Python Programming Tutorial - Full Course for Beginners

Watch this Python tutorial for beginners to learn Python programming from scratch πŸ‘

Watch this Python tutorial for beginners to learn Python programming from scratch. πŸ‘

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Further reading

☞ Complete Python Bootcamp: Go from zero to hero in Python 3

☞ Machine Learning A-Zβ„’: Hands-On Python & R In Data Science

☞ Python and Django Full Stack Web Developer Bootcamp

☞ Complete Python Masterclass

☞ Best Python IDEs for Data Science

☞ Top 10 Python Frameworks for Web Development In 2019

☞ A REAL Python cheat sheet for beginners

Python Tutorial for Beginners (2019) - Learn Python for Machine Learning and Web Development

Python Tutorial for Beginners (2019) - Learn Python for Machine Learning and Web Development


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TABLE OF CONTENT

00:00:00 Introduction

00:01:49 Installing Python

00:06:10 Your First Python Program

00:08:11 How Python Code Gets Executed

00:11:24 How Long It Takes To Learn Python

00:13:03 Variables

00:18:21 Receiving Input

00:22:16 Python Cheat Sheet

00:22:46 Type Conversion

00:29:31 Strings

00:37:36 Formatted Strings

00:40:50 String Methods

00:48:33 Arithmetic Operations

00:51:33 Operator Precedence

00:55:04 Math Functions

00:58:17 If Statements

01:06:32 Logical Operators

01:11:25 Comparison Operators

01:16:17 Weight Converter Program

01:20:43 While Loops

01:24:07 Building a Guessing Game

01:30:51 Building the Car Game

01:41:48 For Loops

01:47:46 Nested Loops

01:55:50 Lists

02:01:45 2D Lists

02:05:11 My Complete Python Course

02:06:00 List Methods

02:13:25 Tuples

02:15:34 Unpacking

02:18:21 Dictionaries

02:26:21 Emoji Converter

02:30:31 Functions

02:35:21 Parameters

02:39:24 Keyword Arguments

02:44:45 Return Statement

02:48:55 Creating a Reusable Function

02:53:42 Exceptions

02:59:14 Comments

03:01:46 Classes

03:07:46 Constructors

03:14:41 Inheritance

03:19:33 Modules

03:30:12 Packages

03:36:22 Generating Random Values

03:44:37 Working with Directories

03:50:47 Pypi and Pip

03:55:34 Project 1: Automation with Python

04:10:22 Project 2: Machine Learning with Python

04:58:37 Project 3: Building a Website with Django


Thanks for reading ❀

If you liked this post, share it with all of your programming buddies!

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Further reading

☞ Complete Python Bootcamp: Go from zero to hero in Python 3

☞ Machine Learning A-Zβ„’: Hands-On Python & R In Data Science

☞ Python and Django Full Stack Web Developer Bootcamp

☞ Complete Python Masterclass

☞ Python Programming Tutorial | Full Python Course for Beginners 2019 πŸ‘

☞ Top 10 Python Frameworks for Web Development In 2019

☞ Python for Financial Analysis and Algorithmic Trading

☞ Building A Concurrent Web Scraper With Python and Selenium

Using Scikit-Learn for Machine Learning Application Development in Python

Using Scikit-Learn for Machine Learning Application Development in Python

Using Scikit-Learn for Machine Learning Application Development in Python - Python is arguably the best programming language for machine learning. However, many aspiring machine learning developers don’t know where to start...

Using Scikit-Learn for Machine Learning Application Development in Python - Python is arguably the best programming language for machine learning. However, many aspiring machine learning developers don’t know where to start...

They should look into the scikit-learn library, which is one of the best for developing machine learning applications. It is free and relatively easy to install and learn.

Why Machine Learning Programmers Should Be Familiar With Scikit-Learn

If you are trying to develop machine learning applications, then you were going to need a robust toolkit. Scikit-learn is just the solution that you need. This library was developed in 2007 as part of a Google project. Three years later, the code was released as hey solution for machine learning algorithms in conjunction with Google and several other major companies.

Scikit-learn is a library that contains several implementations of machine learning algorithms. There are two essential classifiers for developing machine learning applications with this library: a supervised learning model known as an SVM and a Random Forest (RF).

