The Epic Python Developer Certification Bundle

Your First Program

Introduction to the Data Science in Python Bootcamp

• What is Data Science? - 3:37
• Introduction to the Course & Instructor - 11:34
• Data and Scripts for the Course
• Introduction to the Python Data Science Tool - 10:57
• For Mac Users - 4:05
• Introduction to the Python Data Science Environment - 19:15
• Some Miscellaneous IPython Usage Facts - 5:25
• Online iPython Interpreter - 3:26
• Conclusion to Section 1 - 2:36

Introduction to Python Pre-Requisites for Data Science

• Different Types of Data Used in Statistical & ML Analysis - 3:37
• Different Types of Data Used Programatically - 3:46
• Python Data Science Packages To Be Used - 3:16
• Conclusion to Section 2 - 1:59

Introduction to Numpy

• Numpy: Introduction - 3:46
• Create Numpy Arrays - 10:51
• Numpy Operations - 16:48
• Matrix Arithmetic and Linear Systems - 7:34
• Numpy for Basic Vector Arithmetic - 6:16
• Numpy for Basic Matrix Arithmetic - 5:16
• Broadcasting for Numpy - 3:52
• Solve for Equations - 5:04
• Numpy For Statistics - 7:23
• Conclusions to Section 3 - 2:24

Introduction to Pandas

• What are Pandas? - 12:06
• Read CSV Data in Python - 5:42
• Read in Excel File - 5:31
• Read HTML Data - 12:06
• Read JSON Data - 3:09
• Conclusions to Section 4 - 2:06

Data Pre-Processing/Wrangling

• Rationale behind this section - 4:19
• Remove NA Values - 10:28
• Basic Data Handling: Starting with Conditional Data Selection - 5:24
• Drop Column/Row - 4:42
• Subset and Index Data - 9:44
• Basic Data Grouping Based on Qualitative Attributes - 9:47
• Crosstabulation - 4:54
• Reshaping - 9:26
• Pivotting - 8:30
• Rank and Sort Data - 8:03
• Concatenate - 8:16
• Merge - 10:47
• Conclusion to Section 5

Introduction to Data Visualization

• What is Data Visualisation? - 9:33
• Theory Behind Data Visualisation - 6:46
• Histograms- Visualise the Distribution of Quantitative Variables - 12:13
• Boxplot- Visualise the Data Summary - 5:54
• Scatterplot- Visualise The Relationship Between Quantitative Variables - 11:57
• Line Chart - 12:31
• Barplot - 22:25
• Pie Chart - 5:29
• Conclusion to Section 6 - 2:14

Basic Statistical Data Analysis

• What is Statistical Data Analysis? - 10:08
• Some Pointers on Collecting Data for Statistical Studies - 8:38
• Explore the Quantitative Data: Descriptive Statistics - 9:05
• Group By Qualitative Categories - 10:25
• Visualize Descriptive Statistics-Boxplots - 5:28
• Common Terms Relating to Descriptive Statistics - 5:15
• Data Distribution- Normal Distribution - 4:07
• Check for Normal Distribution - 6:23
• Standard Normal Distribution and Z-scores - 4:10
• Confidence Interval-Theory - 6:06
• Confidence Interval-Calculation - 5:20
• Conclusion to Section 7 - 1:28

Statistical Inference & Relationship Between Variables

• What is Hypothesis Testing? - 5:42
• Test the Difference Between Two Groups - 7:30
• Test the Difference Between More Than Two Groups - 10:55
• Explore the Relationship Between Two Quantitative Variables - 4:26
• Correlation Analysis - 8:26
• Linear Regression-Theory - 10:44
• Linear Regression-Implementation in Python - 11:18
• Conditions of Linear Regression-Check in Python - 12:03
• Polynomial Regression - 3:53
• GLM: Generalized Linear Model - 5:25
• Logistic Regression - 11:10
• Conclusion to Section 8 - 1:52

Machine Learning for Data Science

• How is Machine Learning Different from Statistical Data Analysis? - 11:12
• What is Machine Learning (ML) About? Some Theoretical Pointers - 5:32

Unsupervised Learning

• Some Basic Pointers - 1:38
• kmeans-theory - 2:31
• KMeans-implementation on the iris data - 8:01
• Quantifying KMeans Clustering Performance - 3:53
• kmeans clustering on real data - 4:16
• How Do We Select the Number of Clusters? - 5:38
• Theory of hierarchical clustering - 4:10
• Implement hierarchical clustering - 9:19
• Theory of Principal Component Analysis (PCA) - 2:37
• Implement PCA - 3:52
• Conclusion to Section 10 - 2:08
• Data Preparation for Supervised Classification - 9:47
• Classification accuracy evaluation - 9:42
• Random Forest (RF) For Regression - 9:20

Supervised Learning

• What is this section about? - 10:10
• Logistic regression with classification - 8:26
• Random Forest (RF) For Classification - 12:02
• Linear Support Vector Machine (SVM) Classification - 3:10
• Non-Linear Support Vector Machine (SVM) Classification - 2:06
• Support Vector Regression - 4:30
• kNN Classification - 7:46
• kNN Regression - 3:48
• Gradient Boosting Machine (GBM) Classification - 5:54
• GBM Classification
• Gradient Boosting Regression (GBR) - 4:46
• Voting Classifier - 4:00
• Conclusion to Section 11 - 2:46

Artificial Neural Networks (ANN) and Deep Learning

• Introduction
• Perceptrons for Binary Classification - 4:27
• Getting Started with ANN-binary classification - 3:26
• Multi-label classification with MLP - 4:53
• Regression with MLP - 3:48
• MLP with PCA on a Large Dataset - 7:33
• Start With Deep Neural Network (DNN)
• Start with H20 - 4:14
• Default H2O Deep Learning Algorithm - 3:20
• Specify the Activation Function - 2:06
• Deep Learning Predictions - 5:02
• Conclusion to section 12 - 2:03

• Introduction - 4:22

• Code and Data
• 1 - KMeans intuition - 12:18
• 2 - Choosing the right number of clusters - 15:35
• 3 - KMeans in Python (Part 1 - 18:35
• 4 - KMeans in Python (Part 2) - 9:41
• 5 - KMeans Limitations - (Part 1-Clusters with different sizes) - 10:30
• 6 - KMeans Limitations - (Part-2-Clusters with non spherical shapes) - 10:37
• 7 - KMeans Limitations - (Part 3-Clusters with varying densities) - 5:22

