The Complete Python Certification Bootcamp Bundle

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Lifetime

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7 hours

Lessons

9

Data Mining with Python Course

These Real-Life Data Science Exercises Will Help You Become a Data Scientist

By Mammoth Interactive | in Online Courses

What is data mining? Data mining is getting useful actionable insights from data. Whoever owns data owns the future. But owning data is not good enough. You need to know how to draw insights from a dataset, draw useful insights, look at statistics, and find patterns using a dataset. In this course, you'll grow your skills and become an indispensable data scientist.

Access 9 lectures & 7 hours of content 24/7
Use real-world examples of data mining & datasets
Practice for real-world projects
Clean data, filter noise, make data available for analysis
Perform cluster analysis, classification, & regression, including logistic regression
Use the K-NN classifier & SVM
Detect outliers in univariate, multivariate & high dimensional spaces
Learn how to use Apache Spark, the number one framework used for distributed processing

Instructor

John Bura has been programming games since 1997 and teaching since 2002. John is the owner of the game development studio Mammoth Interactive. This company produces XBOX 360, iPhone, iPad, Android, HTML 5, ad-games and more. Mammoth Interactive recently sold a game to Nickelodeon! John has been contracted by many different companies to provide game design, audio, programming, level design, and project management. To this day John has 40 commercial games that he has contributed to. Several of the games he has produced have risen to number 1 in Apple's app store. In his spare time, John likes to play ultimate Frisbee, cycle and work out.

Important Details

Length of time users can access this course: lifetime
Access options: web and mobile streaming
Certification of completion included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels

Requirements

Internet required

Course Outline

Your First Program
Preview
Course Trailer
- Course Trailer: Data Mining with Python - 0:29
Python Language Basics for Beginners
- Learning Python in Pycharm
- Source Files - Learning Python with Mammoth Interactive
- Downloading and Installing Pycharm and Python
- Exploring Pycharm
- Declaring Variables in Python - 13:17
- Using and Converting Variables - 12:35
- Types of Collections in Python - 12:47
- Collections Operations - 8:42
- Control Flow: 'If' Statements - 12:50
- 'While' Loops and 'For' Loops - 10:44
- Functions - 11:23
- Classes and Objects - 15:40
Introduction to Data Mining
- Introduction to Data Mining - 9:30
- Project Files - Mammoth Interactive
Data Wrangling: A Complete Overview
- Data Wrangling Demystified - 63:56
- Project Files - Mammoth Interactive
Data Mining Fundamentals
- 01. Cluster Analysis - 20:08
- 02. Classification and Regression - 34:30
- 03. Association and Correlation - 13:10
- 04. Dimensionality Reduction - 25:38
- Project Files - Mammoth Interactive
Frameworks Explained: Taming Data with Spark
- 01. Apache Spark - An Overview Of The Framework - 26:36
- 02. Spark Key Functions - 20:26
- 03. Spark Machine Learning - 7:32
- 04. EXAMPLES - Using The Machine Learning Pipeline - 6:16
- Project Files - Mammoth Interactive
EXAMPLES: Mining and Storing Data
- 01. Text Mining - 15:05
- 02. Network Mining - 10:11
- 03. Matrix - 7:16
- 04. SQL - 12:35
- Project Files - Mammoth Interactive
NLP (Natural Language Processing)
- 01 NLP Data Cleaning - 6:55
- 02. Count Vectorizer - 7:58
- 03. NLP Example with Spam - 9:59
- 04. Tweak Model with Spam Data - 5:32
- 05. Pipeline with Spam Data - 4:48
- Project Files - Mammoth Interactive
Conclusion and Challenge
- 06. Conclusion and Challenge - 4:40
- Project Files - Mammoth Interactive

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3 hours

Lessons

24

Learn Python 3 By Making a Game

Make A Game, Learn Python. Two Birds, One Stone!

By ZENVA | in Online Courses

Python is a language that is currently in extremely high-demand, and you can learn it the fun way through this course! With no prior programming experience necessary, this course will demonstrate core concepts you need to program in Python by building your own game, getting you up and running with Python in a way that's both engaging and fun.

Access 24 lectures & 3 hours of content 24/7
Create a game similar to Crossy Road or Frogger
Use the Pygame library to put together your first Python game
Become familiar w/ concepts like variables, functions, conditional statements, & loops

Instructor

Pablo Farias Navarro is a software developer and founder of ZENVA. Since 2012, he has been teaching online how to create games, apps and websites to over 150,000 students through the Udemy and Zenva Academy platforms, and created content for companies such as Amazon and Intel.

Pablo is a member of the Intel Innovator Program in the Asia Pacific, and has run live programming workshops in San Francisco, Brisbane and Bangalore. Pablo holds a Master in Information Technology (Management) degree from the University of Queensland (Australia) and a Master of Science in Engineering degree from the Catholic University of Chile.

Important Details

Length of time users can access this course: lifetime
Access options: web streaming, mobile streaming
Certification of completion included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels

Requirements

Internet required

Course Outline

Introduction and Installation
- Introduction - 2:32
- Source Files
- Installing Python and Pygame for Mac - 4:54
- Installing Python and Pygame for PC - 4:33
- Intro to Idle - 7:12
Introduction to Python
- Variables Intro - 6:22
- Variable Operations - 7:26
- Tuples, Lists, Dictionaries - 13:17
- If Statements - 9:24
- While and For in Loops - 10:51
- Functions - 11:31
- Classes and Objects Intro - 14:30
- Subclasses and Inheritance - 13:40
Build a Road-Crossing Game in Pygame
- Setting up the Display - 7:09
- Building a Basic Game Loop - 10:45
- Displaying Shapes and Images - 11:37
- Making Code Object Oriented - 10:34
- Creating Game Object Class - 8:32
- Implementing Player Class and Basic Movement - 16:43
- Implementing Enemy Class and Bounds Checking - 12:36
- Implement Collision Detection - 15:10
- Implementing Win and Lose Conditions - 11:26
- Increasing Game Difficulty - 7:26
- Project Summary - 5:13

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2 hours

Lessons

23

The Complete Python Data Visualization Course

Master the Major Plotting Libraries & Use Them to Create Beautiful Plots

By ZENVA | in Online Courses

Python is an especially valuable tool for visualizing data, and this course will cover a variety of techniques that will allow you to visualize data using popular plotting libraries like Matplotlib, Seaborn, and Bokeh. Beginning with an intro to statistics, you'll extend into a variety of plots that will cover most use-cases.