There are numerous reasons that scikit-learn is one of the preferred libraries for developing machine learning solutions. Some of the Premier benefits include:

  • Regression modeling
  • Unsupervised classification and clustering
  • Decision tree pruning and induction
  • Comprehensive and neural network training with regression and classification algorithms
  • Decision boundary learning with SVMs
  • Advanced probability modeling
  • Feature analysis and selection
  • Reduction of dimensionality
  • Outlier detection and rejection

Scikit-learn has been used in a number of applications by J.P. Morgan, Spotify, Inria, and other major companies. Machine learning applications built with scikit-learn include financial cybersecurity analytics, product development, neuroimaging, barcode scanner development, medical modeling and help with handling Shopify inventory issues.

The wide range of decision modeling features makes scikit-learn. One of the most versatile machine learning environments available in any programming language. Intermediate and advanced Python programmers should be able to master the nuances of this sophisticated library in a matter of hours.

The scikit-learn library is not installed by default. Fortunately, you should be able to set it up quickly. Here are some guidelines for installation and creating the foundation for your first machine learning project.

Installation of Scikit-Learn

If you already have pip installed, it's very easy to install the scikit-learn library. The instructions are available on this page.

Data for Audio

The purpose of using classification is to create a model based on the representation of a phenomenon in vector form (i.e. as a vector) and its corresponding class. This model will then be used to assign a class to an unknown vector. MFCCs can be used for approximations of sound vectors. MFCC provides 13 values per window. One option is to try classifying the class of a sound using those values. However, the sequences of the sound are very important.

This approach resolves some vector problems. The first approach we can follow is to take a segment of MFCCs and average them. Rather than having 13 values for the size of the segment, we end up with thirteen values. Averaging them is very simple, but we can get other statistics, such as: standard deviations and quartiles. This strategy provides statistical representations of all variables.

Loading Data From a CSV File

You will save your scikit-learn data in CSV files. Each line represents a line and each regular column represents a dimension of the vector. In general, the latter represents the class. Rows are separated by a line break and columns by a column. An illustrative example would be as follows:

#!events
event_1,event_2, event_label
1,2,3
11.1,1221,11341
1322,1422,320
330,222,121

To upload a file you can execute the following code:

import numpy as np
.loadtxt('scikit_1.csv',)
data.shape

At the end of this code, the variable data contains our data. The file scikit_1.csv contains segment data..

Separating Different Data Types

In order to learn a model, we need to follow the methodology presented at the beginning. We are not going to be able to follow it to the letter, but we are going to do our best to make our model the best. The first step is to hide some examples to consider them as evidence.

scikit learn prefers separate data between dimensions and classes.

Here is the code that accomplishes this step:

[:,:2233]
[:,-3]

The first line brings $2233$ dimensions of our vectors (in this case we are ignoring those derived from these data). The data will be stored in the variable $First_variable$. The variable $Second_variable$ stores the classes (all lines, last column).

Scikit learn contains a function that allows separating the training data from the test data, and this is done automatically and shuffles the data randomly that supports our methodology.

We have four sets; two versions of the dimension data we generally call features and two versions of the classes. One version is for training (train), and another for testing (test). The train versions have half of the original data, while testing the other half.

Learn Python 3 from scratch to become a developer in demand

Learn Python 3 from scratch to become a developer in demand

Learn Python 3 from scratch to become a developer in demand

Description
This is the most comprehensive yet simple course on python programming language and it concentrates on Python 3.x which means that what you will learn is relevant and is not obsolete.=

*************************** No Prior Coding Experience Needed ***************************

This course assumes that you have no programming background. If you have some experience then, it's just a bonus point. You have never code, have some experience or have a lot of experience any other programming language, this course is one stop place for you.

Python is one of the most useful programming languages to learn

You can back-end of web applications, games, in-house scripts and even build robust test automation framework.

You will get the best in class support from the instructor for any question you have related to the course.

This course is very well structured:

Each lecture consist of a video screencast and code files
There are quizzes, homework to test your knowledge
High focus on practice and asking questions
You will also learn coding best practices
In recent years demand for python has exploded the job market and there are not as many developers as open jobs because not just development, QA industry is rapidly transitioning to python and building automation tools

What are you waiting for? Enroll today and learn the powerful python language !!!

Who is the target audience?

Beginners with zero programming background
Experienced programmers with other programming language
Testers who want to automate tools
Basic knowledge
Computer access with internet access
Nothing else, all the basics and set up information is covered in the course
What will you learn
Understand the concepts of Object Oriented Programming Language
Understand and write Efficient Code
Use Python Language to develop back-end of Web Applications
Use Python Language to create Games
Use Python Language to build Automation Frameworks
Use Python Programming Language to create your own hobby projects