• Code and Data
• 1 - Intuition of Mean Shift - 9:23
• 2 - Mean Shift in Python - 9:23
• 3 - Mean Shift Performance in Cases where Kmean Fails (Part 1) - 8:51
• 4 - Mean Shift Performance in Cases where Kmean Fails (Part 2) - 11:34

• Code and Data
• 1 - Intuition of DBSCAN - 9:21
• 2 - DBSCAN in python - 12:47
• 3 - DBSCAN on clusters with varying sizes - 6:29
• 4 - DBSCAN on clusters with different shapes and densities - 11:27
• 5 - DBSCAN for handling noise - 8:00
• 6 - Practical Activity

• Code and Data
• 1 - Hierarchical Clustering Intuition (Part 1) - 9:50
• 2 - Hierarchical Clustering Intuition (Part 2) - 15:47
• Hierachical Clustering in python - 11:27

• Code and Data
• 1 - HDBSCAN Intuition - 18:53
• 2 - HDBSCAN in Python - 9:41
• 3 - HDBSCAN clustering on different sizes, shapes and densities - 6:58
• 4 - HDBSCAN for handling noise - 13:31

• Code and Data
• 1 - Image Compression (Part 1) - 11:37
• 2 - Image Compression (Part 2) - 10:55
• 3 - Clustering Sentences (Part 1) - 11:23
• 4 - Clustering sentences (Part 2) - 8:48

• Let's Connect!
• How to Install Python 3 on Windows - 2:57
• How to Install Python 3 on macOS - 2:35
• UPDATE! Python Versions
• Installing Python 3 on Windows, Linux and MacOS
• The Python Interpreter & IDLE in Windows - 3:19
• The Python Interpreter & IDLE in macOS - 2:45
• Python 3 Basics - Scripts in Windows - 3:58
• UPDATE! Saving a Python script in Notepad++
• Python 3 Basics - Scripts in macOS - 4:21
• Python 3 - Reasons for the 'No such file or directory' error (or similar) - 5:07
• Python 3 Basics - User Input - 3:57
• Notebook - User Input
• Python 3 Basics - Variables - 6:19
• Notebook - Variables
• Python 3 Basics - Keywords
• Python 3 - Data Types - 1:51

• Why learn about each of Python's data types? - 7:38
• Python 3 Strings - Introduction - 6:57
• Python 3 Strings - Methods - 8:54
• Python 3 Strings - Operators & Formatting - 7:23
• Python 3 Strings - Formatting Using F-strings - 2:25
• Python 3 Strings - Slices - 7:42
• Python 3 Strings - Slicing Using a Step - 4:08
• Notebook - Strings

• Python 3 Numbers - Math Operators - 6:15
• Notebook - Numbers and Math Operators
• Python 3 Booleans - Logical Operators - 5:58
• Notebook - Booleans and Logical Operators

• Python 3 Lists - Introduction - 3:42
• Python 3 Lists - Methods - 8:27
• Python 3 Lists - Slices - 5:40
• Notebook - Lists

• Python 3 Sets - Introduction - 3:21
• Python 3 Sets - Methods - 2:51
• Python 3 Sets - Frozensets - 3:03
• Notebook - Sets and Frozensets

• Python 3 Tuples - Introduction - 4:48
• Python 3 Tuples - Tuples vs. Lists - 2:50
• Python 3 Tuples - Methods - 3:25
• Notebook - Tuples

• Python 3 Ranges - Introduction - 4:06
• Python 3 Ranges - Methods - 2:40
• Notebook - Ranges

• Python 3 Dictionaries - Introduction - 3:11
• Python 3 Dictionaries - Methods - 6:25
• Python 3 Dictionaries - Updates in v3.6 and v3.7 - 2:09
• Python 3 - Conversions Between Data Types - 6:51
• Notebook - Dictionaries and Conversions Between Data Types

• Python 3 Conditionals - If / Elif / Else - 15:20
• Notebook - If / Elif / Else Conditionals
• Python 3 Loops - For / For-Else - 8:42
• Notebook - For / For-Else Loops
• Python 3 Loops - While / While-Else - 6:05
• Notebook - While / While-Else Loops
• Python 3 Nesting - If / For / While - 10:10
• Notebook - Nesting
• Python 3 - Break / Continue / Pass - 7:40
• Notebook - Break / Continue / Pass
• Python 3 - Exceptions - 2:27
• Python 3 - Try / Except / Else / Finally - 9:42
• Notebook - Try / Except / Else / Finally

• Python 3 - Fixing Syntax Errors - 5:24
• Python 3 - Fixing Exceptions - 8:45

• Python 3 Functions - Basics - 9:51
• Python 3 Functions - Arguments - 8:03
• Notebook - Functions - Basics
• Python 3 Functions - Namespaces - 10:48
• Python 3 Modules - Importing - 11:30
• Python 3 Modules - Helpful Functions: dir() and help() - 2:20
• Notebook - Modules and Importing
• Python 3 Modules - Installing a Non-Default Module in Windows - 3:54
• Python 3 Modules - Installing a Non-Default Module in macOS

• Python 3 Files - Opening & Reading - 12:10
• Python 3 Files - Quick Note for Windows Users - 2:48
• Python 3 Files - Additional Way of Avoiding the Unicode Error - 1:38
• Python 3 Files - Writing & Appending - 7:46
• Python 3 Files - Closing. The "with" Method - 2:28
• Python 3 Files - Deleting File Contents - 4:43
• Python 3 Files - Access Modes Summary
• Notebook - File Operations

• Python 3 Regex - match() & search() - 16:24
• Python 3 Regex - findall() & sub() - 6:16
• Python 3 Regex - Regular Expressions Summary
• Notebook - Regular Expressions
• Bonus Video: Special Sequences - 6:01
• Bonus Video: Sets of Characters - 5:07
• Bonus Video: OR in Regular Expressions - 3:48
• Bonus Video: split() & subn() - 3:33
• Bonus Video: Additional Regex Syntax Elements - 4:45
• Bonus Video: AttributeError: 'NoneType' object has no attribute - 3:34

• Python 3 Classes - Objects - 11:45
• Python 3 Classes - Inheritance - 6:19
• Notebook - Classes and Objects