Access 23 lectures & 2 hours of content 24/7
Explore bar charts, line plots, & scatter plots
Take a deep dive into Matplotlib, Seaborn, & Bokeh
Discover subplots & how they're used

Instructor

Pablo Farias Navarro is a software developer and founder of ZENVA. Since 2012, he has been teaching online how to create games, apps and websites to over 150,000 students through the Udemy and Zenva Academy platforms, and created content for companies such as Amazon and Intel.

Pablo is a member of the Intel Innovator Program in the Asia Pacific, and has run live programming workshops in San Francisco, Brisbane and Bangalore. Pablo holds a Master in Information Technology (Management) degree from the University of Queensland (Australia) and a Master of Science in Engineering degree from the Catholic University of Chile.

Important Details

Length of time users can access this course: lifetime
Access options: web streaming, mobile streaming
Certification of completion included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels

Requirements

Internet required

Course Outline

Introduction
- Introduction - 2:38
- Source Code Files
Project
- Column Chart - 14:04
- Chart Annotations - 6:53
- Bar Charts - 9:45
- Pie Charts - 9:53
- Line Charts - 8:37
- Multiline Charts - 7:17
- Scatter Plots - 11:07
- Multiple Plots in One Figure - 13:06
- Seaborn Column and Bar Charts - 11:45
- Seaborn Line Plots - 6:24
- Seaborn Scatter Plots and Multiple Plots - 9:34
- Seaborn Joint Plots - 9:08
- Bokeh Column Chart - 9:44
- Bokeh Bar Chart - 6:20
- Bokeh Hover-over Tooltips - 5:37
- Bokeh Multiline Plots - 6:35
- Bokeh Interactive Legends - 2:26
- Bokeh Scatter Plots - 8:28
- Bokeh Multiple Plots - 9:41
- Bokeh Linked Panning - 3:55
Conclusion
- Conclusion - 1:43

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1 hours

Lessons

10

Web Scraping with Python and BeautifulSoup

Learn How to Capture Data From the Web by Scraping Websites Using Python & BeautifulSoup

By ZENVA | in Online Courses

Gathering data from a web page is known as web scraping, and is typically performed either by fetching web page via URL and reading the data directly online or by reading the data from a saved HTML file. Understanding web scraping is a skill crucial to anyone interested in data science or those just looking to obtain information from web pages.

Access 10 lectures & 1 hour of content 24/7
Download & install the Python library BeautifulSoup
Inspect a web page to identify the relevant data
Scrape & parse data using BeautifulSoup
Store & sanitize data in a correctly formatted CSV sheet
Read from local HTML files instead of URLs

Instructor

Pablo Farias Navarro is a software developer and founder of ZENVA. Since 2012, he has been teaching online how to create games, apps and websites to over 150,000 students through the Udemy and Zenva Academy platforms, and created content for companies such as Amazon and Intel.

Pablo is a member of the Intel Innovator Program in the Asia Pacific, and has run live programming workshops in San Francisco, Brisbane and Bangalore. Pablo holds a Master in Information Technology (Management) degree from the University of Queensland (Australia) and a Master of Science in Engineering degree from the Catholic University of Chile.

Important Details

Length of time users can access this course: lifetime
Access options: web streaming, mobile streaming
Certification of completion included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels

Requirements

Internet required

Course Outline

Introduction
- Intro to Web Scraping - 3:27
- Source Code Files
Project
- Beautiful Soup and Web Page Inspection - 8:17
- Scraping Web Pages - 11:57
- Sorting HTML Table Data - 14:38
- Writing HTML to CSV - 9:05
- Sanitizing Input - 5:48
- Reading HTML from Local Files - 7:24
- Reading HTML from Non-Table Data - 13:57
Conclusion
- Conclusion - 3:15

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6 hours

Lessons

57

Python Web Programming

Start Programming the Right Way By Diving Into Python

By Stone River eLearning | in Online Courses

Python is considered by many experts to be the ideal learning language for first time programmers because it is syntactically fairly straight-forward and has an enormous reach of applications. It's an excellent stepping stone for other, more complex languages, yet Python programmers are also in constant demand. This course dives into all aspects of web programming with Python, and will be the perfect first step for your coding odyssey.

Access 57 lectures & 6 hours of content 24/7
Acquire an in-depth understanding of Python web programming
Get hands-on experience working w/ Python files & building programs
Access & parse the web w/ Python
Manage a database & a remote server
Create a basic website w/ Python
Run code via a Virtual Private Server

Instructor

At Stone River eLearning, technology is all we teach. If you're interested in programming, development or design - we have it covered.

Check out our huge catalog of courses and join the over 300,000 students currently taking Stone River eLearning courses. We currently offer 125+ different technology training courses on our Stone River eLearning website and are adding new courses on hot and trending topics every month. A subscription option is available for those with a real passion for learning.

Important Details

Length of time users can access this course: lifetime
Access options: web streaming, mobile streaming
Certification of completion included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: beginner

Requirements

Internet required

Course Outline

Course Introduction
- Course Introduction - 4:12
Python Programming Review
- Introduction - 1:07
- Object Oriented Programming Part 1 - 8:25
- Object Oriented Programming Part 2 - 7:16
- Modules - 9:11
- Modules Part 2 - 6:58
- Section Conclusion - 0:42
Basic Database (SQLite) with Python
- Introduction - 0:52
- SQLite Intro - 8:15
- Creating Database And Table - 7:17
- Inserting Data - 6:06
- Inserting Dynamic Data - 4:32
- Reading Data - 6:41
- Limit, Update, and Delete - 7:59
- Section Conclusion - 3:18
Using Python with the Internet
- Section Introduction - 0:51
- urllib module - 5:17
- urllib.requests - 9:42
- urllib headers - 8:15
- xml intro - 5:53
- parsing xml - 8:34
- Section Conclusion - 1:27
Working with HTML
- Section Introduction - 1:10
- Web Page Structure - 8:14
- Web Page Structure 2 - 7:31
- Nav bar - 9:01
- HTML’s body - 8:04
- Comments, footers, and divs - 8:27
- Parsing Paragraph Data - 7:07
- Section Conclusion - 1:20
Intro to Web Server Programming
- Section Introduction - 3:45
- Creating a VPS - 6:58
- Interacting with our VPS - 9:26
- FileZilla - 8:18
- PySFTP - 8:16
- Section Conclusion - 1:05
MySQL database with Python
- Section Introduction - 1:21
- MySQL basics - 9:33
- MySQL Part2 - 8:49
- Database Connection - 9:04
- Inserting into Database - 9:39
- Adding logic to insert - 8:17
- Nohup - 9:25
- Crontab - 6:12
- Section Conclusion - 1:44
Python's Flask Web development Framework
- Section Introduction - 1:35
- Flask setup - 8:52
- Flask backend setup - 9:30
- Basic Website - 9:19
- Templates and Errors - 9:14
- Variables and Logic - 8:51
- Bootstrap incorporation - 9:00
- More on Bootstrap - 10:20
- Adding more pages to our site - 7:36
- Extending Templates - 7:50
- Additional Information - 9:42
Course Conclusion
- Course Conclusion - 1:39

