The Complete Python 3 Masterclass – From Beginner To Pro

The Complete Python 3 Masterclass – From Beginner To Pro
The Complete Python 3 Masterclass – From Beginner To Pro
English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 27 Hours | 12.8 GB

Python for beginners, Learn Python Programming and build your own Python programs: Data Science, Machine Learning, etc.

This course makes you a professional Python 3 developer- no matter if you are a Python beginner, have already programmed a bit, or just want to discover the advanced features of Python 3. It is the ideal Python for beginners course.

This course contains over 250 lessons, countless quizzes, tests, practical projects, fact sheets, and exercises – the easiest way if you want to become a Python 3 developer.


First a small overview of what this course offers you:

  • Understand the basics of Python 3 development
  • Understand object orientation
  • Develop your own applications in Python 3 development
  • Challenge your knowledge with various quizzes and exercises
  • Easy repetition of knowledge: Extensive fact sheets

Various practical examples:

  • Extract data from web pages with a web crawler
  • Write real desktop applications with Qt
  • Develop a web server with Flask
  • Apply Data Analysis with Python 3
  • Develop a face detection software
  • Develop a simple machine learning application
  • And much more…

Python 3 is an incredibly cool programming language that can be used for various purposes. Therefore, it is not only important that you learn Python yourself, but also which tools complement Python perfectly. And that’s why I developed this Python for beginners course – to make you a highly skilled in Python development, no matter what direction you want to go after the course.

After completing this Python for beginners course you can write desktop applications, master Python website development, extract data from other websites, collect additional data via cool forms, and evaluate it automatically with Python – the complete workflow.

To this end, we will look at various additional tools in the course:

  • The Data Science – Stack: Numpy, Pandas and Matplotlib
  • Web – Crawling with “requests” and “beautifulsoup”
  • Web – Development with Flask
  • Interactive programs with Jupyter notebooks
  • Desktop applications with PyQt

My goal is that after completing this Python for beginners course you will be very good at Python development – no matter what purpose you want to use Python for later. This makes you perfectly prepared for future jobs and projects.

Frameworks allow you to save loads and loads of effort and time, when building your programs.

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series for maximum Python development capabilities.

Build a web crawler with “requests” and “beautifulsoup” which allows you to read data from a website and reuse it how ever you like.This is a super powerful tool, which comes in handy in so many occasions.

With Flask you can build websites with Python. Flask is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries. So you can get started with it super fast.

In this Python for beginners course you will also learn how to use PyQt. With PyQt you can build beautiful GUIs (Graphical User Interfaces). Either with code, or by just using the Qt Creator, which allows you to drag and drop your UI elements onto your Window. That is not only convenient, but also decreases the development time, required to build desktop applications.

Machine learning is a super interesting and demanded area. Python is the programming language to go with, when it comes to Machine Learning. So hop aboard and ride with the Python hype train. As you will learn the basics of machine learning with Python in this Python for beginners course. At the end you are skilled in Python development to the highest degree.


First you learn, how the very basics of Python work. This includes variables, functions, loops, if statements, etc.

Then you learn how to use object oriented programming. These skills whill not only help you with Python programming, but also with other programming Languages. As soon as you feel confortable with those skills, you will learn more advanced topics and build more and more interesting and real world applications such as a web crawler, a twitter bot, a website with flask, data science with numpy, pandas and matplotlib.Each chapter contains multiple quizzes, leaflets/handouts and exercises, which help you to progress steadily.

So you see, the course will make you a complete Python developer in no time.

Table of Contents

1 Introduction
2 Download the course material here AND DISCORD
3 Install required tools and Hello World
4 FAQ – Frequently Asked Questions

Python Basics
5 Intro
6 Important note
7 Numbers in Python
8 Variables in Python
9 Strings in Python
10 [skip if solved] Sample solution Quiz: Number or String

Python Bascis Part 2
11 Lists in Python
12 [skip if solved] Sample solution Quiz: Lists in Python
13 Lists – pop function
14 Handout: Lists in Python
15 Motivation
16 Convert Data 1
17 Convert Data 2
19 [skip if solved] Sample solution Quiz: Comments
20 For your reading
21 Exercise Sheet
22 [skip if solved] Sample solution Python Basics

Control Structures
23 Intro
24 if, else
25 Comparison Operators
26 Connect Expressions And Booleans
27 Leaflet: Comparison Operators
28 [skip if solved] Sample solution Quiz: If & Booleans
29 The In Operator
30 The Not Operator
31 Leaflet: Operators and Lists
32 [skip if solved] Sample solution Quiz: The Not Operator
33 elif

