Learn Pro Advanced Python Programming

Become a Professional Python Programmer by learning Advance Level Programming & using them to make real Applications
Learn Pro Advanced Python Programming
File Size :
10.43 GB
Total length :
26h 22m

Category

Instructor

Data Scientist

Language

Last update

11/2020

Ratings

4/5

Learn Pro Advanced Python Programming

What you’ll learn

Advance Level Programming in Python
Make real time advance applications using Advance Level Concepts
Make Machine Learning Models
Use Machine Learning Models to make real time Applications
Learn the Mathematics behind Machine Learning Models

Learn Pro Advanced Python Programming

Requirements

Basic Knowledge of Python or any programming Level is required

Description

In this course, I am going to make you a professional programmer by teaching you Advance Level Programming in Python. The Basic of any programming language is not enough to make real time applications therefor, i have covered most of the Advance Level Concepts in depth in this course. As grabbing the main concept behind Advance Topics is not simple therefor, special attention is given to the intuition part of each concept where we gonna understand these concepts with proper animated slides.Also not only understanding these advance concepts are important but to make something real out of it is very important or else there is no reason to learn Advance Programming therefor we will also make real time Advance level Applications in Python using Advance level concepts. We will also learn Machine Learning in Python in depth by covering the Mathematics behind each model as well. Also we will use these Machine Learning Models to make something real out of it.I believe that after taking this course, you gonna feel much more satisfied and comfortable with your programming skill in Python as you will then be a professional programmer who is capable to give any job interview.. Also after taking this course, learning any Advance Level concept in any other language will be 10x more simpler.I wish you very best for the Course.