• Python 3 - List / Set / Dictionary Comprehensions - 4:53
• Notebook - List / Set / Dictionary Comprehensions
• Python 3 - Lambda Functions - 4:40
• Notebook - Lambda Functions
• Python 3 - map() and filter() - 2:29
• Notebook - map() and filter()
• Python 3 - Iterators and Generators - 6:48
• Notebook - Iterators and Generators
• Python 3 - Itertools - 5:43
• Notebook - Itertools
• Python 3 - Decorators - 2:37
• Notebook - Decorators
• Python 3 - Threading Basics - 5:36
• Notebook - Threading Basics
• Python 3 - Coding Best Practices - 2:36

• Download the Python 3 Cheat Sheet

• Download the Python 3 E-Book

• Planning the Application - 3:01
• Designing and Building the User Menu - 3:08
• Implementing Addition, Subtraction, Multiplication, Division - 6:10
• Implementing Modulo, Raising to a Power, Square Root, Logarithm - 3:43
• Implementing Trigonometric Functions: sin, cos, tan - 3:37
• Testing Each Function of the Application - 3:50
• Download the Code - Interactive Scientific Calculator
• Creating Executable Files (.exe) from Python Scripts (.py) - 3:59

• Setting Up the Working Environment - 2:35
• Loading an Excel Workbook In Python and Creating/Removing Sheets - 5:28
• Notebook - Handling Workbooks
• Getting General Information About a Sheet - 3:58
• Notebook - Sheet Information
• Working with Sheet Cells Using Python - 4:01
• Notebook - Cell Information
• Working with Cell Styles Using Python - 9:03
• UPDATE! Change in cell.column in recent versions of openpyxl
• Notebook - Cell Styles
• Download the Excel-Python Cheat Sheet
• APPLICATION - Migrating Records from a Text File to an Excel Workbook - 18:12
• Download the Code - Excel Application

• Installing the Database Server Software - 2:45
• UPDATE! Downloading and Installing PostgreSQL
• Installing the Necessary Python Module - 1:32
• Creating a New Database, Schema and User - 5:26
• UPDATE! Change in database connection via PSQL
• Notebook - Creating a New Database, Schema and User
• Connecting Python to the Database - 2:45
• Notebook - Connecting Python to the Database
• Creating Database Tables with Python - 4:26
• UPDATE! Handling the InFailedSqlTransaction exception
• Notebook - Creating Database Tables with Python
• Inserting Records Into a Table with Python - 3:18
• Notebook - Inserting Records Into a Table with Python
• Updating Records Into a Table with Python - 2:42
• Notebook - Updating Records Into a Table with Python
• Deleting Records From a Table with Python - 1:54
• Notebook - Deleting Records From a Table with Python
• Querying the Database with Python - 5:15
• Notebook - Querying the Database with Python
• Fetching Information From the Database with Python - 4:04
• Notebook - Fetching Information From the Database with Python
• Committing and Rolling Back Transactions with Python - 3:38
• Notebook - Committing and Rolling Back Transactions
• Download the PostgreSQL Syntax Cheat Sheet
• Download the PostgreSQL-Python Cheat Sheet
• APPLICATION - Migrating Records from a Text File to the Database - 9:14
• Download the Code - Database Application

• Network Setup Overview - 1:27
• Installing the Virtualization Software - 1:36
• Installing the Virtualization Software on Windows, Linux, MacOS
• Downloading & Installing the Network Device VM - 2:16
• Note about Arista vEOS versions
• Signing Up to the Arista Software Download Portal
• Importing the VM & Tweaking the VM Settings - 3:08
• UPDATE! vEOS First Boot and the ZeroTouch Feature
• Connecting the Local PC to the Devices in Windows - 4:52
• Connecting the Local PC to the Devices in macOS
• Necessary Switch/Router Configuration
• Checking the SSH Configuration and Testing the Connectivity - 3:03
• UPDATE! Putty asking for Host Key / Password
• Any Connection Issues? Check Out This Troubleshooting Checklist!
• Planning the Application - 5:46
• Checking IP File Validity - 4:09
• Notebook - Checking IP File Validity
• Checking IP Address Validity - 12:51
• Notebook - Checking IP Address Validity
• Checking IP Address Reachability - 3:57
• Notebook - Checking IP Address Reachability
• Note about pinging in Windows vs. Mac OS / Linux
• Checking Username/Password File Validity - 1:45
• Notebook - Checking Username/Password File Validity
• Checking Command File Validity - 1:08
• Notebook - Checking Command File Validity
• Establishing the SSH Connection - 13:13
• Notebook - Establishing the SSH Connection
• Enabling Simultaneous SSH Connections - 2:12
• Notebook - Enabling Simultaneous SSH Connections
• Putting Everything Together - 2:56
• Download the Code - Network Application and Modules
• Reading Device Configuration - 9:19
• Extracting Network Parameters - 12:13
• Configuring Multiple Devices Simultaneously - 2:58

• Running Python Code - The Next Level: IPython and Jupyter Notebook - 9:08
• Notebook - IPython and Jupyter Notebook
• Introduction to Pandas - Basic Operations - 9:14
• Notebook - Introduction to Pandas
• Handling Files with Pandas - TXT, CSV, JSON, XLSX - 17:27
• Notebook - Handling TXT, CSV, JSON, XLSX Files with Pandas
• Reading HTML Content from URLs and HTML Files with Pandas - 4:52
• Notebook - Reading HTML Content with Pandas
• Indexing and Slicing Tables with Pandas - 21:45
• Notebook - Indexing and Slicing Tables with Pandas
• Adding, Updating, Deleting Table Rows and Columns - 14:22
• Notebook - Adding, Updating, Deleting Table Rows and Columns
• APPLICATION - Reading and Writing Data in PostgreSQL Databases Using Pandas - 18:18
• UPDATE! Preparing for testing the application
• Download the Code - SQL Data Analysis Application

• Introduction to Bokeh - 3:38
• Bookmark These 3 Important Documentation Links
• Creating a Basic Line Plot Based on Python Lists - 11:17
• UPDATE! BokehDeprecationWarning: 'legend' keyword is deprecated
• Notebook - Creating a Basic Line Plot Based on Python Lists
• Creating a Bar Plot Based on Excel Data - 17:42
• Notebook - Creating a Bar Plot Based on Excel Data
• Creating a Pie Chart Based on CSV Data - 11:28
• UPDATE! Using 'legend_field' instead of 'legend'
• Notebook - Creating a Pie Chart Based on CSV Data
• Plotting Multiple Stock Prices Simultaneously - 9:04
• Notebook - Plotting Multiple Stock Prices Simultaneously
• Plotting Bitcoin Prices as an Interactive Plot with a Range Tool - 12:55
• UPDATE! Code change according to a new website structure
• Notebook - Plotting Bitcoin Prices as an Interactive Plot with a Range Tool
• Plotting Bitcoin Prices as an Interactive Plot with Candlesticks - 9:49
• Notebook - Plotting Bitcoin Prices as an Interactive Plot with Candlesticks