Control Structures Part 2
34 While Loop
35 For Loop
36 When To Use Which Loop?
37 [skip if solved] Sample solution Quiz: In, Loops,…
38 Loops (break, continue)
39 Excursus: How to Handle Problems
40 Project: Trump Twitter Bot
41 Leaflet: Loops
42 Challenge Sheet Control Structures
43 [skip if solved] Sample solution Challenge Control Structures

44 Intro
45 Functions
46 Functions 2
47 Outlook – Objects
48 Intro: Opening a File
49 Open a File
50 Write a File
51 File And the With Keyword
52 Open CSV
53 Read And Skip Rows in CSV
54 Excursus: Drawing Graphics
55 Info: Birth statistics
56 Birth Statistics
57 Challenge Birthstatistics
58 [skip if solved] Sample solution Challenge Birth Statistics
59 Leaflet: Functions and Methods
60 Challenge Paper – Functions
61 Tips on how to solve Functions Challenge
62 [skip if solved] Sample solution Challenge Functions

Lists in Python
63 Lists Intro
64 Working with Lists
65 List Slicing
66 [skip if solved] Sample solution Quiz: List Slicing
67 List Comprehension
68 Outlook – Tuple & Dictionaries
69 Dictionaries
70 [skip if solved] Sample solution Quiz: Dictionaries
71 Tupel
72 Packing and Unpacking of Tuples
73 Dictionaries and Loops
74 Challenge: Dictionaries & Loops
75 Tips: Dictionaries & Loops
76 [skip if solved] Sample solution Challenge Dictionaries & Loops
77 Nest Data Structures
78 Leaflet: Dictionaries, Lists and Tuples

Object Oriented Programming
79 Intro
80 Class and Method
81 Constructor and Methods
82 Why Private Properties and Methods?
83 Private Properties and Methods
84 Why do We Encapsulate Data?
85 Special Methods
86 Inheritance in Python
87 Challenge: Inheritance and Object Orientation
88 [skip if solved] Sample solution Challenge: Inheritance and Object Orientation
89 Check Types Of Variables
90 In Python Everything is an Object
91 How to name Variables, Classes and Methods
92 Static Variables
93 Leaflet: Object Orientation
94 Challenge: Object Orientation
95 [skip if solved] Sample solution Challenge: Object Orientation

Module in Python
96 Intro
97 Load Code from a seperate File
98 Different Ways to Import Modules
99 Create Modules in a Folder
100 Leaflet: Module in Python
101 [skip if solved] Sample solution Quiz: Modules
102 Example of a Module

103 Intro
104 Excursus: What is HTML?
105 Get HTML Code
106 beautifulsoup
107 Excursus: CSS-Selectors 1
108 Excursus: CSS-Selectors 2
109 Find Elements 1
110 Find Elements 2
111 Find Elements – urllib
112 Challenge: Crawl Multiple Pages
113 Solution: Crawl Multiple Pages
114 Note: Encoding, Writing a File
115 Challenge: Store Results as CSV
116 Solution: Store Results as CSV
117 Open CSV File with Excel
118 Generators
119 Use Generators with a Crawler
120 Install PyCharm and Make a Real Program from our Crawler
121 Make a Real Program from our Crawler

122 Intro
123 Exceptions
124 Create Multiple and your own Exceptions
125 The finally Construct
126 The With Construct
127 Leaflet: Exceptions
128 Challenge: Exceptions
129 [skip if solved] Sample solution Challenge: Exceptions

Data Structures in Python
130 Overview: What are Data Structures?
131 Data Structure: Set
132 Challenge: Sets
133 Sample Solution: Sets
134 Sample Solution: Quiz Data Structures
135 Data Structure: queue
136 Data Structure: priority queue
137 Overview: Data Strcutures
138 Challenge: Priority Queue
139 Sample Solution: Priority Queue
140 Relevant Readings

Miscellaneous (1) – Functions Parameters and Sorting
141 Intro
142 Excursus name Functions Parameters
143 Excursus – How to pass a Functions Argument
144 Pass Variable Amount of Arguments
145 Pass Variable Amount of Arguments Part 2
146 Leaflet: Pass Variable Amount of Arguments
147 Sort Data
148 Lambda Functions
149 User-Tipp: Regular Expressions
150 Use Regular Expressions
151 Exercise: Functions Parameters and Lambda Functions
152 Sample Solution: Functions Parameters and Lambda Functions

