Cross-Validation
22 Running Multiple Models to Find the Best One
23 Hyperparameter Tuning
24 Case Study – Time Series and Simple Linear Regression
25 Loading the Average High Temperatures into a DataFrame
26 Splitting the Data for Training and Testing
27 Training the Model
28 Testing the Model
29 Predicting Future Temperatures and Estimating Past Temperatures
30 Visualizing the Dataset with the Regression Line
31 Overfitting_Underfitting
32 Case Study – Multiple Linear Regression with the California Housing Dataset
33 Loading the Dataset
34 Exploring the Data with Pandas
35 Visualizing the Features
36 Splitting the Data for Training and Testing
37 Training the Model
38 Testing the Model
39 Visualizing the Expected vs. Predicted Prices
40 Regression Model Metrics
41 Choosing the Best Model
42 Case Study – Unsupervised Machine Learning, Part 1–Dimensionality Reduction
43 Loading the Digits Dataset
44 Creating a TSNE Estimator for Dimensionality Reduction
45 Transforming the Digits Dataset’s Features into Two Dimensions
46 Visualizing the Reduced Data
47 Visualizing the Reduced Data with Different Colors for Each Digit
48 Visualizing the Reduced Data in 3D
49 Case Study – Unsupervised Machine Learning, Part 2–k-Means Clustering
50 Loading the Iris Dataset
51 Exploring the Iris Dataset – Descriptive Statistics with Pandas
52 Visualizing the Dataset with a Seaborn pairplot
53 Using a KMeans Estimator
54 Dimensionality Reduction with Principal Component Analysis
55 Choosing the Best Clustering Estimator
56 Lesson overview
57 Introduction
58 Deep Learning Applications
59 Deep Learning Demos
60 Keras Resources
61 Keras Built-In Datasets
62 Custom Anaconda Environments
63 Neural Networks
64 Tensors
65 Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset
66 Reproducibility in Keras and Deep Learning
67 Basic Keras Neural Network
68 Loading the MNIST Dataset
69 Data Exploration
70 Visualizing Digits
71 Reshaping the Image Data
72 Normalizing the Image Data
73 One-Hot Encoding – Converting the Labels From Integers to Categorical Data
74 Creating the Neural Network
75 Adding Layers to the Network
76 Convolution
77 Adding a Conv2D Convolution Layer to Our Model
78 Dimensionality of the First Convolution Layer’s Output
79 Overfitting
80 Adding a Pooling Layer
81 Adding Another Convolutional Layer and Pooling Layer
82 Flattening the Results to One Dimension with a Keras Flatten Layer
83 Adding a Dense Layer to Reduce the Number of Features
84 Adding Another Dense Layer to Produce the Final Output
85 Printing the Model’s Summary
86 Visualizing a Model’s Structure
87 Compiling the Model
88 Training and Evaluating the Model
89 Evaluating the Model on Unseen Data
90 Making Predictions
91 Locating the Incorrect Predictions
92 Visualizing Incorrect Predictions
93 Displaying the Probabilities for Several Incorrect Predictions
94 Saving and Loading a Model
95 Visualizing Neural Network Training with TensorBoard
96 ConvnetJS – Browser-Based Deep-Learning Training and Visualization
97 Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset
98 Loading the IMDb Movie Reviews Dataset
99 Data Exploration
100 Movie Review Encodings and Decoding a Review
101 Data Preparation
102 Creating the Neural Network
103 Adding an Embedding Layer
104 Adding an LSTM Layer
105 Adding a Dense Output Layer
106 Compiling the Model and Displaying the Summary
107 Training and Evaluating the Model (1 of 2)
108 Training and Evaluating the Model (2 of 2)
109 Tuning Deep Learning Models
110 Lesson overview
111 Introduction–Databases
112 Introduction–Apache Hadoop and Apache Spark
113 Introduction–Internet of Things
114 Introduction–Experience Cloud and Desktop Big-Data Software
115 Introduction–Big Data Sources
116 Relational Databases and Structured Query Language (SQL)
117 A books Database
118 SELECT Queries
119 WHERE Clause
120 ORDER BY Clause
121 Merging Data from Multiple Tables – INNER JOIN
122 INSERT INTO Statement
123 UPDATE Statement
124 DELETE FROM Statement
125 NoSQL and NewSQL Big-Data Databases – A Brief Tour
126 NoSQL Key-Value Databases
127 NoSQL Document Databases
128 NoSQL Columnar Databases
129 NoSQL Graph Databases
130 NewSQL Databases
131 Case Study – A MongoDB JSON Document Database
132 Creating the MongoDB Atlas Cluster
133 Streaming Tweets into MongoDB
134 Hadoop
135 Hadoop Overview
136 Summarizing Word Lengths in Romeo and Juliet via MapReduce
137 Creating an Apache Hadoop Cluster in Microsoft Azure HDInsight – Part 1
138 Creating an Apache Hadoop Cluster in Microsoft Azure HDInsight – Part 2
139 Hadoop Streaming
140 Implementing the Mapper
141 Implementing the Reducer
142 Preparing to Run the MapReduce Example
143 Running the MapReduce Job
144 Spark Overview
145 Docker and the Jupyter Docker Stacks
146 Word Count with Spark
147 Spark Word Count on Microsoft Azure
148 Spark Streaming – Counting Twitter Hashtags Using the pysparknotebook Docker Stack
149 Streaming Tweets to a Socket
150 Summarizing Tweet Hashtags; Introducing Spark SQL
151 Internet of Things and Dashboards
152 Publish and Subscribe
153 Visualizing a PubNub Sample Live Stream with a Freeboard Dashboard
154 Simulating an Internet-Connected Thermostat in Python and Creating a Dashbboard in Freeboard.io
155 Creating a Python PubNub Subscriber
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