• Installing pytest and Writing Your First Test - 12:30
• Notebook - Introduction to pytest
• Running Multiple Tests. Test Discovery Rules in Action - 6:08
• Notebook - Running Multiple Tests
• Testing a Basic Script - Preparing the Test Bed - 9:33
• Download the Code for Testing
• Fixture Functions - 4:26
• Notebook - Fixture Functions
• Sharing a Fixture Instance & Fixture Finalization - 9:17
• Notebook - Sharing a Fixture Instance & Fixture Finalization
• Parametrizing Fixtures - 4:36
• Notebook - Parametrizing Fixtures
• Marking Test Functions Using Attributes - 6:22
• Notebook - Marking Test Functions Using Attributes
• Marking Test Functions Using Custom Markers - 4:23
• Notebook - Marking Test Functions Using Custom Markers

• Installing the Necessary Modules - 1:47
• Notebook - Installing the Necessary Modules
• Extracting and Parsing Web Content - 4:34
• Notebook - Extracting and Parsing Web Content
• Tags, Names and Attributes - 10:07
• Notebook - Tags, Names and Attributes
• Searching the Tree of HTML Tags: find() and find_all() - 6:09
• Notebook - Searching the Tree of HTML Tags: find() and find_all()
• APPLICATION - Extracting the Product Names, Links and Prices. Saving to Excel - 12:44
• Download the Code - Scraping Web Data and Saving to Excel
• APPLICATION - Handling Website Pagination When Extracting Data - 6:24
• Download the Code - Handling Website Pagination When Extracting Data

• Putting Your Skills to Work - Part 1 - 8:46
• Putting Your Skills to Work - Part 2 - 8:45
• Download the Presentation

• Follow My Work and Join My LinkedIn Group

• Let's Connect!
• How to Install Python 3 in Windows - 2:57
• How to Install Python 3 in macOS - 2:35
• UPDATE! Python Versions
• Installing Python 3 on Windows, Linux and MacOS
• The Python Interpreter & IDLE in Windows - 3:19
• The Python Interpreter & IDLE in macOS - 2:35
• Python 3 Basics - Scripts in Windows - 3:58
• UPDATE! Saving a Python script in Notepad++
• Python 3 Basics - Scripts in macOS - 4:21
• Python 3 - Reasons for the 'No such file or directory' error (or similar) - 5:07
• Python 3 Basics - User Input - 3:57
• Notebook - User Input
• Python 3 Basics - Variables - 6:19
• Notebook - Variables
• Python 3 Basics - Keywords
• Python 3 - Data Types - 1:51

• Why learn all the Python topics below before building the network apps? - 3:04
• Python 3 Strings - Introduction - 6:57
• Python 3 Strings - Methods - 8:54
• Python 3 Strings - Operators & Formatting - 7:23
• Python 3 Strings - Formatting Using F-strings - 2:25
• Python 3 Strings - Slices - 7:42
• Python 3 Strings - Slicing Using a Step - 4:08
• Notebook - Strings

• Python 3 Numbers - Math Operators - 6:15
• Notebook - Numbers and Math Operators
• Python 3 Booleans - Logical Operators - 5:58
• Notebook - Booleans and Logical Operators

• Python 3 Lists - Introduction - 3:42
• Python 3 Lists - Methods - 8:27
• Python 3 Lists - Slices - 5:40
• Notebook - Lists

• Python 3 Sets - Introduction - 3:21
• Python 3 Sets - Methods - 2:51
• Python 3 Sets - Frozensets - 3:03
• Notebook - Sets and Frozensets

• Python 3 Tuples - Introduction - 4:48
• Python 3 Tuples - Tuples vs. Lists - 2:50
• Python 3 Tuples - Methods - 3:25
• Notebook - Tuples

• Python 3 Ranges - Introduction - 4:06
• Python 3 Ranges - Methods - 2:40
• Notebook - Ranges

• Python 3 Dictionaries - Introduction - 3:11
• Python 3 Dictionaries - Methods - 6:25
• Python 3 - Conversions Between Data Types - 6:51
• Notebook - Dictionaries and Conversions Between Data Types

• Python 3 Conditionals - If / Elif / Else - 15:20
• Notebook - If / Elif / Else Conditionals
• Python 3 Loops - For / For-Else - 8:42
• Notebook - For / For-Else Loops
• Python 3 Loops - While / While-Else - 6:05
• Notebook - While / While-Else Loops
• Python 3 Nesting - If / For / While - 10:10
• Notebook - Nesting
• Python 3 - Break / Continue / Pass - 7:40
• Notebook - Break / Continue / Pass
• Python 3 - Exceptions - 2:27
• Python 3 - Try / Except / Else / Finally - 9:42
• Notebook - Try / Except / Else / Finally

• Python 3 Functions - Basics - 9:51
• Python 3 Functions - Arguments - 8:03
• Notebook - Functions - Basics
• Python 3 Functions - Namespaces - 10:48
• Python 3 Modules - Importing - 11:30
• Python 3 Modules - Helpful Functions: dir() and help() - 2:20
• Notebook - Modules and Importing
• Python 3 Modules - Installing a Non-Default Module in Windows - 3:54
• Python 3 Modules - Installing a Non-Default Module in macOS

• Python 3 Files - Opening & Reading - 12:10
• Python 3 Files - Quick Note for Windows Users - 2:48
• Python 3 Files - Additional Way of Avoiding the Unicode Error - 1:38
• Python 3 Files - Writing & Appending - 7:46
• Python 3 Files - Closing. The "with" Method - 2:28
• Python 3 Files - Access Modes Summary
• Notebook - File Operations

• Python 3 Regex - match() & search() - 16:24
• Python 3 Regex - findall() & sub() - 6:16
• Python 3 Regex - Regular Expressions Summary
• Notebook - Regular Expressions
• Bonus Video: Special Sequences - 6:01
• Bonus Video: Sets of Characters - 5:07
• Bonus Video: OR in Regular Expressions - 3:48
• Bonus Video: split() & subn() - 3:33
• Bonus Video: Additional Regex Syntax Elements - 4:45
• Bonus Video: AttributeError: 'NoneType' object has no attribute - 3:34

