FREE Machine Learning Crash Course – Learn With Google AI

FREE Machine Learning Crash Course – Learn With Google AI. Google has introduced a new “Learn with Google AI”, course, which will bring machine learning skills and concepts to all users. The course is free and available to all users.  Google says the set of educational resources in this course have been developed by machine learning experts at the company.

Google Machine Learning Course

Google says the idea of this machine learning course is to encourage people to learn about machine learning concepts, develop skills in the area, and apply artificial intelligence to real-world problems.

The new Machine Learning Crash Course will give a quick introduction to practical ML concepts using high-level TensorFlow (TF) APIs. TensorFlow is Google’s open source library for machine learning tools and can be accessed by anyone to build AI, ML frameworks suited to their tasks, problems.

Machine Learning For Artificial Intelligence

Machine Learning allows computers to learn, understand and recognize the data, without being explicitly programmed to do so. Machine Learning is the building block for artificial intelligence and is what powers self-driving cars, image recognition, etc.

You Might Be Interested In – Python Tutorial For Beginners

Users will be able to learn about key ML algorithms and frameworks from Google’s new course It will also include videos from ML experts at Google, interactive visualizations illustrating ML concepts and coding exercises using TensorFlow APIs, says the company.

Learn Machine Learning With Google AI

“We believe it’s important that the development of AI reflects as diverse a range of human perspectives and needs as possible. So, Google AI is making it easier for everyone to learn ML by providing a huge range of free, in-depth educational content.

This is for everyone — from deep ML experts looking for advanced developer tutorials and materials, to curious people who are ready to try to learn what ML is in the first place,” Zuri Kemp, Program Manager for Google’s machine learning education said in a press statement.

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How to enroll for Google Machine Learning course? What are the requirements?

Those who are interested can access the Google Machine Learning course at  While the course is free for all, there are some pre-requisites before taking up an understanding of how to code for machine learning.

Related Article: Advantages of Python

Google’s course page says the user must have a mastery of introductory level of algebra, and this includes variables and coefficients, linear equations, graphs of functions, and histograms. Also, familiarity with advanced math concepts such as logarithms and derivatives will be helpful, though it is not compulsory.

Additionally, a knowledge of programming basics and some experience coding in Python are also required. Google says those enrolling should comfortable reading and writing Python code since the exercises in the course are coded in the same programming language. The course will have lectures from Google’s AI experts, along with exercises and access to some material as well.

The Google course on Machine Learning was originally built for the company’s own employees and so far over 18,000 employees in the company have enrolled in this MLCC course. Google says that seeing the program’s success in-house is what inspired them to make it available to everyone. Source: Indian Express.

Enroll Now In Google Machine Learning Course.!

Python For Machine Learning Tutorial For Beginners

Python For Machine Learning Tutorial For Beginners. Machine learning is the new buzz word all over the world across the industries. Everyone trying to learn machine learning models, classifiers, neural networks and other machine learning technologies. If you are willing to learn machine learning, but you have a  doubt of how do you get started? Here Coding compiler gives answers to your questions. Let’s dive into this article, happy machine learning.

Python For Machine Learning

In this article we will talk about the important features of Python and the reasons it applies to machine learning, introducing some important machine learning packages, and other places where you can get more detailed resources.

Why Python for Machine Learning?

Python is well suited for machine learning. First, it is simple. If you are completely unfamiliar with Python but have some other programming experience (C or other programming languages), getting started is fast.

Second, Python’s community is strong. This makes Python documentation not only tractable but also easy to read. You can also find detailed answers to many questions on StackOverflow.

And again, the by-product of a strong community is the vast library of useful libraries (native to Python and third-party software) that basically solve all your problems (including machine learning).

Related Article: Advantages of Python

Is Python Slow?

Python is slow. It’s not the fastest language to implement, and having so many useful abstractions comes at a price.

But this is a problem that can be solved: Libraries can outsource heavy computations to other more efficient (but harder) languages such as C and C ++. Such as NumPy this numerical computing library is written in C, running fast. In practice, almost all libraries use NumPy to do the heavy lifting. If you see Numpy, you should think of it soon.

