Python Fundamentals LiveLessons Part IV: Natural Language Processing (NLP); Data Mining Twitter®; IBM Watson® & Cognitive Computing (Building a Speech-to-Speech Translator)

Python Fundamentals LiveLessons Part IV: Natural Language Processing (NLP); Data Mining Twitter®; IBM Watson® & Cognitive Computing (Building a Speech-to-Speech Translator)
Python Fundamentals LiveLessons Part IV: Natural Language Processing (NLP); Data Mining Twitter®; IBM Watson® & Cognitive Computing (Building a Speech-to-Speech Translator)
English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 11h 25m | 4.34 GB

The professional programmer’s Deitel® video guide to Python development with the powerful IPython and Jupyter Notebooks platforms.

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 demonstrates coding with the interactive IPython interpreter and Jupyter Notebooks. You 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 introduces 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 IV:

  • Lesson 11–Natural Language Processing (NLP)–Install and use the TextBlob, NLTK, Textatistic and spaCy NLP libraries;, tokenize text into words and sentences; parts-of-speech tagging (noun, verb, etc.); sentiment analysis (positive, negative or neutral); detect the language of text; translate between languages; get word roots via stemming and lemmatization; spell checking and correction; word definitions, synonyms and antonyms; remove stop words from text; create word-cloud visualizations; determine text readability.
  • Lesson 12–Data Mining Twitter®–Access tweets and other information on Twitter with Tweepy–a popular Python Twitter API client; search past tweets with the Twitter Search API; sample the live tweet stream with the Twitter Streaming API; work with tweet object meta data; use NLP techniques to clean and preprocess tweets for analysis; perform sentiment analysis on tweets; spot trending topics with Twitter’s Trends API; map tweets using folium and OpenStreetMap.
  • Lesson 13–IBM Watson® and Cognitive Computing–Intro to Watson and its free Lite tier services; demos of several Watson services; registering for an IBM Cloud account; set up and get credentials for Watson services; install the Watson Developer Cloud Python SDK; build a Traveler’s companion language translator app that mashes up the Watson Speech to Text, Language Translator and Text to Speech services.
Table of Contents