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5 hours

Lessons

49

Complete Data Wrangling & Data Visualization With Python

Learn to Preprocess, Wrangle & Visualize Data for Practical Data Science Applications In Python

By Minerva Singh | in Online Courses

This course is a sure-fire way of acquiring the knowledge and statistical data analysis wrangling and visualization skills you need to make it in the data analysis field. You'll go from data zero to performing some of the most common data wrangling tasks in Python. Plus, you'll be able to visualize data like a pro so you can help companies make actionable insights.

Access 49 lectures & 5 hours of content 24/7
Use some of the most important Python data wrangling & visualization packages
Apply important data visualization concepts for practical data analysis & interpretation
Decide which wrangling & visualization techniques are best suited to answer different questions

Instructor

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Important Details

Length of time users can access this course: lifetime
Access options: web streaming, mobile streaming
Certification of completion included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels

Requirements

Internet required

Course Outline

INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
- 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
Read in Data From Different Sources With Pandas
- What are Pandas? - 12:06
- Read CSV - 5:42
- Read Excel - 5:31
Data Clean
- 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 Data Wrangling
- 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
More Data Wrangling
- 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
Feature Selection and Transformation
- 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
Theory Behind Data Visualisation
- What is Data Visualisation? - 9:33
- Some Theoretical Principles Behind Data Visualization - 6:46
Most Common Data Visualisations
- 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

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Content

9 hours

Lessons

165

Python 3 Complete Master Class

Become a Python Developer From Scratch & Make Your Job Easier with Python 3

By Mihai Catalin Teodosiu | in Online Courses

This Python 3 programming course is aimed at anyone with little or no experience in coding but who wants to learn Python from scratch. This hands-on training takes you from "Hello World!" to advanced Python topics in just a few hours. Each lesson is filled with relevant examples, created in a learn-by-doing fashion and the quizzes, coding exercises and assignments will help you consolidate the main ideas behind each Python 3 topic.

Access 165 lectures & 9 hours of content 24/7
Learn & practice every Python 3 key concept
Explore advanced Python 3 topics
Build a scientific calculator in Python 3
Gain real-life skills that you can use at most programming & IT-related jobs

Instructor

Mihai Catalin Teodosiu holds a degree in Telecommunications and Information Technology from University Politehnica of Bucharest, Romania, as well as the CCNP, CCNA, CCDA, JNCIA, and ISTQB CTFL certifications. He has been working as a Network Quality Assurance Engineer since 2010, testing the OS for Nortel/Avaya L3 switches.

5+ years experience in the Networking and Testing/Quality Assurance industries.
Certified professional: Cisco, Juniper & International Software Testing Qualifications Board certifications
Teaching courses on Udemy, GNS3 Academy & other e-learning platforms
Thousands of satisfied students, 4.97 / 5 average course rating
Thousands of followers on LinkedIn, Twitter, Facebook & Blogger

Important Details

Length of time users can access this course: lifetime
Access options: web streaming, mobile streaming
Certification of completion included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels

Requirements

Internet required

Course Outline

Python 3 - Basics
- How to Install Python 3 on Windows - 2:57
- How to Install Python 3 on macOS - 2:35
- 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
- Python 3 Basics - Scripts in macOS - 4:21
- 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
Python 3 - Strings
- 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 - Slices - 7:42
- Notebook - Strings
Python 3 - Numbers and Booleans
- 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
- Python 3 Lists - Introduction - 3:42
- Python 3 Lists - Methods - 8:27
- Python 3 Lists - Slices - 5:40
- Notebook - Lists
Python 3 - Sets
- 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
- Python 3 Tuples - Introduction - 4:48
- Python 3 Tuples - Methods - 3:25
- Notebook - Tuples
Python 3 - Ranges
- Python 3 Ranges - Introduction - 4:06
- Python 3 Ranges - Methods - 2:40
- Notebook - Ranges
Python 3 - Dictionaries
- 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, Loops and Exceptions
- 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 - Handling Errors and Exceptions in Python
- Python 3 - Fixing Syntax Errors - 5:24
- Python 3 - Fixing Exceptions - 8:45
Python 3 - Functions and Modules
- 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 - File Operations
- Python 3 Files - Opening & Reading - 12:10
- Python 3 Files - Quick Note for Windows Users - 2:48
- 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 - Regular Expressions
- Python 3 Regex - match() & search() - 16:24
- Python 3 Regex - findall() & sub() - 6:16
- Python 3 Regex - Regular Expressions Summary
- Notebook - Regular Expressions
Python 3 - Classes and Objects
- Python 3 Classes - Objects - 11:45
- Python 3 Classes - Inheritance - 6:19
- Notebook - Classes and Objects
Python 3 - Other Advanced Concepts
- 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
Python 3 - Cheat Sheet
- Download the Python 3 Cheat Sheet
Python 3 - E-Book
- Download the Python 3 E-Book
APPLICATION: Build a Scientific Calculator with Python 3
- 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
Automate Excel Tasks with Python 3
- 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
- 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
Automate Database Tasks with Python 3
- Installing the Database Server Software - 2:45
- Downloading and Installing PostgreSQL on All Platforms
- Installing the Necessary Python Module - 1:32
- Creating a New Database, Schema and User - 5:26
- 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
- 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
Automate Network Tasks with Python 3
- 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
- Signing Up to the Arista Software Download Portal
- Importing the VM & Tweaking the VM Settings - 3:08
- Connecting the Local PC to the Devices in Windows - 4:52
- Connecting the Local PC to the Devices in macOS
- Checking the SSH Configuration and Testing the Connectivity - 3:03
- Necessary Switch/Router Configuration
- 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
SUPERHERO LEVEL: Automate Data Analysis Tasks with Python 3
- 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
- Download the Code - SQL Data Analysis Application
SUPERHERO LEVEL: Data Visualization with Bokeh and Python 3
- Introduction to Bokeh - 3:38
- Bookmark These 3 Important Documentation Links
- Creating a Basic Line Plot Based on Python Lists - 11:17
- 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
- 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
- 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
SUPERHERO LEVEL: Automate Unit Testing with Python 3
- 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
SUPERHERO LEVEL: Automate Web Scraping with Python 3
- 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
10 Ways to Earn Money and Build a Portfolio with Your Python Skills
- Putting Your Skills to Work - Part 1 - 8:46
- Putting Your Skills to Work - Part 2 - 8:45
- Download the Presentation
Final Section
- Follow My Work and Join My LinkedIn Group

