by Paul J. Deitel | Released August 2019 | ISBN: 0135917417

51+ hours of video instruction.OverviewThe professional programmer's Deitel® video guide to Python development with the powerful IPython and Jupyter Notebooks platforms.DescriptionPython 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.Download the code examples for this LiveLesson from https://github.com/pdeitel/PythonFundamentalsLiveLessons. This repository will be updated with the additional lessons' examples as the lessons are completed.About the InstructorPaul J. Deitel, CEO and Chief Technical Officer of Deitel & Associates, Inc., is a graduate of MIT, where he studied Information Technology. He holds the Sun (now Oracle) Certified Java Programmer and Certified Java Developer certifications, and is an Oracle Java Champion. Through Deitel & Associates, Inc., he has delivered Java, C#, Visual Basic, C++, C and Internet programming courses to industry clients, including Cisco, IBM, Sun Micro systems, Dell, Siemens, Lucent Technologies, Fidelity, NASA at the Kennedy Space Center, the National Severe Storm Laboratory, White Sands Missile Range, Rogue Wave Software, Boeing, SunGard Higher Education, Stratus, Cambridge Technology Partners, One Wave, Hyperion Software, Adra Systems, Entergy, CableData Systems, Nortel Networks, Puma, iRobot, Invensys and many more. He and his co-author, Dr. Harvey M. Deitel, are the world's best-selling programming-language textbook/professional book authors.Skill Level Beginner-to-IntermediateWhat you Will Learn in Part IBefore You Begin—Configure your system for Python, obtain the code examples, Python package managers, Paul's contact info Lesson 1—Test-Drives: Using IPython and Jupyter Notebooks—Work with snippets and scripts in the context of IPython and Jupyter NotebooksLesson 2—Intro to Python Programming—Variables, types, operators, strings, I/O, decisions, objects and dynamic typing Lesson 3—Control Statements—if, if…else, if…elif…else, for, while, break, continue, augmented assignments, boolean operators, intro to listsLesson 4—Functions—Custom function definitions, importing libraries, simulation with random-number generation, scope, default parameter values, keyword arguments, arbitrary argument lists, methods, intro to tuples, intro to functional-style programmingWhat you will learn in Part II:Lesson 5—Sequences: Lists and Tuples—Create, initialize and access the elements of lists and tuples; sort and search lists, and search tuples; pass lists and tuples to functions and methods; list methods; functional-style programming (lambdas, filter, map, reduce, list comprehensions, generator expressions, 2D lists); static visualization with the Seaborn and Matplotlib visualization libraries.Lesson 6—Dictionaries and Sets—Dictionaries of key—value pairs; sets of unique values; iterating through keys, values and key—value pairs; adding, removing and updating key—value pairs; dictionary and set comparison operators; set operators and methods; operators in and not in for membership testing; mutable set operations; dictionary and set comprehensions; dynamic visualization with the Seaborn and Matplotlib visualization libraries.Lesson 7—Array-Oriented Programming with NumPy—numpy module's high-performance ndarrays; how ndarrays differ from lists; comparing list vs. ndarrayperformance with the IPython %timeit magic; one-dimensional and multidimensionalndarrays; common ndarray manipulations; introduction to the pandas data manipulation library; one-dimensional Series and two-dimensional DataFrames; custom Series and DataFrame indices; basic descriptive statistics for data in aSeries and a DataFrame; customizing pandas output formatting.What you will learn in Part III:Lesson 8—Strings: A Deeper Look—String methods; string formatting; concatenating and repeating strings; stripping whitespace; string comparisons; search strings for substrings and replacing substrings; tokenizing strings; regular expressions for pattern matching, replacing substrings and validating data; manipulating data in pandas.Lesson 9—Files and Exceptions—Text-file processing; serializing objects into the JSON with the json module; with statement for avoiding “resource leaks”; exception handling; try…except statement; else clause; executing code when no exceptions