• Python 3 Classes - Objects - 11:45
• Python 3 Classes - Inheritance - 6:19
• Notebook - Classes and Objects

• Python 3 - List / Set / Dictionary Comprehensions - 4:53
• Notebook - List / Set / Dictionary Comprehensions
• Python 3 - Lambda Functions - 4:40
• Notebook - Lambda Functions
• Python 3 - map() and filter() - 2:29
• Notebook - map() and filter()
• Python 3 - Iterators and Generators - 6:48
• Notebook - Iterators and Generators
• Python 3 - Itertools - 5:43
• Notebook - Itertools
• Python 3 - Decorators - 2:37
• Notebook - Decorators
• Python 3 - Threading Basics - 5:36
• Notebook - Threading Basics
• Python 3 - Coding Best Practices - 2:36

• Download the Python 3 Cheat Sheet

• Download the Python 3 E-Book

• Network Setup Overview - 1:27
• Installing the Virtualization Software - 1:36
• Installing the Virtualization Software on Windows, Linux, MacOS
• Downloading & Installing the Network Device VM - 2:16
• Note about Arista vEOS versions
• Signing Up to the Arista Software Download Portal
• Importing the VM & Tweaking the VM Settings - 3:08
• UPDATE! vEOS First Boot and the ZeroTouch Feature
• Connecting the Local PC to the Devices in Windows - 4:52
• Connecting the Local PC to the Devices in macOS
• Necessary Switch/Router Configuration
• Checking the SSH Configuration and Testing the Connectivity - 3:03
• UPDATE! Putty asking for Host Key / Password
• Any Connection Issues? Check Out This Troubleshooting Checklist!

• Planning the Application - 5:46
• Checking IP File Validity - 4:09
• Logical Flow Diagram
• Notebook - Checking IP File Validity
• Checking IP Address Validity - 12:51
• Notebook - Checking IP Address Validity
• Checking IP Address Reachability - 3:57
• Notebook - Checking IP Address Reachability
• Note about pinging in Windows vs. Mac OS / Linux
• Checking Username/Password File Validity - 1:45
• Notebook - Checking Username/Password File Validity
• Checking Command File Validity - 1:08
• Notebook - Checking Command File Validity
• Establishing the SSH Connection - 13:13
• Notebook - Establishing the SSH Connection
• Enabling Simultaneous SSH Connections - 2:12
• Notebook - Enabling Simultaneous SSH Connections
• Putting Everything Together - 2:56
• Download the Code - Network Application and Modules
• Reading Device Configuration - 9:19
• Extracting Network Parameters - 12:13
• Configuring Multiple Devices Simultaneously - 2:58

• What Are We Going to Build? - 2:44
• Planning the Application - 2:11
• Logical Flow Diagram
• Checking IP Address and Subnet Mask Validity - 5:08
• Notebook - Checking IP Address and Subnet Mask Validity
• Converting to Binary. Calculate Hosts per Subnet. Wildcard Masks - 12:34
• Notebook - Converting to Binary. Calculate Hosts per Subnet. Wildcard Masks
• Converting to Binary. Find the Network and Broadcast Addresses - 10:50
• Notebook - Converting to Binary. Find the Network and Broadcast Addresses
• Random IP Address Generation Algorithm - 8:23
• Notebook - Random IP Address Generation Algorithm
• Testing the Application - 4:11
• Download the Full Application Code

• Planning the Application - 2:12
• Logical Flow Diagram
• Connecting to the Network Device via SSH - 6:17
• Notebook - Configuring the Arista Switch for SSH Connectivity
• Extracting the CPU Utilization Value and Saving It to a Text File - 7:51
• Notebook - Extracting the CPU Utilization Value and Saving It to a Text File
• Polling the Switch Every 10 Seconds - 2:51
• Notebook - Polling the Switch Every 10 Seconds
• Matplotlib - Building the Switch CPU Utilization Graph - 5:57
• Notebook - Matplotlib - Building the Switch CPU Utilization Graph
• Testing the Application - 4:43
• Download the Full Code - Network Application, Graph Building Script and Modules

• Planning the Application - 3:13
• Logical Flow Diagram
• Setting Up a Linux VM in VirtualBox - 4:46
• Notebook - Downloading the Linux VM
• Configuring the Linux VM - 9:42
• Notebook - Configuring the Linux VM - Make Sure You Follow These Steps
• Meeting Scapy - 10:08
• Notebook - Meeting Scapy
• Importing the Necessary Modules - 3:26
• Notebook - Importing the Necessary Modules
• Asking the User for Input: Interface, Number of Packets, Interval, Protocol - 6:32
• Notebook - Asking the User for Input
• Extracting Parameters from Packets and Writing to a Log File - 4:49
• Notebook - Extracting Parameters from Packets and Writing to a Log File
• Testing the Application - Running the Sniffer and Filtering Packets by Protocol - 7:20
• Notebook - Running the Sniffer
• Download the Full Application Code

• Planning the Application - 3:22
• Logical Flow Diagram
• Importing the Modules and Defining the Necessary Parameters - 3:48
• Notebook - Installing Necessary Modules and Official Documentation
• Notebook - Importing the Modules and Defining the Necessary Parameters
• Connecting to the Arista Switch via SSH using Netmiko - 1:51
• Notebook - Connecting to the Arista Switch via SSH Using Netmiko
• Handling the Configuration Files and Extracting the Configuration Changes - 6:03
• Notebook - Extracting the Configuration Changes
• Sending E-mails to the Network Admin with Device Configuration Changes - 3:43
• Notebook - Sending E-mails to the Network Admin
• Creating a Schedule for Sending E-mails on a Daily Basis in Linux - 5:15
• Notebook - Creating a Schedule for Sending E-mails
• Testing the Application - 5:15
• Download the Full Application Code

• Running CLI Commands via a Remote Server - 7:20
• Running a Local Python Script via a Remote Server - 2:37
• Running a Remote Python Script via a Remote Server - 2:25
• Notebook - Running Python Code via Remote Servers