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196

Cyber Security: Python & Web Applications

Apply Your Python Knowledge to a Practical, Very In-Demand Field

By Mashrur Hossain | in Online Courses

Develop a practical skill set by learning how to detect and defeat online threats in this beginner-to-advanced course. You'll build security analysis tools using Python and learn how to analyze web app security vulnerabilities and solutions using frameworks like Ruby onRails and PHP. Put your newfound Python skills to the test by applying them to a practical, very in-demand use.

Access 196 lectures & 10 hours of content 24/7
Discuss vulnerability analysis, security scanning, phishing protection, & more
Perform password complexity analysis, multi-factor authentication, network traffic analysis, & more
Explore the biggest threats in IT, including botnets, code exploits, SQL injection, social engineering, & more

Instructor

Mashrur Hossain has been a technology professional for over a decade and holds degrees in both Computer Science and Economics. He has worked with Enterprise Software Systems throughout his career with roles in analysis, development, and management. He is very passionate about web application development and believes Ruby on Rails has proven to be a very strong force in this field. Join him for his comprehensive Ruby on Rails Developer courses as he guides you through the world of web application development using Rails.

Important Details

Length of time users can access this course: lifetime
Access options: web streaming, mobile streaming
Certification of completion included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: beginner

Requirements

Internet required

Course Outline

Introduction
- Course Introduction - 3:03
- Course Work Overview - 6:26
- Sample Tool 1 - Log Analyzer - 10:22
- Sample Tool 1 - Text Instructions
- Sample Tool 2 - Password Checker - 7:26
- Sample Tool 2 - Text Instructions
- Development Environment Overview - 5:00
- Important course updates - don't skip!
Cyber Security
- Cyber Security: Introduction to Section 2 - 0:45
- What is Cyber Security? - 8:03
- Cyber Security Introduction Quiz
- Explaining the Methodology - 7:12
- Methodology Quiz
- Intro to Python and Python Crash Course
- repl.it Introduction - 6:33
- Basics Review
- Penetration Testing - Overview - 5:31
- Penetration Testing - Code Example - 5:27
- Penetration Testing - Text Instructions
- Penetration Testing - Remediation - 8:28
- Penetration Testing Quiz
- Port Scanning - Overview - 4:29
- Port Scanning - Code Examples - 3:38
- Port Scanning - Text Instructions
- Port Scanning - Remediation - 3:51
- Port Scanning Quiz
- Botnets - Overview - 6:01
- Botnets - Code Example - 5:06
- Botnets - Code Example Continued - 5:04
- Botnets - Text Instructions
- Botnets - Remediation - 12:15
- Cyber Security Topics Review 1
- Code Exploits - Overview - 4:51
- Code Exploits - Code Example - 5:33
- Code Exploits - Text Instructions
- Code Exploits - Remediation - 3:55
- Forensic Investigations - 5:31
- Forensic Investigations - Code Example - 8:29
- Forensic Investigations - Text Instructions
- Forensic Investigations - Remediation - 5:09
- Network Traffic Analysis - Overview - 5:11
- Network Traffic Analysis - Code Example - 4:28
- Network Traffic Analysis - Text Instructions
- Network Traffic Analysis - Remediation - 3:43
- Cyber Security Topics Review 2
- Wireless - Overview - 4:33
- Wireless - Code Example - 3:08
- Wireless - Text Instructions
- Wireless - Remediation - 9:58
- Web Reconnaissance - Overview - 5:31
- Web Reconnaissance - Code Example - 9:36
- Web Reconnaissance - Text Instructions
- Web Reconnaissance - Remediation - 6:30
- Antivirus Evasion - Overview - 5:39
- Antivirus Evasion - Code Example - 7:02
- Antivirus Evasion - Text Instructions
- Antivirus Evasion - Remediation - 3:33
- Social Engineering - Overview - 6:57
- Social Engineering - Code Example - 4:30
- Social Engineering - Text Instructions
- Social Engineering - Remediation - 2:56
- Cyber Security Topics Review 3
Building a Security Tool
- Intro to Section 3: Building your own security tool - 1:15
- Installing Python Locally - Text Instructions
- Tool Development with Python - Overview - 4:54
- Introduction to Pip - 3:55
- Introduction to Pip - Text Instructions
- Basic Python Script - 3:24
- Basic Python Script - Text Instructions
- Command Line Arguments - 3:16
- Command Line Arguments - Text Instructions
- Argument Parsing - 6:29
- Argument Parsing - Text Instructions
- Python Tools Review 1
- Validating Input - 3:37
- Validating Input - Text Instructions
- Sending Requests to the Web - 2:40
- Sending Requests to the Web - Text Instructions
- Parsing Results - 6:43
- Parsing Results - Text Instructions
- Checking Forms - 4:31
- Checking Forms - Text Instructions
- Python Tools Review 2
- Generating the Report - 2:19
- Generating the Report - Text Instructions
- Checking Comments - 6:32
- Checking Comments - Text Instructions
- Checking Inputs - 5:12
- Checking Inputs - Text Instructions
- Improving the Report - 2:57
- Improving the Report - Text Instructions
- Using Config Settings - 7:16
- Using Config Settings - Text Instructions
- Configuration from File - 3:42
- Configuration from File - Text Instructions
- Merging the Configs - 2:09
- Merging the Configs - Text Instructions
- Outputting the Report - 8:36
- Outputting the Report - Text Instructions
- Python Tools Review 3
- Pushing to GitHub - 4:29
- Pushing to Github - Text Instructions
- Testing the Python Tool - 6:05
- Writing the Documentation - 6:24
- Distributing Your Tool - 5:48