occur in a try suite; finally clause; raise exceptions; more details on tracebacks; stack unwinding; CSV file processing via the csv module; loading and manipulating CSV files in pandas.Lesson 10—Object-Oriented Programming—Custom classes; controlling access to attributes; properties for data access; simulating “private” attributes; Python special methods for customizing string representations; inheritance, duck typing and polymorphism; class object; Python special methods for overloading operators; named tuples; Python 3.7 data classes; unit testing with doctest; namespaces and how they affect scope; Introduction to time series and simple linear regression.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.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.LiveLessons Video Training series publishes hundreds of hands-on, expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. This professional and personal technology video series features world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, IBM Press, Pearson IT Certification, Prentice Hall, Sams, and Que. Topics include: IT Certification, Programming, Web Development, Mobile Development, Home & Office Technologies, Business & Management, and more. View All LiveLessons on InformIT: http://www.informit.com/imprint/series_detail.aspx?ser=2185116 Show and hide more

1. Part 1
   • Introduction to Python Fundamentals: Part 1 00:23:55
2. Before You Begin
   • Lesson Overview: Before You Begin 00:02:24
   • Getting the code 00:00:49
   • Structure of the examples folder 00:04:26
   • Installing Anaconda 00:03:20
   • Updating Anaconda 00:05:18
   • Package managers 00:02:29
   • Installing jupyter-matplotlib 00:01:29
   • Twitter developer account 00:01:57
   • Getting your questions answered 00:01:50
3. Lesson 01: Test-Drives: Using IPython and Jupyter Notebooks
   • Lesson 01 Overview: Test-Drives: Using IPython and Jupyter Notebooks 00:03:49
   • Using IPython Interactive Mode as a Calculator 00:15:50
   • Executing a Python Program Using the IPython Interpreter 00:09:02
   • Writing and Executing Code in a Jupyter Notebook 00:24:11
4. Lesson 02: Intro to Python Programming
   • Lesson 02 Overview: Intro to Python Programming 00:03:32
   • Variables and Assignment Statements 00:13:14
   • Self Check 00:02:22
   • Arithmetic 00:12:13
   • Self Check 00:02:43
   • Function print and an Intro to Single- and Double-Quoted Strings 00:11:09
   • Self Check 00:01:51
   • Triple-Quoted Strings 00:07:33
   • Self Check 00:03:19
   • Getting Input from the User 00:10:53
   • Self Check 00:01:17
   • Decision Making: The if Statement and Comparison Operators 00:20:23
   • Self Check 00:02:08
   • Objects and Dynamic Typing 00:07:58
   • Self Check 00:01:10
   • Intro to Data Science: Basic Descriptive Statistics 00:11:48
   • Self Check 00:02:00
5. Lesson 03: Control Statements
   • Lesson 03 Overview: Control Statements 00:03:25
   • if Statement 00:07:57
   • Self Check 00:02:16
   • if...else and if...elif...else Statements 00:10:51
   • Self Check 00:01:51
   • while Statement 00:02:26
   • Self Check 00:01:18
   • for Statement; Iterables, Lists and Iterators; Built-in range Function 00:11:28
   • Self Check 00:02:13
   • Augmented Assignments 00:02:07
   • Self Check 00:00:48
   • Sequence-Controlled Iteration 00:06:19
   • Self Check 00:01:40
   • Sentinel-Controlled Iteration 00:08:12
   • Built-In Function range: A Deeper Look 00:03:38
   • Self Check 00:02:26
   • Using Type Decimal for Monetary Amounts 00:18:24
   • Self Check 00:02:17
   • break and continue Statements 00:02:28
   • Boolean Operators and, or and not 00:07:46
   • Self Check 00:02:37
   • Intro to Data Science: Measures of Central Tendency--Mean, Median and Mode 00:11:03
   • Self Check 00:03:49
6. Lesson 04: Functions
   • Lesson 04 Overview: Functions 00:03:28
   • Defining Functions 00:15:46
   • Self Check 00:01:47
   • Functions with Multiple Parameters 00:09:34
   • Self Check 00:01:50
   • Random-Number Generation 00:10:39
   • Self Check 00:02:09
   • Case Study: A Game of Chance 00:15:52
   • Self Check 00:02:16
   • math Module Functions 00:03:20
   • Default Parameter Values 00:03:21
   • Keyword Arguments 00:05:35
   • Arbitrary Argument Lists 00:04:54
   • Self Check 00:02:58