• Follow My Work and Join My LinkedIn Group

• Welcome - 4:57
• Resources and Materials - 5:56

• Installing Python and PyCharm - 4:33

• Create a Hello World App - 7:17

• History and Characteristics of Python - 9:57

• Foundation : Variables - 3:06
• Variables - 8:42
• Comments - 3:11

• Using the print() function in Python - 12:11
• Challenge : Print( ) statements - 2:36
• Solution : Print( ) statements - 4:52

• Foundation : Introduction to Data Types - 1:45
• Data Types - 10:19
• Casting and Conversion - 6:20
• Foundation : Working with Strings - 1:48
• Working with Strings - Part 1 - 13:30
• Working with Strings - Part 2 - 12:56
• Challenge : Strings - 1:47
• Solution : Strings - 8:45

• Foundation : Operators - 6:47
• Working with Operators - 8:34
• Challenge : Operators - 0:48
• Solution : Operators - 1:59

• Foundation : Introduction to Python Data Structures - 6:10
• Foundation : Lists - 2:16
• Working with Lists - Part 1 - 10:38
• Working with Lists - Part 2 - 12:34
• Challenge : Lists - 1:17
• Solution : Lists - 7:02
• Foundation : Sets - 2:32
• Working with Sets - Part 1 - 7:35
• Working with Sets - Part 2 - 7:52
• Challenge : Sets - 1:23
• Solution : Sets - 4:18
• Foundation : Tuples - 1:47
• Working with Tuples - 12:47
• Challenge : Tuples - 0:53
• Solution : Tuples - 3:47
• Foundation : Dictionaries - 2:31
• Working with Dictionaries - Part 1 - 8:52
• Working with Dictionaries - Part 2 - 5:38
• Challenge : Dictionaries - 0:53
• Solution : Dictionaries - 4:43

• Foundation : Conditionals and Looping - 7:16
• If statements - Part 1 - 10:53
• If statements - Part 2 - 9:30
• While statements - 11:12
• For statements - 8:38
• Challenge : Conditionals and Looping - 1:24
• Solution 1 : Conditionals and Looping - 3:46
• Solution 2 : Conditionals and Looping - 4:27

• Foundation : Functions - 3:52
• Functions - Part 1 - 9:10
• Functions - Part 2 - 8:52
• Functions - Parameters passing by reference - 7:36
• Recursive Functions - 7:21
• Challenge : Functions - 1:27
• Solution : Functions - 8:47

• Foundation : Object Oriented Programming in Python - 5:48
• Foundation : Inheritance - 5:53
• Foundation : Encapsulation - 4:28
• Foundation : Abstraction - 2:10
• Foundation : Polymorphism - 3:34
• Defining classes, constructors and methods - 9:07
• Inheritance and Private / Public properties - 11:23
• Private Attributes or Properties - 7:22
• intro-challenge-object-oriented - 2:02
• Solution : Temperature Class - Object-Oriented Programming - 8:51
• Solution : Circle Class - Object-Oriented Programming - 11:56

• Foundation : Exception Handling in Python with try and except - 6:53
• Try Except Else statements - 8:12
• Finally statements and assertions - 12:15
• Challenge : Exceptions - 0:33
• Solution : Exceptions - 5:45

• Foundation : Modules - 2:35
• Creating a Module - 9:29
• Importing a Module - 10:24
• Using if name for checking if your program is executing - 8:13
• Challenge : Modules - 2:47
• Solution : Modules - Create the Module - 7:57
• Solution : Modules - Create the Main Program - 5:10

• Foundation : Getting Input from the User - 1:35
• Using the sys module - 6:49
• Using the argparse library - 9:22
• Foundation : Working with Files - 5:04
• Working with Files - Part 1- Creating, Writing to and Reading from Files - 9:59
• Working with Files - Part 2 - Exploring other ways of reading data from a file - 4:26
• Working with Files - Part 3 - File Seek and Editing Files - 9:56
• Foundation : Working with File and Directory Commands - 1:18
• Working with Files and Directories - 8:22

• Foundation : Working Data Files (CSV and JSON) - 4:00
• Reading CSV Files - Part 1 - 12:34
• Reading CSV Files - Part 2 - 7:07
• Writing CSV Files - 11:54
• Reading JSON Files - Reading (Deserialization) - Part 1 - 7:48
• Working with JSON Files - Writing (Serialization) - Part 2 - 8:00
• Working with Pandas - Loading CSV Files in a DataFrame - Part 1 - 11:12
• Working with Pandas - Writing out CSV Files - Part 2 - 7:35

• Foundation : Working with HTTP in Python - 1:26
• GET Requests - Part 1 - 12:55
• GET Requests - Adding QueryString Parameters - Part 2 - 11:09
• Open Weather Map API Review - 5:23
• GET Requests to Open Weather Map - Part 3 - 7:29
• Handling Network Errors for GET Requests - Part 4 - 8:20
• POST Requests - 5:55

• Closing Comments

• Introduction - 5:16

• Installing Python - 4:57
• Setting up Sublime Text to Build Python - 5:32

• First Program in Python - 1:14
• Data Types - 1:38
• Variables - 4:10
• Indentation - 1:16
• How to Clear Screen - 0:46

• Single-line Comments - 2:15
• Multi-line Comments - 1:47

• Basic Arithmetic - 2:55
• Division Characteristics - 4:06
• Operator Precedence - 2:17
• Complex Arithmetic - 5:56
• Binary Number Manipulation - 5:44

• Basic String Manipulation - 9:41
• Using the format Method - 7:34
• Specific Characters - 4:00

• Logical Operators and Conditional Statements - 7:50
• if Statement - 4:38
• if else Statement - 4:40
• if elif Statement - 5:45
• Ternary Operator - 2:51

• for Loop Part 1 - 4:47
• for Loop Part 2 - 3:34
• for Loop Part 3 - 5:40
• while Loop - 2:23
• break and continue Statements - 2:11

• Defining and Calling Functions and Returning Values - 5:26
• Passing Arguments, Default Parameters, Scope, and Nested Functions - 11:45
• Recursive Functions - 6:55
• Lambda Functions - 4:56

• Exceptions and Errors - 3:43
• Handling Exceptions - 8:32
• Throwing Exceptions - 6:11

• Data Input Setup and Input Function - 6:05
• File Management: Reading - 9:18
• File Management: Writing - 4:10

• Tuples - 9:31
• Tuple Functions - 1:25
• Lists - 6:03
• List Functions - 5:33
• Dictionaries - 6:56
• Shallow Copies - 2:48
• Sets - 5:26
• Set Functions - 1:23