- Python Tools Review 4
Web Application Security
- Section 4 Introduction - 1:17
- Web Application Security Introduction - 6:00
- OWASP Overview - 4:37
- Introduction to the OWASP Top 10 List - 1:27
- Rails Overview - 3:14
- PHP Overview - 5:08
- Installing Rails and PHP
- OWASP Top 10 Vulnerabilities Example Code - Where to get it
- Command Injection - Overview - 4:48
- Command Injection - Rails Example - 6:44
- Command Injection - Rails Example - Text Instructions
- Command Injection - PHP Example - 4:10
- Command Injection - PHP Example - Text Instructions
- SQL Injection - Overview - 6:25
- SQL Injection - Rails Example - 3:59
- SQL Injection - Rails Example - Text Instructions
- SQL Injection - PHP Example - 3:31
- SQL Injection - PHP Example - Text Instructions
- SQL Injection - PHP Example - Remediation - 2:26
- Password Complexity - Overview - 6:46
- Password Complexity - Rails Example - 6:50
- Password Complexity - Rails Example - Text Instructions
- Password Complexity - PHP Example - 6:28
- Password Complexity - PHP Example - Text Instructions
- Information Leakage - Overview - 4:56
- Information Leakage - Rails Example - 3:07
- Information Leakage - Rails Example - Text Instructions
- Information Leakage - PHP Example - 7:13
- Information Leakage - PHP Example - Text Instructions
- Sensitive Data Exposure - Overview - 4:53
- Sensitive Data Exposure - Rails Example - 4:23
- Sensitive Data Exposure - Rails Example - Text Instructions
- Sensitive Data Exposure - PHP Example - 2:54
- Sensitive Data Exposure - PHP Example - Text Instructions
- Web Application Security Review 1
- XML External Entities (XXE) - Overview - 4:40
- XML External Entities (XXE) - Rails Example - 4:36
- XML External Entities (XXE) - Rails Example - Text Instructions
- XML External Entities (XXE) - PHP Example - 4:28
- XML External Entities (XXE) - PHP Example - Text Instructions
- Broken Access Control - Overview - 5:12
- Broken Access Control - Rails Example - 11:56
- Broken Access Control - Rails Example - Text Instructions
- Broken Access Control - PHP Example - 3:39
- Broken Access Control - PHP Example - Text Instructions
- Security Misconfiguration - Overview - 5:50
- Security Misconfiguration - Rails Example - 3:46
- Security Misconfiguration - Rails Example - Text Instructions
- Security Misconfiguration - PHP Example - 3:28
- Security Misconfiguration - PHP Example - Text Instructions
- Web Application Security Review 2
- Cross-Site Scripting (XSS) - Overview - 1:34
- Cross-Site Scripting (XSS) - Rails Example - 5:09
- Cross-Site Scripting (XSS) - Rails Example - Text Instructions
- Cross-Site Scripting (XSS) - PHP Example - 5:50
- Cross-Site Scripting (XSS) - PHP Example - Text Instructions
- Insecure Deserialization - Overview - 3:46
- Insecure Deserialization - Rails Example - 5:18
- Insecure Deserialization - Rails Example - Text Instructions
- Insecure Deserialization - PHP Example - 7:22
- Insecure Deserialization - PHP Example - Text Instructions
- Using Components with Known Vulnerabilities - Overview - 5:28
- Using Components with Known Vulnerabilities - Rails Example - 11:01
- Using Components with Known Vulnerabilities - Rails Example - Text Instructions
- Using Components with Known Vulnerabilities - PHP Example - 4:38
- Using Components with Known Vulnerabilities - PHP Example - Text Instructions
- Insufficient Logging and Monitoring - Overview - 4:06
- Insufficient Logging and Monitoring - Rails Example - 5:49
- Insufficient Logging and Monitoring - Rails Example - Text Instructions
- Insufficient Logging and Monitoring - PHP Example - 6:14
- Insufficient Logging and Monitoring - PHP Example - Text Instructions
- Web Application Security Review 3
- Web Security Analysis Tool - 9:17
Bonus Material
- Multi-Factor Authentication - 16:35
- Ethical Hacking - 5:08
- Password Complexity - 11:18
- Physical Security - 10:31
- Data Breaches - 9:34
- Cloud Security - 11:44
Summary
- Appendix A: Acknowledgements
- Appendix B: Glossary
- Thank you! - 1:07
Python crash course
- Installation and expectations
- Strings
- Numbers
- List, Tuples and Sets
- Dictionaries
- Branching and Functions/Methods
- Standard library and imports
- Object Oriented Programming (OOP)

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55

From 0 to 1: Learn Python Programming - Easy as Pie

Make Quick Work of This Popular, Powerful Programming Language

By Loonycorn | in Online Courses

Python's one of the easiest yet most powerful programming languages you can learn, and it's proven its utility at top companies like Dropbox and Pinterest. In this quick and dirty course, you'll learn to write clean, efficient Python code, learning to expedite your workflow by automating manual work, implementing machine learning techniques, and much more.

Access 55 lectures & 10 hours of content 24/7
Acquire the database knowledge you need to effectively manipulate data
Eliminate manual work by creating auto-generating spreadsheets w/ xlsxwriter
Master machine learning techniques like sk-learn
Utilize tools for text processing, including nltk
Learn how to scrape websites like the NYTimes & Washington Post using Beautiful Soup
Complete drills to consolidate your newly acquired knowledge

Instructor

Loonycorn is comprised of two individuals—Janani Ravi and Vitthal Srinivasan—who have honed their respective tech expertise at Google and Flipkart. The duo graduated from Stanford University and believes it has distilled the instruction of complicated tech concepts into funny, practical, engaging courses, and is excited to be sharing its content with eager students.