   • Methods: Functions That Belong to Objects 00:04:53
   • Scope Rules 00:11:27
   • import: A Deeper Look 00:05:38
   • Self Check 00:01:08
   • Passing Arguments to Functions: A Deeper Look 00:08:29
   • Self Check 00:01:21
   • Functional-Style Programming 00:04:49
   • Intro to Data Science: Measures of Dispersion 00:10:02
7. Part 2
   • Introduction to Python Fundamentals: Part 2 00:08:30
8. Lesson 05: Sequences: Lists and Tuples
   • Lesson 05 Overview: Sequences--Lists and Tuples 00:03:09
   • Lists 00:17:44
   • Self Check 00:03:41
   • Tuples 00:12:56
   • Self Check 00:02:06
   • Unpacking Sequences 00:11:31
   • Creating a primitive bar chart 00:04:58
   • Self Check 00:04:07
   • Sequence Slicing Part 1: Getting a Subset of a Sequence 00:08:32
   • Sequence Slicing Part 2: Modifying a List 00:06:01
   • Self Check 00:04:44
   • del Statement 00:03:22
   • Self Check 00:02:03
   • Passing Lists to Functions 00:05:02
   • Sorting Lists 00:06:20
   • Self Check 00:02:12
   • Searching Sequences 00:09:22
   • Self Check 00:01:31
   • Other List Methods 00:11:32
   • Self Check 00:02:45
   • Simulating Stacks with Lists 00:02:02
   • List Comprehensions 00:06:51
   • Self Check 00:02:48
   • Generator Expressions 00:05:55
   • Self Check 00:01:51
   • Filter, Map and Reduce 00:16:32
   • Self Check 00:05:31
   • Other Sequence Processing Functions 00:08:51
   • Self Check 00:03:58
   • Two-Dimensional Lists 00:06:37
   • Self Check 00:02:45
   • Intro to Data Science: Simulation and Static Visualizations 00:02:49
   • Sample Graphs for 600, 60,000 and 6,000,000 Die Rolls 00:06:02
   • Visualizing Die-Roll Frequencies and Percentages--Part 1 00:10:24
   • Visualizing Die-Roll Frequencies and Percentages--Part 2 00:11:10
   • Visualizing Die-Roll Frequencies and Percentages--Part 3 00:11:20
   • Visualizing Die-Roll Frequencies and Percentages--Part 4 00:07:22
9. Lesson 06: Dictionaries and Sets
   • Lesson 06 Overview: Dictionaries and Sets 00:03:37
   • Dictionaries 00:01:35
   • Creating a Dictionary 00:04:57
   • Self Check 00:01:16
   • Iterating through a Dictionary 00:03:31
   • Basic Dictionary Operarations 00:08:09
   • Self Check 00:01:28
   • Dictionary Methods keys and values 00:07:11
   • Self Check 00:01:46
   • Dictionary Comparisons 00:02:39
   • Example: Dictionary of Student Grades 00:04:28
   • Example: Word Counts 00:07:49
   • Python Standard Library Module collections 00:04:52
   • Self Check 00:02:24
   • Dictionary Method update 00:04:40
   • Dictionary Comprehensions 00:04:44
   • Self Check 00:01:34
   • Sets 00:07:26
   • Self Check 00:01:54
   • Comparing Sets 00:07:21
   • Self Check 00:02:29
   • Mathematical Set Operations 00:06:41
   • Self Check 00:02:25
   • Mutable Set Operators and Methods 00:06:11
   • Set Comprehensions 00:01:42
   • Intro to Data Science: Dynamic Visualizations--Using Simulation and Dynamic Visualization to Demonstrate the Law of Large Numbers 00:09:32
   • Intro to Data Science: Dynamic Visualizations--Implementing Dynamic Visualization, Part 1 00:17:38
10. Lesson 07: Array-Oriented Programming with Numpy
    • Lesson 07 Overview: Array-Oriented Programming with Numpy; Intro to Pandas 00:03:01
    • Creating arrays from Existing Data 00:03:23
    • Self Check 00:02:51
    • array Attributes 00:08:31
    • Self Check 00:00:57
    • Filling arrays with Specific Values 00:02:32
    • Creating arrays from Ranges 00:05:57
    • Self Check 00:01:38
    • List vs. array Performance: Introducing %timeit 00:10:10
    • Self Check 00:02:05
    • array Operators 00:07:35
    • Self Check 00:01:03
    • NumPy Calculation Methods 00:06:02
    • Self Check 00:02:38
    • Universal Functions 00:05:43
    • Self Check 00:00:54
    • Indexing and Slicing 00:05:58
    • Self Check 00:02:49
    • Views: Shallow Copies 00:05:32
    • Deep Copies 00:02:06
    • Reshaping and Transposing: reshape vs. resize 00:02:14
    • Reshaping and Transposing: flatten vs. ravel 00:03:23
    • Reshaping and Transposing: Transposing Rows and Columns 00:02:04
    • Reshaping and Transposing: Horizontal and Vertical Stacking 00:02:50
    • Self Check 00:01:22
    • Intro to Data Science: pandas Series and DataFrames 00:03:58
    • Intro to Data Science: pandas Series and DataFrames--pandas Series Part 1 00:08:12