• Modules - 4:12
• Packages - 3:28
• Built-in Modules - 10:02

• Introduction to OOP - 3:16
• Class Definition and Object Instantiation - 9:45
• Class Methods Part 1 - 4:45
• Class Methods Part 2 - 9:55
• Operator Overloading - 13:19
• Class Inheritance Part 1 - 10:28
• Class Inheritance Part 2 - 2:58
• Extra Notes in Python - 5:14

• Installing Modules for Visualization - 14:35
• Visualization Part 1 - 15:49
• Visualization Part 2 - 16:13
• Visualization Part 3 - 8:09
• Pandas Library - 8:56

• Instaling the Numpy Library - 4:59
• Creating Numpy Objects - 11:52
• Useful Functions from the Numpy Library - 19:09
• Basic Operations with Numpy Library - 7:57

• The pdb Module - 9:44
• Commands for Debugging Part 1 - 10:19
• Commands for Debugging Part 2 - 13:24

• Creating, Evaluating, and Compiling Regular Expressions - 10:44
• Patterns - 18:27
• Division and Grouping the Results - 8:30
• Setting the Search Parameters - 12:23

• What is Python? Installation and first execution - 8:47
• Variables, creating and running external srcript, interactive Shell - 14:06
• Comments - 3:17
• Types of Variables - 9:15
• Math Operators - 10:12
• Semicolon and ENTER - assigning multiple values to variables at once - 4:30
• Exercise: adding VAT to products - 8:12
• Assignment operators - 2:18
• Playing with Strings (Slicing, adding and having fun) - 10:01

• Importing libraries - basics - 10:51
• ATTENTION - important lecture about common mistake regarding functions - 5:22
• Taking data from user and type conversion (casting) - 11:52

• Comparison (Relational) Operators - 5:10
• Instruction 'if' why INDENTATION is IMPORTANT in Python | DO NOT SKIP! - 10:50
• EXERCISE: Simple Calculator - 11:05
• Values different than 0 - 3:04
• Logical Operators - 9:58

• Loop while - 6:06
• EXERCISE: Adding numbers taken from the user - 8:08
• Loop for - 6:12
• Instruction break and continue - 11:20
• EXERCISE: Guess the number - 8:34

• What are Lists? Basic operations on lists - 9:36
• Checking if element is 'in' or 'not in' the list - 2:41
• Operating on lists with Functions - 13:34

• Tuples - what does immutable mean? - 5:20
• Dictionary - 10:07
• Sets - 7:12
• Operations on sets - 7:32
• Nested types - 12:49
• Processing nested types - 3:28
• Dictionary inside Dictionary, Dictionary inside List - when to choose which? - 8:37
• Extracting (Iterating Through) values from nested dictionaries - 19:33
• EXERCISE: Dynamic dictionary with definitions - 12:20

• List Comprehensions - 10:14
• Generator expression - 11:29
• Dictionary Comprehensions - 14:04
• Set Comprehensions - 2:51
• EXERCISE: Finding numbers that are divisible by 7, but are not divisible by 5 - 10:24

• How to create a function? - 10:24
• Multiple parameters in function (passing more arguments) - 4:46
• Returning values from function - 12:26

• Multi module application | How to import your own module? - 9:52
• enum - what it is and why you should use it? - 13:01
• EXERCISE: Sum of all numbers up to the one entered by user | IMPORTANT lecture - 13:49
• Measuring PERFORMANCE of code | How well (fast) some part of code work | time - 15:06
• Function as argument of another function | How to measure performance of func - 7:19
• default arguments - 7:21
• named (keyword) and unnamed (positional) arguments - 8:31
• EXERCISE - checking if value is in container - 6:48
• Variable Length Argument (Multiple Arguments sent and saved in single parameter) - 11:45
• Local vs Global Variables - scope - lifetime of variables - 9:12
• Mutable vs immutable objects - 20:56
• Shallow vs Deep copy of object - 14:08
• Lambda | Anonymous functions - what are they? when should you use them? - 11:49

• Drawing random numbers - creating a program that checks if you hit the monster - 14:47
• Random events - choice vs choices function - 9:04
• shuffle - shuffling cards in 'war' game - 3:04
• EXERCISE: Drawing elements without REPETITION - lottery game - 6 numbers from 49 - 12:46
• EXERCISE | GAME | Drawing random chests colours with random rewards - 25:02
• EXERCISE | GAME | Drawing approximate value to a certain value - 8:05

• What is a file? How to create it? Why do we need to CLOSE it? How to save data? - 10:09
• Exceptions, try, finally block - 4:22
• Reading the content of file - read vs readlines, splitting lines, encoding - 7:29
• Opening the file using: with... as...: - 3:01
• seek and tell - changing and reading the position of last operation in file - 5:04
• append - adding text at the end of file - 2:21

• Generator functions - yield keyword - 13:30
• EXERCISE: Generate infinite amount of numbers multiplied by themselves - 6:19
• send method - how to send a value into a generator? - 11:54

• Welcome to the Course - 1:40
• Data and Scripts for the Course
• Introduction to the Python Data Science Tool - 10:57
• For Mac Users - 4:05
• Introduction to the Python Data Science Environment - 19:15
• Some Miscellaneous IPython Usage Facts - 5:25
• Online iPython Interpreter - 3:26
• Conclusion to Section 1 - 2:36

• What are Pandas? - 12:06
• Read CSV Data in Python - 5:42
• Read in Excel File - 5:31
• Read HTML Data - 12:06
• Read JSON Data - 9:14
• Conclusions to Section 4 - 2:06

• Remove NA Values - 10:28
• Basic Data Handling: Starting with Conditional Data Selection - 5:24
• Basic Data Grouping Based on Qualitative Attributes - 9:47
• Rank and Sort Data - 8:03
• Concatenate - 8:16
• Merge - 10:47

• What is Statistical Data Analysis? - 10:08
• Some Pointers on Collecting Data for Statistical Studies - 8:38
• Explore the Quantitative Data: Descriptive Statistics - 9:05
• Group By Qualitative Categories - 10:25
• Visualize Descriptive Statistics-Boxplots - 5:28
• Common Terms Relating to Descriptive Statistics - 5:15
• Data Distribution- Normal Distribution - 4:07
• Check for Normal Distribution - 6:23
• Standard Normal Distribution and Z-scores - 4:10
• Confidence Interval-Theory - 6:06
• Confidence Interval-Calculation - 5:20