Details & Requirements

Length of time users can access this course: lifetime
Access options: web streaming, mobile streaming
Certification of completion included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels

Compatibility

Internet required

Course Outline

What is coding? - It's a lot like cooking!
- Introduction - 2:51
- Coding is like Cooking - 7:38
- Anaconda and Pip - 9:02
- Variables are like containers - 11:03
Don't Jump Through Hoops, Use Dictionaries, Lists and Loops
- A List is a list - 9:19
- Fun with Lists! - 8:46
- Dictionaries and If-Else - 6:20
- Don't Jump Through Hoops, Use Loops - 4:28
- Doing stuff with loops - 5:31
- Everything in life is a list - Strings as lists - 7:09
Our First Serious Program
- Modules are cool for code-reuse - 2:32
- Our first serious program : Downloading a webpage - 17:50
- A few details - Conditionals - 7:50
- A few details - Exception Handling in Python - 7:50
Doing Stuff with Files
- A File is like a barrel - 11:23
- Autogenerating Spreadsheets with Python - 9:17
- Autogenerating Spreadsheets - Download and Unzip - 17:16
- Autogenerating Spreadsheets - Parsing CSV files - 18:36
- Autogenerating Spreadsheets with XLSXwriter - 5:27
Functions are like Foodprocessors
- Functions are like Foodprocessors - 11:00
- Argument Passing in Functions - 16:32
- Writing your first function - 12:56
- Recursion - 16:58
- Recursion in Action - 5:43
Databases - Data in rows and columns
- How would you implement a Bank ATM? - 17:41
- Things you can do with Databases - I - 20:08
- Things you can do with Databases - II - 8:14
- Interfacing with Databases from Python - 6:48
- SQLite works right out of the box - 6:29
- Manually downloading the necessary zip files
- Build a database of Stock Movements - I - 15:03
- Build a database of Stock Movements - II - 13:50
- Build a database of Stock Movements - III - 13:24
An Object Oriented State of Mind
- Objects are like puppies! - 3:45
- A class is a type of variable - 17:33
- An Interface drives behaviour - 13:42
Natural Language Processing and Python
- Natural Language Processing with NLTK - 7:28
- Natural Language Processing with NLTK - See it in action - 14:16
- Web Scraping with BeautifulSoup - 18:11
- A Serious NLP Application : Text Auto Summarization using Python - 12:02
- Autosummarize News Articles - I - 18:35
- Autosummarize News Articles - II - 11:30
- Autosummarize News Articles - III - 10:23
Machine Learning and Python
- Machine Learning - Jump on the Bandwagon - 16:33
- Plunging In - Machine Learning Approaches to Spam Detection - 17:32
- Spam Detection with Machine Learning Continued - 19:06
- News Article Classification using K-Nearest Neighbors - 20:03
- News Article Classification using Naive Bayes - 19:49
- Code Along - Scraping News Websites - 18:53
- Code Along - Feature Extraction from News articles - 15:47
- Code Along - Classification with K-Nearest Neighbours - 4:17
- Code Along - Classification with Naive Bayes - 8:10
- Document Distance using TF-IDF - 11:24
- News Article Clustering with K-Means and TF-IDF - 15:09
- Code Along - Clustering with K-Means - 8:34

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176

The Python Mega Course: Build 10 Real World Applications

Explore the Power of Python By Actually Building Apps with Python

By Ardit Sulice | in Online Courses

The best way to learn Python is by using Python, and this massive course will teach you while you develop real life applications. Over the course, you'll truly begin to appreciate the many, many uses of Python as you build web applications, database applications, web visualizations, and much more. By course's end, you will have built 10 applications that you can be proud of, and have the tools to go off on your own into the world of Python programming.

Access 176 lectures & 23 hours of content 24/7
Build a name generator, a website URL timed blocker, a web map generator, a portfolio website w/ Flask, a GUI-based desktop application, a webcam motion detector, a web scraper of property, an interactive web-based financial chart, a data collector web application, a geocoding web service
Under & use object-oriented design
Use Python to build applications w/ Flask, Tkinter, Numpy, Folium & more
Explore scraping data, computer vision, sending automated emails & more using Python
Schedule Python programs based on computer events

Instructor

Ardit Sulce received his master's degree in Geospatial Technologies from the Institute of Geoinformatics at University of Muenster, Germany. He also holds a Bachelor's degree in Geodetic Engineering.

Ardit offers his expertise in Python development on Upwork where he has worked with companies such as the Swiss in-Terra, Center for Conservation Geography, and Rapid Intelligence. He is the founder of PythonHow where he authors written tutorials about the Python programming language.

Important Details

Length of time users can access this course: lifetime
Access options: web and mobile
Certification of completion included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels

Requirements

Internet required

Course Outline

Getting started
- Course Introduction - 5:04
- Important Video - 1:50
- Create Your First Python Program - 7:29
- Useful Commands of the Command Line Interface - 8:52
- Running Python Interactively - 2:34
Python Basics
- Variables - 4:57
- Program output explained - 7:04
- Strings - 2:33
- Numbers - 2:05
- Programming tip
- Math operators
- Builtin functions - 4:41
- Lists - 5:28
- List indexing and slicing - 5:12
- List slice examples
- List methods - 3:26
- Tuples
- Dictionaries - 2:28
- More operations with dictionaries
- Getting user input - 6:19
- Conditionals - 9:56
- Conditionals with multiple conditions - 4:03
- Custom functions - 3:05
- Custom functions: Example - 5:44
- Custom functions: Return vs Print - 6:44
- Custom functions with conditionals - 4:54
- Custom functions with multiple parameters - 2:55
- Custom functions with default parameters - 1:27
- Opening external text files - 7:23
- Working with file paths - 3:47
- Processing the content of a file - 5:50
- For loops - 7:47
- Writing text to a file - 6:53
- Appending text to a file - 1:35
- Reading and appending text to a file - 2:58
Beyond the Basics
- Section introduction - 2:26
- Setting up - 5:09
- While loop - 5:47
- While loop password checker - 3:30
- String formatting - 4:06
- Modules, libraries, and packages - 11:13
- Datetime objects - 8:32
- Iterating multiple sequences - 1:53
- The "with" context manager - 3:21
Dealing with programming errors
- Syntax errors - 8:22
- Runtime errors - 10:58
- Fixing difficult errors - 5:38
- The structure of a good programming question - 5:59
- Error handling - 7:59
Application 1: Building an Interactive Dictionary
- Program demonstration - 4:10
- The data source - 4:54
- Loading JSON data - 3:52
- Returning the definition of a word - 3:25
- Counting for non-existing words - 2:51
- Implementing case sensitivity - 3:09
- Similarity ratio between two words - 4:39
- Best match out of a list of words - 6:07
- Recommending the best match - 9:42
- Confirmation from the user - 10:17
- Optimizing the final output - 7:51
- Exercise: Fixing a program bug (1)
- Solution
- Exercise: Fixing a program bug (2)
- Solution
Data Analysis with Pandas
- What is Pandas - 6:37
- Getting Started with Pandas - 8:37
- Getting Started with Jupyter Notebooks - 9:18
- Loading Data in Python from CSV, TXT, Excel and JSON Files - 12:48
- Indexing and Slicing Dataframes - 10:46
- Dropping Dataframe Columns and Rows - 2:30
- Updating and Adding New Columns and Rows - 7:31
- Example: Geocoding Addresses with Pandas and Geopy - 15:11
Numpy
- What is Numpy - 8:07
- Creating Numpy Arrays from Images and Vice-Versa - 12:30
- Indexing, Slicing and Iterating - 4:57
- Stacking and Splitting - 5:44
Application 2: Creating Leaflet Webmaps with Python and Folium
- Demonstration of the Web Mapping Application - 1:24
- Creating an Open Street Map with Python - 6:34
- Adding Markers to the Map - 5:10
- Adding Markers to the Map from CSV Data - 9:12
- Rule-based Coloring of Markers - 4:31
- More on Rule-based Styling - 4:27
- Calculating the Map Center from the Input Data - 7:56
- Adjusting the Code for the Latest Version of Folium - 8:12
- Adding a Choropleth Map from GeoJson - 20:59
- Adding a Layer Control Panel - 4:28
Application 3: Building a Website Blocker
- Demonstration of the Website Blocker Application - 3:48
- Application Architecture - 3:44
- Setting up the Script - 9:08
- Setting up the Infinite Loop - 11:00
- Implementing the First Part - 12:16
- Implementing the Second Part - 18:55
- Scheduling the Python Program on Windows - 12:39
- Scheduling the Python Program on Mac and Linux - 6:15
Application 4: Building a Website with Python and Flask
- Demonstration of the Website - 1:42
- Building Your First Website - 8:07
- Returning HTML Templates - 4:09
- Adding a Navigation Menu - 8:32
- Adding CSS Styling - 5:59
- Creating a Python Virtual Environment - 6:22
- Deploying the Website to a Live Server - 21:52
- Maintaining the Website - 7:26
Building Graphical User Interfaces with Tkinter
- Introduction to Tkinter - 2:35
- Setting up a GUI with Widgets - 9:11
- Connecting GUI Widgets with Callback Functions - 9:33
Python for Interacting with SQLite and PostgreSQL Databases
- Introduction to Working with Databases - 3:04
- Connecting and Inserting Data to SQLite via Python - 13:11
- Selecting, Inserting, Deleting, and Updating SQLite Records - 6:58
- Introduction to PostgreSQL Psycopg2 - 8:46
- Selecting, Inserting, Deleting, and Updating PostgreSQL Records - 12:53
Application 5: Building a Desktop Database Application
- Demonstration of the Database Application - 2:25
- User Interface Design - 5:54
- Building the Front-end Interface - 27:00
- Building the Back-end - 24:28
- Connecting the Front-end to the Back-end, Part 1 - 17:31
- Connecting the Front-end to the Back-end, Part 2 - 21:59
- Creating a Standalone Executable Version of the Program - 5:00
Object Oriented Programming
- Object Oriented Programming Explained - 4:59
- Turning this Application into OOP Style, Part 1 - 13:01
- Turning this Application into OOP Style, Part 2 - 14:06
- Creating a Bank Account Object - 21:06
- Inheritance - 12:08
- OOP Glossary - 8:12
Python for Image and Video Processing with OpenCV
- Introduction - 2:29
- Installing OpenCV for Python - 2:48
- Loading, Displaying, Resizing, and Writing Images with Python - 14:00
- Face Detection - 19:38
- Capturing Video - 19:45
Application 6: Building a Webcam Motion Detector
- Demonstration of the Motion Detector Application - 1:59
- Detecting Objects from the Webcam - 30:20
- Recording Motion Time - 20:38
Python for Interactive Data Visualization on the Browser
- Introduction to Bokeh - 2:02
- The Bokeh Charts Interface - 10:52
- The Bokeh Plotting Interface - 8:03
- Customizing Pot Styles - 5:16
- Understanding the Structure Behind the Graphs - 5:45
- Time-series Plots - 6:18
- More Visualization Examples with Bokeh - 4:21
- Plotting Time Intervals of the Motion Detector - 14:05
- Hover Tool Implementation - 9:57
Webscraping
- Section Introduction - 1:57
- The Concept Behind Webscraping - 4:30
- Scraping a Webpage with Requests and BeautifulSoup - 16:22
Application 7: Scraping Real Estate Property Data
- Demonstration of the Webscraping Application - 2:28
- Understanding the Problem and Loading the Webpage in Python - 7:15
- Extracting Divisions of All Properties - 11:34
- Extracting Addresses and Property Details - 14:39
- Extracting Elements with no Unique Identifiers - 12:07
- Saving the Extracted Data in CSV Files - 8:27
- Crawling Through Webpages - 17:15
Application 8: Building a Web-based Financial Graph
- Demonstration of the Financial Analysis Application - 1:59
- Downloading Various Datasets with Python - 11:31
- Understanding Stock Market Data - 3:25
- Understanding Stock Market Data Candlestick Charts - 5:39
- Building Chart Candlesticks with Bokeh Quadrants - 10:13
- Building Chart Candlesticks with Bokeh Rectangles - 22:28
- Building Candlestick Segments - 5:02
- Stylizing the Chart - 4:21
- The Concept Behind Embedding a Bokeh Chart in a Webpage - 11:04
- Embedding the Bokeh Chart in a Webpage - 15:33
- Deploying the Chart Website to a Live Server - 8:22
Application 9: Building a Data Collector Web App
- Demonstration of the Web Application - 2:59
- Steps for Building a PostgreSQL Database-enabled Web Application - 6:08
- Building the Front-end: HTML Part - 14:52
- Building the Front-end: CSS Part - 10:11
- Building the Back-end: Getting User Input - 17:31
- Building the Back End: Creating the PostGreSQL Database Model - 18:17
- Building the Back End: Storing User Data to the Database - 19:14
- Building the Back End: Emailing Database Values Back to the User - 11:14
- Building the Back End: Sending Statistics to Users - 16:00
- Deploying the Web Application to a Live Server - 29:31
- Bonus Lecture: User Downloads and Uploads - 20:51
Application 10: Student Project on Building a Geocoder Web Service
- Demonstration of the Geocoding Web Service Application and Project Requirements - 7:31
- Solution, Part 1 - 16:21
- Solution, Part 2 - 5:51
- End of the Course - 0:47