    • Intro to Data Science: pandas Series and DataFrames--pandas Series Part 2 00:08:41
    • Self Check 00:04:12
    • Intro to Data Science: pandas Series and DataFrames--Creating DataFrames and Customizing Indices 00:05:35
    • Intro to Data Science: pandas Series and DataFrames--Accessing a DataFrame's Columns 00:01:48
    • Intro to Data Science: pandas Series and DataFrames--Selecting Rows via the loc and iloc Attributes 00:03:14
    • Intro to Data Science: pandas Series and DataFrames--Selecting Rows via Slices and Lists with the loc and iloc Attributes 00:03:04
    • Intro to Data Science: pandas Series and DataFrames--Selecting Subsets of the Rows and Columns 00:02:27
    • Intro to Data Science: pandas Series and DataFrames--Boolean Indexing 00:03:52
    • Intro to Data Science: pandas Series and DataFrames--Accessing a Specific DataFrame Cell by Row and Column 00:03:37
    • Intro to Data Science: pandas Series and DataFrames--Descriptive Statistics 00:04:09
    • Intro to Data Science: pandas Series and DataFrames--Transposing the DataFrame with the T Attribute 00:02:59
    • Intro to Data Science: pandas Series and DataFrames--Sorting by Indices 00:02:50
    • Intro to Data Science: pandas Series and DataFrames--Sorting by Column Values 00:06:55
    • Self Check 00:03:58
11. Part 3
    • Introduction to Python Fundamentals: Part 3 00:11:52
12. Lesson 08: Strings--A Deeper Look
    • Lesson 08 Overview: Strings--A Deeper Look; Pandas, Regular Expressions and Data Munging 00:02:49
    • Formatting Strings--Presentation Types 00:04:24
    • Self Check 00:00:35
    • Formatting Strings--Field Widths and Alignment 00:04:45
    • Self Check 00:01:06
    • Formatting Strings--Numeric Formatting 00:02:51
    • Self Check 00:01:38
    • Formatting Strings--String's format Method 00:03:35
    • Self Check 00:03:03
    • Concatenating and Repeating Strings 00:01:52
    • Self Check 00:01:38
    • Stripping Whitespace from Strings 00:01:28
    • Self Check 00:01:03
    • Changing Character Case 00:00:47
    • Self Check 00:00:36
    • Comparison Operators for Strings 00:01:52
    • Searching for Substrings 00:05:07
    • Self Check 00:01:17
    • Replacing Substrings 00:00:49
    • Self Check 00:00:40
    • Splitting and Joining Strings 00:06:53
    • Self Check 00:03:50
    • Characters and Character-Testing Methods 00:02:08
    • Raw Strings 00:02:29
    • Introduction to Regular Expressions 00:01:26
    • re Module and Function fullmatch Part 1--Matching Literal Characters 00:02:29
    • re Module and Function fullmatch Part 2--Metacharacters, Character Classes and Quantifiers 00:04:50
    • re Module and Function fullmatch Part 3--Custom Character Classes 00:03:41
    • re Module and Function fullmatch Part 1--Quantifiers 00:04:54
    • Self Check 00:02:17
    • Replacing Substrings and Splitting Strings 00:03:45
    • Self Check 00:02:11
    • Other Search Functions; Accessing Matches--Function search: Finding the First Match Anywhere in a String 00:03:18
    • Other Search Functions; Accessing Matches--Ignoring Case with the Optional flags Keyword Argument 00:01:05
    • Other Search Functions; Accessing Matches--Metacharacters that Restrict Matches to the Beginning or End of a String 00:01:45
    • Other Search Functions; Accessing Matches--Functions findall and finditer: Finding All Matches in a String 00:02:17
    • Other Search Functions; Accessing Matches--Capturing Substrings in a Match 00:04:13
    • Self Check 00:02:19
    • Intro to Data Science: Pandas, Regular Expressions and Data Munging Part 1: Introduction 00:04:20
    • Intro to Data Science: Pandas, Regular Expressions and Data Munging Part 3: Data Validation 00:04:45
    • Intro to Data Science: Pandas, Regular Expressions and Data Munging Part 4: Reformatting Your Data 00:07:18
    • Self Check 00:03:10
13. Lesson 09: Files and Exceptions
    • Lesson 09 Overview: Files and Exceptions; Pandas, CSV Files and Simple Data Analysis 00:04:01
    • Files 00:01:13
    • Text-File Processing--Writing to a Text File: Introducing the with Statement 00:06:04
    • Self Check 00:01:42
    • Text-File Processing--Reading Data from a Text File 00:05:25
    • Self Check 00:02:05
    • Updating Text Files 00:06:24
    • Self Check 00:03:28