Miscellaneous (2) – The datetime Module and String Formatting
153 Working With Dates And Their Values
154 Format Date Entries And Read Them
155 TimeDelta
156 Leaflet: Dates in Python
157 Sample Solution: Date Functions
158 Enhanced String Functions
159 Leaflet: Strings
160 Format Strings
161 Leaflet: Format Strings
162 Sample Solution: Quiz String Functions
163 DefaultDict

Miscellaneous (3) – Working with Pycharm
164 Outlook, What To Expect In This Chapter
165 Python Programs With PyCharm
166 Start Python Programs With Terminal
167 Start Python Programs With Terminal (Windows)
168 Start Python Programs via Shortcut (Windows)
169 The input() Function
170 Challenge BMI Calculator
171 Solution: BMI Calculator
172 sys.argv in Python
173 Working with Folders
174 Os Path Join With Multiple Parameters and Dot Dot
175 Charset UTF-8
176 Open Files With UTF-8
177 Challenge – How Many Max’s?
178 Sample Solution: How Many Max’s

Jupyter Notebooks
179 Intro
180 Jupyter As IDE
181 Jupyter Notebooks Markdown
182 Using Mathematical Expressions in Jupyter Notebooks
183 Problem: Jupyter Widget Not Displaying?
184 Jupyter Widgets
185 Interact With Jupyter Notebooks
186 Challenge: Jupyter Widgets
187 Excursus: Write CSV Files
188 Sample Solution: Jupyter Widgets

189 Intro
190 Note
191 Excursus HTML 1
192 Excursus HTML 2
193 Excursus CSS 1
194 Excursus CSS 2
195 Info about Course Materials
196 Hello World in Flask
197 Generate HTML-Code with Flask
198 Static
199 Template
200 Template Inheritance
201 Style our Navigation
202 Pass URL-Parameter
203 Submit Formulars per GET
204 Fill Input Field
205 Solution: Fill Input Field
206 Pass Classes and Dictionaries to a View
207 Pass Tuple to View
208 View vs Python – File for Logic
209 Challenge: Exchange Rates
210 Info – dictionary.get
211 Excursus HTML – How to create a Table
212 Excursus HTML – How To Split Your Page
213 Sample Solution 1
214 Sample Solution 2
215 Sample Solution 3
216 Decorators 1
217 Decorators 2
218 Decorators 3
219 Flask – What’s next?

The Data Science Stack
220 Intro
221 Numpy Intro
222 Filter Numpy Arrays
223 Numpy and Multi Dimensional Arrays
224 How Does Numpy Actually Work?
225 Pandas – Intro
226 Pandas – Filter Data
227 Pandas – Sort Data
228 Pandas – Draw Graphic Based On Excel Data
229 Challenge: Pandas
230 Solution: Pandas
231 Matplotlib intro
232 Matplotlib graphic – parameters
233 When To Use Which Diagram Type?
234 Different Diagram Types
235 Overview: The Datascience- Stack
236 [skip if solved] Sample solution Quiz: Data Science – Stack
237 Machine Learning
238 Machine Learning With Linear Regression

Image Processing with OpenCV
239 OpenCV Intro
240 Installing OpenCV
241 Load Image To OpenCV
242 Color Presentation And OpenCV
243 How Are Numbers Optimised And Stored Internally In OpenCV
244 Increase Brightness 1
245 Increase Brightness 2
246 Draw A Rectangle
247 Face Detection

248 QT Intro
249 Installing qt and pyqt and qtpy
250 First Desktop Application with pyqt
251 Build UI with drag and drop
252 Qt Creator 1
253 Qt Creator 2
254 Qt Creator 3
255 Concept: Signals and Slots
256 Signals and Slots
257 Signals and Slots Python
258 Restructor Python Code
259 Read Input and access Properties
260 Build BMI Calculator 1
261 Challenge: Build BMI Calculator
262 Sample Solution BMI Calculator
263 Naming Functions and Variables
264 Datatype Conversion pyqt – cpp
265 Introduction to Tables – Widget
266 The Table – Widget 2
267 Configure Properties based on Table Widget
268 Documentation and reacting to Cell Entries
269 Challenge – Read CSV, Display and store
270 Hints for – Challenge – Read CSV, Display and store Data
271 Sample Solution – Challenge – Read CSV, Display and store 1
272 Sample Solution – Challenge – Read CSV, Display and store 2
273 Sample Solution – Challenge – Read CSV, Display and store 3
274 Sample Solution – Challenge – Read CSV, Display and store 4
275 Add a Menu
276 Finish