• Explore the Relationship Between Two Quantitative Variables - 4:26
• Correlation Analysis - 8:26
• Linear Regression-Theory - 10:44
• Linear Regression-Implementation in Python - 11:18
• Conditions of Linear Regression-Check in Python - 12:03
• Polynomial Regression - 3:53
• GLM: Generalized Linear Model - 5:25
• Logistic Regression - 11:10

• How is Machine Learning Different from Statistical Data Analysis? - 5:36
• What is Machine Learning (ML) About? Some Theoretical Pointers - 5:32

• What is this section about - 10:10
• Data Preparation for Supervised Learning - 9:47
• Pointers on Evaluating the Accuracy of Classification and Regression Modelling - 9:42
• Random Forest (RF) Regression - 9:20
• Support Vector Regression - 4:30
• kNN Regression - 3:48
• Gradient Boosting-regression - 4:46
• Theory Behind ANN and DNN - 9:17
• Regression with MLP - 3:48

• What is Data Science? - 3:37
• Data and Scripts for the Course
• Introduction to the Python Data Science Tool - 10:57
• For Mac Users - 4:05
• Introduction to the Python Data Science Environment - 19:15
• Some Miscellaneous IPython Usage Facts - 5:25
• Online iPython Interpreter - 3:26

• What are Pandas? - 12:06
• Read CSV Data in Python - 5:42
• Read in Excel File - 5:31
• Read HTML Data - 12:06
• Read JSON Data - 9:14

• Remove NA Values - 10:28
• Basic Data Handling: Starting with Conditional Data Selection - 5:24
• Subset and Index Data - 9:44
• Basic Data Grouping Based on Qualitative Attributes - 9:47
• Rank and Sort Data - 8:03
• Concatenate - 8:16
• Merge - 10:47

• Some Basic Pointers - 1:38
• kmeans-theory - 2:31
• KMeans-implementation on the iris data
• Quantifying KMeans Clustering Performance - 3:53
• kmeans clustering on real data - 4:16
• How Do We Select the Number of Clusters? - 5:38
• Theory of hierarchical clustering - 4:10
• Implement hierarchical clustering - 9:19
• Theory of Principal Component Analysis (PCA) - 2:37
• Implement PCA - 3:52

• What is this section about? - 10:10
• Logistic regression with classification - 8:26
• Random Forest (RF) For Classification - 12:02
• Linear Support Vector Machine (SVM) Classification - 3:10
• Non-Linear Support Vector Machine (SVM) Classification - 2:06
• kNN Classification - 7:46
• kNN Regression - 3:48
• Gradient Boosting Machine (GBM) Classification - 5:54
• GBM Classification
• Voting Classifier - 4:00

• Introduction
• Perceptrons for Binary Classification - 4:27
• Getting Started with ANN-binary classification - 3:26
• Multi-label classification with MLP - 4:53
• Start with H20 - 4:14
• Specify the Activation Function - 2:06
• Deep Learning Predictions - 5:02

• Welcome to the Course - 2:01
• Data & Script For the Course
• Python Data Science Environment - 10:57
• For Mac Users - 4:05
• Introduction to IPython/Jupyter - 19:13

• What are Pandas? - 12:06
• Read CSV - 5:42
• Read Excel - 5:31

• Remove NA Values - 10:28
• Missing Values in a Real Dataset - 6:04
• Data Imputation - 9:07
• Imputing Qualitative Value - 3:27
• Theory Behind k-NN Algorithm
• Use k-NN for Data Imputation - 6:23

• Basic Principles - 4:20
• Preliminary Data Explorations - 8:17
• Basic Data Handling With Conditional Statements - 5:24
• Drop Column/Row - 4:42
• Change Column Name - 3:35
• Change the Column Type - 3:50
• Explore Date Related Data - 4:02
• Simple Date Related Computations - 3:46

• Data Grouping - 9:47
• Data Subsetting and Indexing - 9:44
• More Data Subsetting - 8:54
• Extract Information From Strings - 4:40
• (Fuzzy) String Matching - 2:39
• Ranking & Sorting - 8:03
• Concatenate - 8:16
• Merging and Joining - 10:47

• Correlation Analysis - 8:26
• Using Correlation to Decide Which Features to Retain - 5:00
• Univariate Feature Selection - 4:56
• Recursive Feature Elimination (RFE) - 4:26
• Theory Behind PCA - 2:37
• Implement PCA - 3:53
• Data Standardisation - 4:10
• Create a New Feature - 6:16

• What is Data Visualisation? - 9:33
• Some Theoretical Principles Behind Data Visualization - 6:46

• Histograms- Visualize the Distribution of Continuous Numerical Variables - 12:13
• Boxplot- Visualise Data Distribution - 5:54
• Scatter Plot: Relationship Between Variables - 11:57
• Barplot - 22:25
• Pie Chart - 5:29
• Line Chart - 12:31
• More Line Charts - 2:32
• Some More Plot Types - 11:14
• And Some More - 8:40

• Course Intro - 1:55

• TCP Reverse Shell Outline - 4:50
• Server Side - Coding a TCP Reverse Shell - 7:46
• Client Side - Coding a TCP Reverse Shell - 8:44
• Coding a Low Level Data Exfiltration - 7:11
• Exporting To EXE - 2:52
• HTTP Reverse Shell Outline - 2:36
• Coding a HTTP Reverse Shell - 11:06
• Data Exfiltration - 7:54
• Persistence Outline - 4:29
• Making our HTTP Reverse Shell Persistent - 10:07
• Tuning the connection attempts - 4:24

• DDNS Aware Shell - 5:28
• Interacting with Twitter - 9:49
• Target Directory Navigation - 6:45
• Replicating Metasploit "Screen Capturing" - 4:54
• Replicating Metasploit "Searching for Content" - 5:49
• Integrating Low Level Port Scanner - 5:29

• Bypassing Host Based Firewall Outline - 3:24
• Hijacking Internet Explorer - Shell Over Internet Explorer - 5:04
• Bypassing Reputation Filtering in Next Generation Firewalls - Outline - 2:56
• Interacting with Source Forge - 5:49
• Interacting with Google Forms - 4:55

• Bypassing IPS with Hand-Made XOR Encryption - 7:27
• Quick Introduction To Encryption Algorithms - 7:05
• Protecting Your Tunnel with AES - 8:57
• Protecting Your Tunnel with RSA - 12:41
• Developing One Time, Hybrid - Encryption Key - 4:35