    • Serialization with JSON--JSON Data Format 00:02:56
    • Serialization with JSON--Serializing an Object to JSON 00:03:54
    • Serialization with JSON--Deserializing a JSON Object into Python 00:02:02
    • Serialization with JSON--Displaying JSON Text 00:03:34
    • Self Check 00:02:53
    • File Open Modes 00:02:47
    • Handling Exceptions 00:01:24
    • Division by Zero and Invalid Input 00:02:24
    • try Statements 00:06:51
    • Self Check 00:02:04
    • finally Clause 00:07:17
    • Self Check 00:01:54
    • Explicitly Raising an Exception 00:01:33
    • Stack Unwinding and Tracebacks 00:04:16
    • Intro to Data Science: Working with CSV Files--Python Standard Library Module csv 00:07:49
    • Self Check 00:02:10
    • Intro to Data Science: Working with CSV Files--Reading CSV Files into Pandas DataFrames 00:04:39
    • Intro to Data Science: Working with CSV Files--Reading the Titanic Disaster Dataset 00:05:40
    • Intro to Data Science: Working with CSV Files--Simple Data Analysis with the Titanic Disaster Dataset 00:04:14
    • Intro to Data Science: Working with CSV Files--Passenger Age Histogram 00:03:14
14. Part 4
    • Introduction to Python Fundamentals: Part 4 00:11:34
15. Lesson 10: Object-Oriented Programming
    • Lesson 10 Overview: Object-Oriented Programming 00:07:09
    • Custom Class Account--Test-Driving Class Account 00:06:17
    • Custom Class Account--Account Class Definition 00:12:17
    • Self Check 00:04:50
    • Controlling Access to Attributes 00:03:31
    • Properties for Data Access--Test-Driving Class Time 00:06:05
    • Properties for Data Access--Class Time Definition 00:16:58
    • Self Check 00:04:35
    • Properties for Data Access--Class Time Definition Notes 00:02:10
    • Simulating "Private" Attributes 00:05:59
    • Case Study: Card Shuffling and Dealing Simulation--Test Driving Classes Card and DeckOfCards 00:04:23
    • Case Study: Card Shuffling and Dealing Simulation--Class Card and an Introduction to Class Attributes 00:09:30
    • Case Study: Card Shuffling and Dealing Simulation--Class DeckOfCards 00:07:18
    • Case Study: Card Shuffling and Dealing Simulation--Displaying Card Images with Matplotlib 00:14:16
    • Self Check 00:03:33
    • Inheritance: Base Classes and Subclasses 00:05:41
    • Building an Inheritance Hierarchy and Introducing Polymorphism--Base Class CommissionEmployee 00:08:37
    • Building an Inheritance Hierarchy and Introducing Polymorphism--Sublass SalariedCommissionEmployee 00:10:03
    • Building an Inheritance Hierarchy and Introducing Polymorphism--Processing CommissionEmployees and SalariedCommissionEmployees Polymorphically 00:06:01
    • Duck Typing and Polymorphism 00:05:06
    • Operator Overloading 00:03:48
    • Test-Driving Class Complex 00:05:51
    • Class Complex Definition 00:06:21
    • Self Check 00:03:58
    • Named Tuples 00:07:08
    • A Brief Intro to Python 3.7's New Data Classes 00:01:29
    • A Brief Intro to Python 3.7's New Data Classes--Creating a Card Data Class 00:08:53
    • A Brief Intro to Python 3.7's New Data Classes--Using the Card Data Class 00:04:55
    • Self Check 00:02:10
    • A Brief Intro to Python 3.7's New Data Classes--Advantages Over Named Tuples and Traditional Classes 00:04:11
    • Unit Testing with Docstrings and doctest 00:15:42
    • Self Check 00:02:54
    • Namespaces and Scopes 00:12:36
    • Intro to Data Science: Time Series and Simple Linear Regression--Introduction 00:09:16
    • Intro to Data Science: Time Series and Simple Linear Regression--Components of the Simple Linear Regression Calculation 00:04:42
    • Intro to Data Science: Time Series and Simple Linear Regression--Loading the Average High Temperatures into a DataFrame 00:02:51
    • Intro to Data Science: Time Series and Simple Linear Regression--Cleaning the Data 00:03:46
    • Intro to Data Science: Time Series and Simple Linear Regression--Calculating Basic Descriptive Statistics for the Dataset 00:01:27
    • Intro to Data Science: Time Series and Simple Linear Regression--Forecasting Future January Average High Temperatures 00:05:00
    • Intro to Data Science: Time Series and Simple Linear Regression--Plotting the Average High Temperatures and a Regression Line 00:06:01
16. Lesson 11: Natural Language Processing