So you can make the program run faster with its low-level language to achieve the speed of operation compared. You do not need to worry about the speed of the program.

Machine Learning Tutorial For Beginners

Worth knowing python libraries for machine learning.

  1. Scikit-learn
  2. NLTK
  3. Theano
  4. TensorFlow
  5. Keras
  6. PyTorch

1) Scikit-learn

Have you just started to learn machine learning? If you need a library that covers all the features of feature engineering, model training, and model testing, scikit-learn is your best bet!

This great free software provides all the tools you need for machine learning and data mining. It is the current standard library for machine learning in Python. This library is recommended for use with any sophisticated machine learning algorithm.

This library supports both categorization and regression, implementing all of the classic algorithms (support vector machines, random forests, naive Bayes, etc.). The library design makes migrating algorithms so easy that experimenting with different algorithms is easy. These classic algorithms are highly usable and can be used in a large number of different situations.

But this is not the full functionality of Scikit-learn, it can also be used to do dimensionality reduction, clustering, whatever you can think of. Because it builds on Numpy and Scipy (all numerical calculations are done in C), it runs extremely fast.

These examples can tell you the function of this library, if you want to learn how to use it, you can read the tutorial.


NLTK is not a machine learning library, but it is a library necessary for natural language processing (NLP). In addition to the features used for word processing, such as clustering, word segmentation, stemming, marking, parsing, etc., it also contains a large number of datasets and other lexical resources that can be used for model training.

3) Theano

Theano is widely used in industry and academia and is the originator of all deep learning architecture. Theano is Python, in conjunction with Numpy. You can use it to build neural networks with multidimensional arrays. Theano handles all the math and you do not need to know the underlying math formula implementation.

Theo already provided support for GPU computing as early as supporting the use of GPU for computing not as popular as it is today. This library is currently very mature and can support many different types of operations. This allows Theano to win when compared to other libraries.

Currently, the biggest problem with Theano is that APIs are not very useful and difficult to use for newbies. However, packages such as  Keras , Blocks, and  Lasagne that already have a solution to this problem can simplify the use of Theano.

TensorFlow Machine Learning

Google Brain Team created TensorFlow for internal use and turned it open in 2015. Designed to replace their existing DistBelief, a closed machine learning framework, it is said that the architecture is too dependent on Google’s overall architecture and not flexible enough to be very inconvenient when sharing code.

So there is TensorFlow. Google learned from previous mistakes. Many consider TensorFlow an improved version of Theano, which provides a more flexible and easy-to-use API. Can be used in scientific research and industry, while supporting the use of a large number of GPU model training. TensorFlow does not support Theano’s much more operations, but its computational visualization is better than Theano’s.

TensorFlow is currently very popular. If you just heard one of the names mentioned in this article today, it is most likely this. Every day, new posts to TensorFlow’s blog posts or academic articles are posted. This popularity provides a large number of users and tutorials, new people are very easy to use.


Keras is a library that provides higher-level neural network APIs that can be based on Theano or TensorFlow. It has the powerful features of both libraries while greatly simplifying ease of use. It puts the user experience in the forefront, providing simple APIs and useful error messages.

Keras’s design is module-based, which allows you to freely mix different models (neural layers, cost functions, etc.) and the model is very scalable because you only have to simply associate new modules with existing ones It can be up.

If you start with deep learning, take a look at examples  and  documentation  and have a look at what you can do with it. If you want to learn to use it, can from this tutorial begins.

Two similar libraries are Lasagne  and  Blocks , but they only support Theano. If you’ve tried Keras but you do not like it you can try these other libraries, maybe they’re better for you.


There is also a famous deep learning architecture Torch , it is implemented with Lua. Facebook implemented Torch in Python, called PyTorch, and made it open source. With this library you can use the lower level library Torch uses, but you can use Python instead of Lua.

PyTorch is good at troubleshooting, because Theano and TensorFlow use symbolic computation and PyTorch does not. Using symbolic calculations means that an operation (x + y) will not be executed when a single line of code is interpreted, until then it must be compiled (interpreted as CUDA or C).

