Why Programmer and Developers Should Learn Python in 2020

Why Programmer and Developers Should Learn Python in 2020

Jobs, Machine Learning, Web Development, Automation, and 10 more reasons to learn Python in 2020

Python is growing and growing big time. If you read programming and technology news or blog post, then you might have noticed the rise of Python as many popular developer communities, including StackOverFlow and CodeAcademy has mentioned the rise of Python as a major programming language.

But, the biggest question is, why a programmer should learn Python? Python is growing, Ok, that’s great, but it doesn’t mean Java is going down or C++ is declining.

Well, I am a proud Java developer, and it is my favorite programming language and always remains, but, that doesn’t stop us from learning potential new tools and programming language, which will make you a better programmer and Python fits that bill.

Why should Programmers Learn Python in 2020?

If you are thinking of learning Python but not sure why you should do that, then here are ten reasons which highlight the benefits of learning Python in 2020.

Though, the questions depend upon who is asking that i.e. for a beginner, learning Python makes sense because its simple and main reason for learning Python is simplicity.

Similarly, for an experienced programmer who is looking to go into Data Science and Machine learning, learning Python makes sense because it’s quickly becoming the most used programming language, and there are powerful APIs and libraries available for AI, Data Science and Machine learning.

Anyway, without any further ado, here are my ten reasons to learn Python in 2020:

1. Data Science

This is the single, biggest reason why many programmers are learning Python in 2020. I know many of my friends who are bored with their Java programming jobs in Investment banks are learning Python on Udemy to make a career in Data Science due to exciting work and high pay.

But, what makes Python a preferred language for Data Science and Machine Learning? Didn’t R was considered best for that not too long ago? Well, I think the libraries and framework Python offers, like Pandas, PyBrain, NumPy and PyMySQL on AI, DataScience, and Machine learning, are one of that reason.

Another reason is diversity; Python experience allows you to do a lot more than R like you can create scripts to automate stuff, go into web development, and so much more.

2. Machine Learning

This is another reason why programmers are learning Python in 2020. The growth of machine learning is phenomenal in the last couple of years, and it’s rapidly changing everything around us.

Algorithms become sophisticated day by day; the best example is Google’s Search Algorithms, which can now answer what you are expecting. There are Chatbots around to answer your queries, and Uber is totally driven by Algorithms.

If you are interested in machine learning, want to do a pet project, or just want to play around, Python is the only major programming language that makes it easy.

Though there are machine learning libraries available in Java, you will find more content around Python as the developer community is preferring Python over anything else on Data Science and Machine learning.

3. Web Development

The good old development is another reason for learning Python. It offers so many good libraries and frameworks, like Django and Flask, which makes web development really easy.

The task which takes hours in PHP can be completed in minutes on Python. Python is also used a lot for web scrapping. Some of the popular websites on the Internet, like Reddit, are built using Python.

4. Simplicity

This is the single biggest reason for beginners to learn Python. When you first start with programming and coding, you don’t want to start with a programming language that has tough syntax and weird rules.

Python is both readable and simple. It also easier to set up; you don’t need to deal with any classpath problems like Java or compiler issues like C++.

Just install Python, and you are done. While installing, it will also ask you to add Python in PATH, which means you can run Python from anywhere on your machine.

5. Huge Community

You need a community to learn new technology, and friends are your biggest asset when it comes to learning a programming language. You often get stuck with one or another issue, and that time, you need a helping hand.

Thanks to Google, you can find the solution of your any Python-related problem in minutes. Communities like StackOverflow and Groups also brings many Python experts together to help newcomers.

6. Libraries and Frameworks

One of the similarities between Python and Java is the sheer number of open source libraries, frameworks, and modules available to do whatever you want to do. It makes application development really easy.

Just imagine creating a web application without Spring in Java or Django and Flask in Python. It makes your job simple as you only need to focus on business logic.

Python has numerous libraries for different needs. Django and Flask are two of the most popular for web development, and NumPy and SciPy are for Data Science.

In fact, Python has one of the best collections of machine learning and data science libraries like TensorFlow, Scikit-Learn, Keras, Pandas and many more.

7. Automation

When I first come to know about Python was due to one of my scripting needs. I was working with an application that receives messages over UDP and there was a problem, we did not see messages in the log.

I wanted to check if we are receiving any UDP traffic on that box and that port or not, but I couldn’t find a handy UNIX command to do that.

One of my friends, who sits next to me, was learning Python, and he wrote a utility in just 5 minutes to intercept UDP messages using one of the Python modules.

Obviously, I was impressed with the time it took for him to write such a tool, but that just highlights the power of Python when it comes to writing scripts, tools, and automating stuff.

8. Multipurpose

One of the things I like about Python is its Swiss Army knife nature. It’s not tied to just one thing, e.g. R, which is good on Data Science and Machine learning but nowhere when it comes to web development. Learning Python means you can do many things.

You can create your web applications using Django and Flask, Can do Data Analysis using NumPy, Scipy, Scikit-Learn, and NLTK.

At a bare minimum, you can use Python to write scripts to automate many of your days to day tasks.

9. Jobs and Growth

Python is growing really fast and big time, and it makes a lot of sense to learn a growing programming major programming language if you are just starting your programming career.

It not only help you to get a job quickly but also it will also accelerate your career growth. IMHO, for beginners, after simplicity, this should be the most important reason to learn Python

10. Salary

Python developers are one of the highest-paid developers, particularly in Data Science, Machine learning, and web development.

On average, also, they are very good-paying, ranging from 70,000 USD to 150,000 USD depending upon their experience, location, and domain.

That’s all about some of the important reasons to learn Python in 2020. As I said, it’s important to know programming and coding in today’s world, and if you don’t know to code, you are missing something, and Python is a great way to start learning to code.

For programmers who already know Java or C++, learning Python not just makes you a Polyglot programmer but also gives you a powerful tool in your arsenal to write scripts, create a web application, and open door on the exciting field of Data Science and Machine Learning.

Thanks, You made it to the end of the article … Good luck with your Python journey! It’s certainly a great decision and pays you a lot in your near future.

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Python Tutorial - Learn Python for Machine Learning and Web Development

Python Tutorial - Learn Python for Machine Learning and Web Development

Python tutorial for beginners - Learn Python for Machine Learning and Web Development. Can Python be used for machine learning? Python is widely considered as the preferred language for teaching and learning ML (Machine Learning). Can I use Python for web development? Python can be used to build server-side web applications. Why Python is suitable for machine learning? How Python is used in AI? What language is best for machine learning?

Python tutorial for beginners - Learn Python for Machine Learning and Web Development

TABLE OF CONTENT

  • 00:00:00 Introduction
  • 00:01:49 Installing Python 3
  • 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

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Further reading

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

Machine Learning A-Z™: 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 👍

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

Top Python Development Companies | Hire Python Developers

Top Python Development Companies | Hire Python Developers

After analyzing clients and market requirements, TopDevelopers has come up with the list of the best Python service providers. These top-rated Python developers are widely appreciated for their professionalism in handling diverse projects. When...

After analyzing clients and market requirements, TopDevelopers has come up with the list of the best Python service providers. These top-rated Python developers are widely appreciated for their professionalism in handling diverse projects. When you look for the developer in hurry you may forget to take note of review and ratings of the company's aspects, but we at TopDevelopers have done a clear analysis of these top reviewed Python development companies listed here and have picked the best ones for you.

List of Best Python Web Development Companies & Expert Python Programmers.

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.