    • Lesson 11 Overview: Natural Language Processing (NLP) 00:05:56
    • Introduction 00:04:17
    • TextBlob 00:07:50
    • Create a TextBlob 00:03:02
    • Tokenizing Text into Sentences and Words 00:02:02
    • Parts-of-Speech Tagging 00:06:35
    • Extracting Noun Phrases 00:02:41
    • Sentiment Analysis with TextBlob's Default Sentiment Analyzer 00:02:57
    • Sentiment Analysis with the NaiveBayesAnalyzer 00:05:10
    • Language Detection and Translation 00:03:56
    • Inflection: Pluralization and Singularization 00:03:55
    • Spell Checking and Correction 00:03:30
    • Normalization: Stemming and Lemmatization 00:02:01
    • Word Frequencies 00:04:31
    • Getting Definitions, Synonyms and Antonyms from WordNet 00:07:36
    • Deleting Stop Word 00:04:51
    • n-grams 00:04:10
    • Visualizing Word Frequencies with Pandas 00:13:15
    • Visualizing Word Frequencies with Word Clouds 00:09:04
    • Readability Assessment with Textatistic 00:04:38
    • Named Entity Recognition with spaCy 00:07:19
    • Similarity Detection with spaCy 00:09:24
17. Lesson 12: Data Mining Twitter
    • Lesson 12 Overview: Data Mining Twitter 00:03:24
    • Introduction 00:04:16
    • Overview of the Twitter APIs 00:11:54
    • Creating a Twitter Developer Account 00:02:04
    • Getting Twitter Credentials--Creating an App 00:07:14
    • What's in a Tweet? 00:08:57
    • Tweepy 00:02:26
    • Authenticating with Twitter Via Tweepy 00:05:47
    • Getting Information About a Twitter Account 00:10:30
    • Self Check 00:01:12
    • Introduction to Tweepy Cursors: Getting an Account's Followers and Friends 00:02:19
    • Determining an Account's Followers 00:06:52
    • Self Check 00:02:14
    • Determining Whom an Account Follows 00:03:10
    • Getting a User's Recent Tweets 00:02:43
    • Self Check 00:01:00
    • Searching Recent Tweets 00:09:21
    • Self Check 00:01:02
    • Spotting Trends: Twitter Trends API 00:00:59
    • Places with Trending Topics 00:04:19
    • Getting a List of Trending Topics 00:07:58
    • Self Check 00:02:23
    • Create a Word Cloud from Trending Topics 00:05:05
    • Self Check 00:02:24
    • Cleaning/Preprocessing Tweets for Analysis 00:06:44
    • Twitter Streaming API 00:01:41
    • Creating a Subclass of StreamListener 00:12:02
    • Initiating Stream Processing 00:12:58
    • Twitter Restrictions Note 00:01:30
    • Tweet Sentiment Analysis 00:17:34
    • Geocoding and Mapping 00:06:54
    • Getting and Mapping the Tweets 00:22:17
    • Utility Functions in tweetutilities.py and Class LocationListener 00:10:21
18. Lesson 13: IBM Watson and Cognitive Computing; Building a Speech-to-Speech Translator
    • Lesson 13 Overview: IBM Watson and Cognitive Computing; Building a Speech-to-Speech Translator 00:01:46
    • Introduction to Watson 00:05:32
    • IBM Cloud Account and Cloud Console 00:03:29
    • Watson Services: Watson Assistant Demo 00:04:02
    • Watson Services: Visual Recognition 00:05:12
    • Watson Services: Speech to Text 00:03:57
    • Watson Services: Text to Speech 00:02:51
    • Watson Services: Language Translator 00:02:33
    • Watson Services: Natural Language Understanding 00:04:03
    • Watson Services: Personality Insights 00:03:28
    • Additional Services and Tools 00:05:07
    • Watson Developer Cloud Python SDK 00:03:39
    • Case Study: Traveler's Companion Translation App 00:01:42
    • Before You run the App 00:01:15
    • Before You run the App: Registering for the Speech to Text Service 00:04:32
    • Before You run the App: Registering for the Text to Speech Service 00:02:21
    • Before You run the App: Registering for the Language Translator Service 00:01:20
    • Test-Driving the App 00:08:32
    • SimpleLanguageTranslator.py Script Walkthrough 00:01:40
    • SimpleLanguageTranslator.py Script Walkthrough: Importing Watson SDK Classes from the ibm_watson Module 00:02:28
    • SimpleLanguageTranslator.py Script Walkthrough: Other Imported Modules 00:01:31
    • SimpleLanguageTranslator.py Script Walkthrough: Main Program: Function run_translator 00:07:07
    • SimpleLanguageTranslator.py Script Walkthrough: Function speech_to_text 00:08:14
    • SimpleLanguageTranslator.py Script Walkthrough: Function translate 00:04:45