This makes it hard to troubleshoot problems with Theano and TensorFlow because it’s hard to relate the error to the current code. This has its advantages, but it is not easy to find the wrong one. If you want to start learning PyTorch, official documents for beginners will also contain difficult content.

The first step in machine learning?

Now, You know about so many machine learning packages, which one should I use? How can I compare them? Where do I start?

You can try our Ape Advice ™ platform for beginners and do not bother with the details. If you have absolutely no contact with machine learning, start with scikit-learn. You can see how labeling, training and testing work, and how a model is built.

If you want to try out in-depth learning, starting with Keras, this is the easiest framework to recognize. You can try it first to find the feeling. After you get a bit of experience, you can begin to think about what you need most: speed, different APIs, or whatever, and you’re better off later.

There are currently numerous articles comparing Theano, Torch and TensorFlow. No one can say which is the best. What you have to keep in mind is that all packages support a lot of things and are constantly improving, making it harder and harder to compare them to each other. Six months ago the standard may be outdated, a year ago’s assessment said the framework X does not have the Y function may not be effective.

If you want to know more about the concepts of machine learning, check out this Machine Learning Getting Started Guide.

Python Tutorial For Beginners With Examples – Learn Python In One Day

Python Tutorial For Beginners With Examples. Learn Python In One Day by following this blog post. Coding compiler has done it’s best to share this Python tutorial with examples. Let’s start learning Python programming in a day to get started with Python language. All the best for your future and happy python learning.

Python Tutorial For Beginners

The first question, what is Python? According to Guido van Rossum, the father of Python, Python is:

A high-level programming language whose core design philosophy is code readability and syntax that allows programmers to express their ideas with very little code.

For me, the primary reason to learn Python is that it is a language that can be gracefully programmed. It’s easy and natural to write code and implement my ideas.

What is Python Used For?

Another reason is that we can use Python in many places: data science, web development, machine learning, and so on, all can be developed using Python. Google, Quora, Pinterest, and Spotify all use Python for their backend Web development. Let’s learn about Python now.

Python Basics For Beginners

Here we go with learning Python in one day.

1. Python Variables

You can think of a variable as a word for storing a value. Let’s see an example.

It’s easy to define a variable in Python and assign it a value. If you want to store the number one to the variable “one,” let’s try it out:

In addition to an integer, we can also use the True / False, string, float and other data types.

 # booleanstrue_boolean = Truefalse_boolean = False# stringmy_name = "Leandro Tk"# floatbook_price = 15.80

 2. Python Control flow: conditional statement

” If ” uses an expression to determine whether a statement is True or False. If True, execute the code in if, as shown in the following example:

if True:
  print(“Hello Python If”)if 2 > 1:
  print(“2 is greater than 1”)

2 is larger than 1, so the print code is executed.

When the expression in ” if ” is false, the ” else ” statement will be executed.

1 is smaller than 2, so the code in ” else ” will be executed.

You can also use the “elif ” statement:

3. Python Loop and iterate

In Python, we can iterate in different ways. I will say while and for.

Python While Loop: While the statement is True, while the code block inside will be executed. So the code below prints 1 through 10.

The while loop needs a loop condition, and if condition, on is always true, it iterates all the time, with a loop condition of false when num’s value is 11.

Another piece of code can help you better understand the use of the while statement:

The loop condition is True so it iterates until it’s False.

Python For loop : You can apply the variable ” num ” on the block of code , and the “for” statement will iterate over it for you. This code will print the same code as in while : from 1 to 10.

Did you see? This is too simple. The range of i starts from 1 until the eleventh element (10 is the tenth element).

Python List: collection | array | data structure

Suppose you want to store the integer 1 in a variable, but you also want to store 2 and 3, 4, 5 …

Instead of using hundreds or thousands of variables, I have other ways to store these integers that I want to store? As you have already guessed, there are other ways to store them.