1 Introduction to Python Fundamentals – Part 4
2 Lesson overview
3 Custom Class Account–Test-Driving Class Account
4 Custom Class Account–Account Class Definition
5 Self Check
6 Controlling Access to Attributes
7 Properties for Data Access–Test-Driving Class Time
8 Properties for Data Access–Class Time Definition
9 Self Check
10 Properties for Data Access–Class Time Definition Notes
11 Simulating ‘Private’ Attributes
12 Case Study – Card Shuffling and Dealing Simulation–Test Driving Classes Card and DeckOfCards
13 Case Study – Card Shuffling and Dealing Simulation–Class Card and an Introduction to Class Attributes
14 Case Study – Card Shuffling and Dealing Simulation–Class DeckOfCards
15 Case Study – Card Shuffling and Dealing Simulation–Displaying Card Images with Matplotlib
16 Self Check
17 Inheritance – Base Classes and Subclasses
18 Building an Inheritance Hierarchy and Introducing Polymorphism–Base Class CommissionEmployee
19 Building an Inheritance Hierarchy and Introducing Polymorphism–Sublass SalariedCommissionEmployee
20 Building an Inheritance Hierarchy and Introducing Polymorphism–Processing CommissionEmployees and SalariedCommissionEmployees Polymorphically
21 Duck Typing and Polymorphism
22 Operator Overloading
23 Test-Driving Class Complex
24 Class Complex Definition
25 Self Check
26 Named Tuples
27 A Brief Intro to Python 3.7’s New Data Classes
28 A Brief Intro to Python 3.7’s New Data Classes–Creating a Card Data Class
29 A Brief Intro to Python 3.7’s New Data Classes–Using the Card Data Class
30 Self Check
31 A Brief Intro to Python 3.7’s New Data Classes–Advantages Over Named Tuples and Traditional Classes
32 Unit Testing with Docstrings and doctest
33 Self Check
34 Namespaces and Scopes
35 Intro to Data Science – Time Series and Simple Linear Regression–Introduction
36 Intro to Data Science – Time Series and Simple Linear Regression–Components of the Simple Linear Regression Calculation
37 Intro to Data Science – Time Series and Simple Linear Regression–Loading the Average High Temperatures into a DataFrame
38 Intro to Data Science – Time Series and Simple Linear Regression–Cleaning the Data
39 Intro to Data Science – Time Series and Simple Linear Regression–Calculating Basic Descriptive Statistics for the Dataset
40 Intro to Data Science – Time Series and Simple Linear Regression–Forecasting Future January Average High Temperatures
41 Intro to Data Science – Time Series and Simple Linear Regression–Plotting the Average High Temperatures and a Regression Line
42 Lesson overview
43 Introduction
44 TextBlob
45 Create a TextBlob
46 Tokenizing Text into Sentences and Words
47 Parts-of-Speech Tagging
48 Extracting Noun Phrases
49 Sentiment Analysis with TextBlob’s Default Sentiment Analyzer
50 Sentiment Analysis with the NaiveBayesAnalyzer
51 Language Detection and Translation
52 Inflection – Pluralization and Singularization
53 Spell Checking and Correction
54 Normalization – Stemming and Lemmatization
55 Word Frequencies
56 Getting Definitions, Synonyms and Antonyms from WordNet
57 Deleting Stop Word
58 n-grams
59 Visualizing Word Frequencies with Pandas
60 Visualizing Word Frequencies with Word Clouds
61 Readability Assessment with Textatistic
62 Named Entity Recognition with spaCy
63 Similarity Detection with spaCy
64 Lesson overview
65 Introduction
66 Overview of the Twitter APIs
67 Creating a Twitter Developer Account
68 Getting Twitter Credentials–Creating an App
69 What’s in a Tweet
70 Tweepy
71 Authenticating with Twitter Via Tweepy
72 Getting Information About a Twitter Account
73 Self Check
74 Introduction to Tweepy Cursors – Getting an Account’s Followers and Friends
75 Determining an Account’s Followers
76 Self Check
77 Determining Whom an Account Follows
78 Getting a User’s Recent Tweets
79 Self Check
80 Searching Recent Tweets
81 Self Check
82 Spotting Trends – Twitter Trends API
83 Places with Trending Topics
84 Getting a List of Trending Topics
85 Self Check
86 Create a Word Cloud from Trending Topics
87 Self Check
88 Cleaning_Preprocessing Tweets for Analysis
89 Twitter Streaming API
90 Creating a Subclass of StreamListener
91 Initiating Stream Processing
92 Twitter Restrictions Note
93 Tweet Sentiment Analysis
94 Geocoding and Mapping
95 Getting and Mapping the Tweets
96 Utility Functions in tweetutilities.py and Class LocationListener
97 Lesson overview
98 Introduction to Watson
99 IBM Cloud Account and Cloud Console
100 Watson Services – Watson Assistant Demo
101 Watson Services – Visual Recognition
102 Watson Services – Speech to Text
103 Watson Services – Text to Speech
104 Watson Services – Language Translator
105 Watson Services – Natural Language Understanding
106 Watson Services – Personality Insights
107 Additional Services and Tools
108 Watson Developer Cloud Python SDK
109 Case Study – Traveler’s Companion Translation App
110 Before You run the App
111 Before You run the App – Registering for the Speech to Text Service
112 Before You run the App – Registering for the Text to Speech Service
113 Before You run the App – Registering for the Language Translator Service
114 Test-Driving the App
115 SimpleLanguageTranslator.py Script Walkthrough
116 SimpleLanguageTranslator.py Script Walkthrough – Importing Watson SDK Classes from the ibm_watson Module
117 SimpleLanguageTranslator.py Script Walkthrough – Other Imported Modules
118 SimpleLanguageTranslator.py Script Walkthrough – Main Program – Function run_translator
119 SimpleLanguageTranslator.py Script Walkthrough – Function speech_to_text
120 SimpleLanguageTranslator.py Script Walkthrough – Function translate
121 SimpleLanguageTranslator.py Script Walkthrough – Function text_to_speech
122 SimpleLanguageTranslator.py Script Walkthrough – Function record_audio
123 SimpleLanguageTranslator.py Script Walkthrough – Function play_audio
124 Watson Resources