Overview

Section 1: Python Comprehension

Lecture 1 Section Overview

Lecture 2 Introduction to Comprehension

Lecture 3 List Comprehension Part-1

Lecture 4 List Comprehension Part-2

Lecture 5 List Comprehension vs Lambda Function

Lecture 6 Parsing a File Using List Comprehension

Lecture 7 Accessing Function using List Comprehension

Lecture 8 Dictionary Comprehension

Lecture 9 Set Comprehension

Lecture 10 Generator Comprehension

Section 2: Descriptors in Python

Lecture 11 Section Overview

Lecture 12 Introduction to Descriptors

Lecture 13 Invoking Descriptors

Lecture 14 Purpose of Descriptors Part-1

Lecture 15 Purpose of Descriptors Part-2

Lecture 16 Creating Descriptors using Property

Lecture 17 Creating Descriptors using Class

Lecture 18 Creating Descriptors using @

Lecture 19 Uses of Descriptors

Section 3: Linked List in Python

Lecture 20 Section Overview

Lecture 21 Singly and Doubly Linked List

Lecture 22 Intro to Linked Lists

Lecture 23 Create and Traverse – Singly Linked List

Lecture 24 Insertion – Singly Linked List

Lecture 25 Deletion – Singly Linked List

Lecture 26 Creating – Doubly Linked List

Lecture 27 Insertion – Doubly Linked List

Lecture 28 Appending – Doubly Linked List

Lecture 29 Deletion – Doubly Linked List

Section 4: OpenCV – Image/Video Processing in Python

Lecture 30 Installing the OpenCV Module

Lecture 31 Showing the Image

Lecture 32 Keyboard Events in OpenCV

Lecture 33 Drawing Different Shapes on Images

Lecture 34 Capturing Video with OpenCV

Lecture 35 Writing the Video

Lecture 36 Different Video Properties

Lecture 37 Events

Lecture 38 Implementing Mouse Events

Lecture 39 Image Thresh holding

Lecture 40 Object Detection with OpenCV

Section 5: Generators in Python

Lecture 41 Understanding Generators

Lecture 42 Creating Generator – Part-1

Lecture 43 Creating Generator – Part-2

Lecture 44 Generator – Example

Section 6: Managing Files in Python

Lecture 45 Section Overview

Lecture 46 Introduction to File Handling

Lecture 47 Creating a Text File

Lecture 48 Modes in Files

Lecture 49 Modes for other File Types

Lecture 50 Reading from a Text File

Lecture 51 Renaming a Text File

Lecture 52 Writing to a Text File

Lecture 53 Saving Data to a File

Lecture 54 Appending to a File

Lecture 55 Closing a File

Lecture 56 Deleting a File

Lecture 57 With Statement

Lecture 58 Saving Dictionary to a File

Section 7: Magic Functions in Python

Lecture 59 Section Overview

Lecture 60 Introduction to Magic Methods

Lecture 61 Various Magic Methods

Lecture 62 Magic Methods based on Object Usefulness

Lecture 63 Initialization and Construction

Lecture 64 Unary Operators – Magic Methods

Lecture 65 Strings – Magic Methods

Lecture 66 Operators – Magic Methods

Lecture 67 Augmented Assignment Operator

Lecture 68 Magic Methods for Binary Operators

Section 8: Thread Programming in Python

Lecture 69 Section Overview

Lecture 70 Concurrency vs Parallelism

Lecture 71 Multiprocessing vs Multi-Threading

Lecture 72 Introduction to Thread

Lecture 73 The Threading Module

Lecture 74 Creating a Thread

Lecture 75 Determining the Current Thread

Lecture 76 Daemon vs non_Daemon

Lecture 77 Enumerating Threads

Section 9: The Arcade Module

Lecture 78 Introduction to Arcade Module

Lecture 79 Sad Images – Arcade Module

Lecture 80 Still Images – Arcade Module

Lecture 81 Auto Timer – Arcade Module

Lecture 82 Auto Snow – Arcade Module

Lecture 83 Auto Radar – Arcade Module

Lecture 84 User Controlled Moving Object – Arcade Module

Section 10: PDF Audio Reader Application

Lecture 85 PDF Audio Reader Application Part-1

Lecture 86 PDF Audio Reader Application Part-2

Lecture 87 PDF Audio Reader Application Part-3

Lecture 88 PDF Audio Reader Application Part-4

Lecture 89 PDF Audio Reader Application Part-5

Lecture 90 PDF Audio Reader Application Part-6

Lecture 91 PDF Audio Reader Application Part-7

Lecture 92 PDF Audio Reader Application Part-8

Lecture 93 PDF Audio Reader Application Part-9

Section 11: Email Sender Application

Lecture 94 Email Sender Application Part-1

Lecture 95 Email Sender Application Part-2

Lecture 96 Email Sender Application Part-3

Lecture 97 Email Sender Application Part-4

Lecture 98 Email Sender Application Part-5

Lecture 99 Email Sender Application Part-6

Lecture 100 Email Sender Application Part-7

Lecture 101 Email Sender Application Part-8

Lecture 102 Email Sender Application Part-9

Lecture 103 Email Sender Application Part-10

Section 12: Graphs in Python

Lecture 104 Understanding Graphs

Lecture 105 Installing the Module for Graphs

Lecture 106 Drawing a Simple Line Graph

Lecture 107 Drawing a Multi-Line Graph

Lecture 108 Drawing a Bar Graph

Lecture 109 Styling a Bar Graph

Lecture 110 Drawing a Scatter Graph

Lecture 111 Drawing a Pie Graph

Lecture 112 Drawing a Histogram

Section 13: Text Editor Application in Python

Lecture 113 Text Editor – Introduction

Lecture 114 File Cascade

Lecture 115 Edit Cascade

Lecture 116 Insert and Format Cascade

Lecture 117 Personalize and Help Cascade

Lecture 118 Adding Scrollbar

Lecture 119 Revision of Back End

Lecture 120 New File Function

Lecture 121 Open, Save and Close a File

Lecture 122 Copy, Cut and Paste

Lecture 123 Erase and Clear Screen

Lecture 124 Data, Text Color and No Format

Lecture 125 Bold, Italic and Underline

Lecture 126 Background and Help

Lecture 127 Summary of the Code

Section 14: Numerical Python

Lecture 128 Understanding Numerical Python

Lecture 129 Installing the Numpy Module

Lecture 130 Creating a 1D Array using Numpy

Lecture 131 Creating 2D Array using Numpy

Lecture 132 Arithmetic Operations on Array

Lecture 133 Useful Matrices with Numpy

Lecture 134 Computing scientific Functions using Numpy

Lecture 135 Computing Mathematical Functions using Numpy

Lecture 136 Making Complex Graphs using Numpy

Section 15: Data Analysis With Pandas Module

Lecture 137 Pandas Module Part-1

Lecture 138 Pandas Module Part-2

Lecture 139 Pandas Module Part-3

Lecture 140 Pandas Module Part-4

Lecture 141 Pandas Module Part-5

Lecture 142 Pandas Module Part-6

Lecture 143 Pandas Module Part-7

Lecture 144 Pandas Module Part-8

Lecture 145 Pandas Module Part-9

Lecture 146 Pandas Module Part-10

Lecture 147 Pandas Module Part-11

Lecture 148 Pandas Module Part-12

Lecture 149 Pandas Module Part-13

Section 16: Data Sets for the Remaining Sections

Lecture 150 Data Sets for the Remaining Sections

Section 17: Introduction to Machine Learning | Understanding our 1st Machine Learning Model

Lecture 151 What is Machine Learning

Lecture 152 The Simple Linear Regression Model Intuition – 1

Lecture 153 The Simple Linear Regression Model Intuition – 2

Lecture 154 The Simple Linear Regression Model Intuition – 3

Lecture 155 The Simple Linear Regression Model Code Part-1

Lecture 156 The Simple Linear Regression Model Code Part-2

Lecture 157 The Simple Linear Regression Model Code Part-3

Lecture 158 The Simple Linear Regression Model Code Part-4

Lecture 159 The Simple Linear Regression Model Code Part-5

Lecture 160 The Simple Linear Regression Model Code Part-6

Lecture 161 The Simple Linear Regression Model Code Part-7

Section 18: The Multiple Linear Regression Model

Lecture 162 Multiple Linear Regression Intuition 1

Lecture 163 Multiple Linear Regression Intuition 2

Lecture 164 Multiple Linear Regression Intuition 3

Lecture 165 Multiple Linear Regression Intuition 4

Lecture 166 Multiple Linear Regression Intuition 5

Lecture 167 Multiple Linear Regression Intuition 6

Lecture 168 Multiple Linear Regression Code Part-1

Lecture 169 Multiple Linear Regression Code Part-2

Lecture 170 Multiple Linear Regression Code Part-3

Lecture 171 Multiple Linear Regression Code Part-4

Lecture 172 Multiple Linear Regression Code Part-5

Lecture 173 Multiple Linear Regression Code Part-6

Lecture 174 Multiple Linear Regression Code Part-7

Anyone curious to learn Advance Level Programming,Anyone curious to make Advance Level Applications,Anyone Curious about Machine Learning Models

Course Information:

Udemy | English | 26h 22m | 10.43 GB
Created by: Data Scientist

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