    • SimpleLanguageTranslator.py Script Walkthrough: Function text_to_speech 00:02:34
    • SimpleLanguageTranslator.py Script Walkthrough: Function record_audio 00:06:06
    • SimpleLanguageTranslator.py Script Walkthrough: Function play_audio 00:01:20
    • Watson Resources 00:03:36
19. Lesson 14, Machine Learning: Classification, Regression and Clustering
    • Lesson 14 Overview: Machine Learning: Classification, Regression and Clustering 00:07:07
    • Introduction to Machine Learning 00:16:12
    • Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 1 00:07:40
    • k-Nearest Neighbors Algorithm 00:03:18
    • k-Nearest Neighbors Algorithm: Hyperparameters and Hyperparameter Tuning 00:02:25
    • Loading the Dataset 00:01:48
    • Loading the Dataset: Displaying the Description 00:03:55
    • Loading the Dataset: Checking the Sample and Target Sizes 00:03:27
    • Loading the Dataset: A Sample Digit Image 00:02:20
    • Loading the Dataset: Preparing the Data for Use with Scikit-Learn 00:02:55
    • Visualizing the Data 00:07:06
    • Splitting the Data for Training and Testing 00:07:10
    • Creating the Model 00:02:04
    • Training the Model 00:04:31
    • Predicting Digit Classes 00:04:51
    • Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 2 00:00:48
    • Metrics for Model Accuracy: Estimator Method score 00:01:23
    • Metrics for Model Accuracy: Confusion Matrix 00:06:28
    • Metrics for Model Accuracy: Classification Report 00:04:24
    • Metrics for Model Accuracy: Visualizing the Confusion Matrix 00:05:33
    • K-Fold Cross-Validation 00:07:02
    • Running Multiple Models to Find the Best One 00:07:01
    • Hyperparameter Tuning 00:05:23
    • Case Study: Time Series and Simple Linear Regression 00:03:12
    • Loading the Average High Temperatures into a DataFrame 00:03:48
    • Splitting the Data for Training and Testing 00:05:39
    • Training the Model 00:03:58
    • Testing the Model 00:01:49
    • Predicting Future Temperatures and Estimating Past Temperatures 00:02:13
    • Visualizing the Dataset with the Regression Line 00:05:29
    • Overfitting/Underfitting 00:01:41
    • Case Study: Multiple Linear Regression with the California Housing Dataset 00:01:43
    • Loading the Dataset 00:06:56
    • Exploring the Data with Pandas 00:06:54
    • Visualizing the Features 00:13:28
    • Splitting the Data for Training and Testing 00:01:23
    • Training the Model 00:04:27
    • Testing the Model 00:01:40
    • Visualizing the Expected vs. Predicted Prices 00:06:15
    • Regression Model Metrics 00:03:19
    • Choosing the Best Model 00:06:05
    • Case Study: Unsupervised Machine Learning, Part 1--Dimensionality Reduction 00:05:20
    • Loading the Digits Dataset 00:01:15
    • Creating a TSNE Estimator for Dimensionality Reduction 00:03:19
    • Transforming the Digits Dataset's Features into Two Dimensions 00:02:44
    • Visualizing the Reduced Data 00:05:07
    • Visualizing the Reduced Data with Different Colors for Each Digit 00:05:08
    • Visualizing the Reduced Data in 3D 00:05:08
    • Case Study: Unsupervised Machine Learning, Part 2--k-Means Clustering 00:03:19
    • Loading the Iris Dataset 00:03:09
    • Exploring the Iris Dataset: Descriptive Statistics with Pandas 00:06:05
    • Visualizing the Dataset with a Seaborn pairplot 00:08:53
    • Using a KMeans Estimator 00:05:20
    • Dimensionality Reduction with Principal Component Analysis 00:10:22
    • Choosing the Best Clustering Estimator 00:08:06
20. Lesson 15: Deep Learning
    • Lesson 15 Overview: Deep Learning 00:06:47
    • Introduction 00:07:14
    • Deep Learning Applications 00:03:01
    • Deep Learning Demos 00:02:02
    • Keras Resources 00:01:39
    • Keras Built-In Datasets 00:02:00
    • Custom Anaconda Environments 00:08:16
    • Neural Networks 00:06:44
    • Tensors 00:04:26
    • Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset 00:02:50
    • Reproducibility in Keras and Deep Learning 00:01:18
    • Basic Keras Neural Network 00:03:30
    • Loading the MNIST Dataset 00:07:14
    • Data Exploration 00:01:31
    • Visualizing Digits 00:07:32
    • Reshaping the Image Data 00:05:25
    • Normalizing the Image Data 00:02:44
    • One-Hot Encoding: Converting the Labels From Integers to Categorical Data 00:05:13