The list is a collection that can store a list of values (just like what you want to store), then let’s use it:

This is really easy. We created an array called my_integer and put the data in it.

Maybe you might ask, “How do I get the value in the array?”

Ask good. The list has a concept called indexing. The following table of the first element is index 0 (0). The second index is 1, and so on, you should understand.

In Python’s syntax, it is also good to understand:

If you do not want to save the whole number. You just want to save some strings, like your relative’s list of names. My look is similar to this:

Its principle is the same as storing an integer, very friendly.

We only learned how the index of a list works and I also need to tell you how to add an element to the list’s data structure (add an item to the list).

The most common way to add new data to the list is to splicing. Let’s take a look at how it is used:

Stitching super easy, you only need to put an element (such as “valid machine”) as the splicing parameters.

Well, the list of knowledge is enough, let’s take a look at the other data structures.

Python Dictionary: Key-Value Data Structure

Now we know that List is an indexed integer number set. But what if we do not use integer numbers as indexes? We can use some other data structures, such as numbers, strings or other types of indexes.

Let’s learn about this dictionaries data structure. A dictionary is a collection of key-value pairs. The dictionary is about this long:

Key is the index to value . How do we access the dictionary value ? You should guess, that is the use of key. Let’s try it out

We have a key (age) value (24) that uses a string as the key integer for value .

I created a dictionary about me that contains my name, nickname, and nationality. These attributes are the keys in the dictionary.

Just as we have learned of using an index to access a list, we also use an index (the key in the dictionary is an index) to access the value stored in the dictionary.

As we use the list, let’s learn how to add elements to the dictionary. The dictionary mainly points to the value of the key. The same is true when we add elements:

We only need to point a key in a dictionary to a vvalue. No hardship, right?

Python Iteration: Loop through the data structure

As we learned in the basics of Python, List iteration is simple. We Python developers usually use For loops. Let’s try it out

For each book on the shelf, we print ( can do anything ) to the console. Super easy and intuitive. This is the beauty of Python.

We can also use the for loop for the hash data structure, but we need to use the key:

The above is an example of how to use a For loop in a dictionary. For each key in the dictionary, we print out the value of key and key.

We named the two parameters key and value, but this is not necessary. We are free to name it. Let’s take a look:

You can see that we use the attribute as the key parameter in the dictionary, which has the same effect as using key naming. Really great!

Python Classes & Objects

Some theories:

Objects are representations of real-world entities, such as cars, dogs, or bicycles. These objects share two main features in common: data and behavior.

Cars have data such as the number of wheels, the number of doors and the seat space, and they can show their behavior: they can accelerate, stop, show how much fuel left, and many more.

We treat the data as attributes and behaviors in object-oriented programming. Again expressed as:

Data → Properties and Behaviors → Methods

The class is a blueprint to create a single object. In the real world, we often find many objects of the same type. For example, cars. All cars have the same structure and model (all with an engine, wheels, doors, etc.). Each car is constructed from the same blueprint and has the same components.

Python object-oriented programming model: ON

Python, as an object-oriented programming language, has the notion of classes and objects.

A class is a blueprint that is a model of the object.

So, a class is a model or a way to define properties and behaviors (as we discussed in the theory section). For example, a vehicle class has its own properties that define what kind of vehicle this object is. The attributes of a car are the number of wheels, energy type, seat capacity and a maximum speed of these.

With that in mind, let’s take a look at the syntax of Python’s classes :

Above the code, we use the class statement to define a class. Is not it easy?

An object is a class instantiation, which we can instantiate by class name.

Here, car is an object (or instantiation) of class Vehicle.

Remember the vehicle class has four attributes: the number of wheels, fuel tank type, seat capacity and maximum speed. When we create a new vehicle object to set all the attributes. So here, we define a class that accepts parameters when it’s initialized:

This init method. We call this a constructor. So when we create a vehicle object, we can define these properties. Imagine we like Tesla Model S, so we want to create an object of this type. 

It has four wheels, uses electric energy, five seats and a maximum speed of 250 kilometers (155 miles). Let’s start by creating an object like this :

Four + energy + five + maximum speed of 250 km.