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46

The Deep Learning Masterclass: Classifying Images with Keras

Catalyze Your Foray Into AI by Building a Model That Classifies Images

By Mammoth Interactive | in Online Courses

A subset of machine learning, deep learning focuses on how machines use neural networks to learn from data. These neural networks are used to perform tasks and are adjusted to better the outcome each time, paving the way for groundbreaking machines that learn on their own! This master class takes you through machine learning, neural networks, and several core tools, like Keras, TensorFlow, and Python as you work toward creating a model that can classify images.

Access 46 lectures & 6 hours of content 24/7
Walk through the essentials for using Python, Keras, TensorFlow & more machine learning tools
Expand your understanding of machine learning, neural networks & convolutions
Dive into creating your own image classifier model from scratch

Instructor

Bucky Roberts is a computer programmer and web developer. He started working for Google in 2008 and also has a YouTube channel "thenewboston" that has over 800,000 subscribers. He makes tutorials on computer programming, Adobe software, computer science and many other topics!

Important Details

Length of time users can access this course: lifetime
Access options: web and mobile streaming
Certification of completion included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels

Requirements

Internet required

Course Outline

DAY 1: Learn to Use PyCharm
- 00. Bootcamp Intro - 5:42
- 00. Intro to PyCharm - 3:55
- 01. Downloading and Installing - 9:28
- 02. Exploring PyCharm Interface - 8:32
- 03. Add and Run Python Files - 7:25
- 04. Building and Running a Simple Program - 10:05
DAY 2: Learn Python Language Basics
- 00. Introduction - 5:13
- 01. Variables Syntax And Basic Types - 8:33
- 02. Variable Operations - 9:29
- 03. Tuples and Lists - 11:54
- 04. Dictionaries - 6:36
- 05. If Statements - 10:03
- 06. While and For In Loops - 10:43
- 07. Function Implementation and Execution - 10:05
- 08. Parameters and Return Values - 7:47
- 09. Intro to Classes and Objects - 12:40
- 10. Subclasses and Superclasses - 13:06
- 11. Summary and Outro - 3:37
DAY 3: Understand Machine Learning Neural Networks
- 00. Intro to Day 3 - 2:01
- 01. Intro to Machine Learning - 11:23
- 02. Intro to Neutral Networks - 10:23
- 03. Intro to Convolutions - 14:10
DAY 4: Explore the Keras API
- 00. Intro to Day 4 - 1:49
- 01. Intro To TensorFlow And Keras - 9:06
- 02. Understanding Keras Syntax - 19:13
- 03. Intro to Activation Functions - 13:26
DAY 5: Format Datasets and Examine CIFAR-10
- 00. Intro to Day 5 - 1:53
- 01. Exploring CIFAR10 Dataset - 8:36
- 02. Understanding Specific Data Points - 17:43
- 03. Formatting Input Images - 12:04
DAY 6: Build the Image Classifier Model
- 00. Intro to Day 6 - 2:23
- 01. Building the Model - 18:18
- 02. Compiling and Training the Model - 12:38
- 03. Gradient Descent and Optimizers - 14:50
DAY 7: Save and Load Trained Models
- 00. Intro to Day 7 - 2:08
- 01. Saving and Loading Model to H5 - 15:20
- 02. Saving Model to Protobuf File - 17:50
- 03. BootCamp Summary - 5:40
Source Material
- Source Code: Learn Python Language Basics
- Texts Assets: Understand Machine Learning Neural Networks
- Texts Assets: Explore the Keras API
- Asset Files: Format Datasets and Examine CIFAR-10
- Asset Files: Build the Image Classifier Model
- Asset Files: Save and Load Trained Models

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Access

Lifetime

Content

3 hours

Lessons

22

Image Processing with Python

Develop a Photo Filter Editor From Scratch

By ZENVA | in Online Courses

In this course, you’ll build a photo filter editor which allows you to create filters such as those used in Instagram and Snapchat. This app allows you to load a photo, edit its contrast, brightness, and grey-scale. You can also create and apply custom filters using this tool. Build the filters you love! What's cooler than that?

Access 22 lectures & 3 hours of content 24/7
Learn about matrices, color models, brightness, contract, & convolution
Install the Anaconda development environment
Explore matrix operations & OpenCV

Instructor

Pablo Farias Navarro is a software developer and founder of ZENVA. Since 2012, he has been teaching online how to create games, apps and websites to over 150,000 students through the Udemy and Zenva Academy platforms, and created content for companies such as Amazon and Intel.

Pablo is a member of the Intel Innovator Program in the Asia Pacific, and has run live programming workshops in San Francisco, Brisbane and Bangalore. Pablo holds a Master in Information Technology (Management) degree from the University of Queensland (Australia) and a Master of Science in Engineering degree from the Catholic University of Chile.

Important Details

Length of time users can access this course: lifetime
Access options: web streaming, mobile streaming
Certification of completion included
Redemption deadline: redeem your code within 30 days of purchase
Experience level required: all levels

Requirements

Internet required

Course Outline

Introduction
- Introduction - 2:44
- Source Files
- Anaconda Development Environment - 8:35
Project
- Images - 7:45
- Matrices - 9:10
- Overview - 3:04
- Creating the UI - Part 1 - 20:12
- Creating the UI - Part 2 - 5:57
- Matrix Addition and Subtraction - 6:54
- Constraints on Matrix Addition and Subtraction - 3:22
- Scalar Multiplication - 7:23
- Color Models - 12:03
- Grayscale - 9:47
- Brightness and Contrast - 14:09
- Brightness and Contrast Filters - 6:57
- Kernels - 8:24
- Intro to Convolution - 9:21
- Convolution Filters - 19:21
- Save Functionality - 6:13
Conclusion
- Conclusion - 1:26
- Convolution Example (Optional) - 9:18
- Convolution Detailed Example (Optional) - 8:34

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