    • Creating the Neural Network 00:01:23
    • Adding Layers to the Network 00:02:05
    • Convolution 00:07:42
    • Adding a Conv2D Convolution Layer to Our Model 00:04:51
    • Dimensionality of the First Convolution Layer‚Äôs Output 00:01:50
    • Overfitting 00:03:04
    • Adding a Pooling Layer 00:04:40
    • Adding Another Convolutional Layer and Pooling Layer 00:03:14
    • Flattening the Results to One Dimension with a Keras Flatten Layer 00:01:41
    • Adding a Dense Layer to Reduce the Number of Features 00:02:10
    • Adding Another Dense Layer to Produce the Final Output 00:01:29
    • Printing the Model's Summary 00:04:12
    • Visualizing a Model‚Äôs Structure 00:03:37
    • Compiling the Model 00:03:00
    • Training and Evaluating the Model 00:08:04
    • Evaluating the Model on Unseen Data 00:02:12
    • Making Predictions 00:02:11
    • Locating the Incorrect Predictions 00:03:39
    • Visualizing Incorrect Predictions 00:04:14
    • Displaying the Probabilities for Several Incorrect Predictions 00:04:20
    • Saving and Loading a Model 00:02:25
    • Visualizing Neural Network Training with TensorBoard 00:21:41
    • ConvnetJS: Browser-Based Deep-Learning Training and Visualization 00:04:53
    • Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset 00:05:39
    • Loading the IMDb Movie Reviews Dataset 00:05:36
    • Data Exploration 00:02:47
    • Movie Review Encodings and Decoding a Review 00:10:05
    • Data Preparation 00:05:38
    • Creating the Neural Network 00:00:41
    • Adding an Embedding Layer 00:03:56
    • Adding an LSTM Layer 00:03:23
    • Adding a Dense Output Layer 00:00:43
    • Compiling the Model and Displaying the Summary 00:02:12
    • Training and Evaluating the Model (1 of 2) 00:04:30
    • Training and Evaluating the Model (2 of 2) 00:02:18
    • Tuning Deep Learning Models 00:03:32
21. Lesson 16: Big Data: Hadoop, Spark, NoSQL (MongoDB) and IoT
    • Lesson 16 Overview: Big Data: Hadoop, Spark, NoSQL (MongoDB) and IoT 00:09:49
    • Introduction--Databases 00:03:18
    • Introduction--Apache Hadoop and Apache Spark 00:03:35
    • Introduction--Internet of Things 00:01:37
    • Introduction--Experience Cloud and Desktop Big-Data Software 00:03:07
    • Introduction--Big Data Sources 00:01:10
    • Relational Databases and Structured Query Language (SQL) 00:03:28
    • A books Database 00:12:27
    • SELECT Queries 00:01:17
    • WHERE Clause 00:03:02
    • ORDER BY Clause 00:02:26
    • Merging Data from Multiple Tables: INNER JOIN 00:01:42
    • INSERT INTO Statement 00:02:21
    • UPDATE Statement 00:01:20
    • DELETE FROM Statement 00:02:03
    • NoSQL and NewSQL Big-Data Databases: A Brief Tour 00:03:49
    • NoSQL Key-Value Databases 00:01:26
    • NoSQL Document Databases 00:01:34
    • NoSQL Columnar Databases 00:02:33
    • NoSQL Graph Databases 00:02:05
    • NewSQL Databases 00:03:46
    • Case Study: A MongoDB JSON Document Database 00:03:22
    • Creating the MongoDB Atlas Cluster 00:08:36
    • Streaming Tweets into MongoDB 00:24:08
    • Hadoop 00:00:51
    • Hadoop Overview 00:06:48
    • Summarizing Word Lengths in Romeo and Juliet via MapReduce 00:02:31
    • Creating an Apache Hadoop Cluster in Microsoft Azure HDInsight: Part 1 00:03:46
    • Creating an Apache Hadoop Cluster in Microsoft Azure HDInsight: Part 2 00:10:07
    • Hadoop Streaming 00:02:40
    • Implementing the Mapper 00:05:06
    • Implementing the Reducer 00:03:19
    • Preparing to Run the MapReduce Example 00:06:47
    • Running the MapReduce Job 00:10:56
    • Spark Overview 00:05:59
    • Docker and the Jupyter Docker Stacks 00:14:15
    • Word Count with Spark 00:17:00
    • Spark Word Count on Microsoft Azure 00:18:22
    • Spark Streaming: Counting Twitter Hashtags Using the pysparknotebook Docker Stack 00:05:28
    • Streaming Tweets to a Socket 00:11:16
    • Summarizing Tweet Hashtags; Introducing Spark SQL 00:20:48
    • Internet of Things and Dashboards 00:01:04
    • Publish and Subscribe 00:00:46
    • Visualizing a PubNub Sample Live Stream with a Freeboard Dashboard 00:11:10
    • Simulating an Internet-Connected Thermostat in Python and Creating a Dashbboard in Freeboard.io 00:14:21
    • Creating a Python PubNub Subscriber 00:11:05
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