All the properties have been set. But how do we access these property values? We send a message to the object to request that value from it. We call this method. It is the behavior of the object. Let’s achieve it:

This is the implementation of the two methods number_of_wheels and set_number_of_wheels. We call it getter & setter. Because the first function is to get the property value, the second is to set a new value for the property.

In Python, we can use @property (modifier) to define getters and setters. Let’s take a look at the actual code:

And we can use these methods as properties:

This is slightly different from the method definition. The method here is based on the property. For example, when we set the new number of tires, we do not regard these two as parameters, but set the value 2 to number_of_wheels. This is a way of writing python-style getter and setter code.

But we can also use this method for other things, such as the ” make_noise ” method. let us see:

When we call this method, it simply returns a string  VRRRRUUUUM. 

Python Package: Hide information

Encapsulation is a mechanism that limits direct access to object data and methods. At the same time, however, it makes it easier to manipulate data (object methods).

“Package can be used to hide data members and member functions in accordance with this definition, it means that the package. Objects inside an external view showing typically hidden in the object definition.” – Wikipedia

All internal representations of objects are hidden from the outside. Only the object itself can interact with its internal data.

First, we need to understand how open and closed instance variables and methods work.

Public instance variables

For the Python class, we can initialize a public instance variable in our constructor method. Let’s take a look at this one:

In this construction method:

Here, we apply the first_name value as a parameter to the public instance variables.

In the class:

Here, we do not need to first_name as a parameter, all instance objects have a class attribute initialized with TK.

Too cool, and now we have learned that we can use public instance variables and class properties. Another interesting thing about the public part is that we can manage the values of the variables. What do I mean? Our object can manage its variable values: Get and Set variable values.

Still in the Person class, we want to set another value for its first_name variable:

This is fine, we just set another value (kaio) for the first_name instance variable and update the value. It’s that simple. Because this is a public variable, we can do that.

Python Non-public instance variables

We do not use the term “private” here because all of the properties in Python are not really private (there is usually no unnecessary amount of work). -  PEP 8

As public instance variable(public instance variables), we can define non-public instance variable(non-public instance variables) inside the constructor or class.

The difference in the syntax is that for non-public instance variables(non-public instance variables), an underscore (_) is used before the variable name.

“Private ‘instance variables that are not accessible from inside the object do not exist in Python, however, there is a convention that most Python code will follow: underscore names (such as _spam) should be considered as Non-public part of API (whether function, method, or data member) “

Here is sample code:

Have you seen the email variable yet? This is how we define non-public variables:

We can access and update it. Non-public variables are just a matter of usage and should be treated as a non-public part of the API.

So we use a method within the class definition to implement this functionality. Let’s implement two methods (email and update_email) to deepen our understanding:

Now we can use these two methods to update and access non-public variables. Here is an example

  1. We initialized a new object using first_name TK and email [email protected]
  2. Use the method to access a non-public variable email and output it
  3. Try setting a new email outside the class
  4. We need to treat non-public variables as non-public parts of the API
  5. Use our instance method to update the non-public variables
  6. Success! We’ve updated it inside the class using helper methods.

Python Public method

For public methods, we can also use them in classes:

Let’s test it:

That’s fine – we have no problems using it in our class.

Python Non-public methods

But with a non-public method, we can not do that. If we want to implement the same Person class, we now use the underscore (_) show_age non-public method.

Now, we will try to invoke this non-public method with our object:

We can access and update it. The non-public method is just a convention and should be considered as a non-public part of the API.

Here’s an example of how we can use it:

Here’s a _get_age non-public method and a show_age public method. show_age can be used by our object (not in our class), while _get_age is only used in our class definition (in the show_age method). But again, this is usually the practice.

Python Package summary

Through the package, we can ensure that the internal representation of the object is hidden from the outside.

Python Inheritance: Behavior and Features

Some objects have something in common: their behavior and characteristics.

For example, I inherited some of my father’s features and behaviors. I inherited the characteristics of his eyes and hair, as well as his impatience and introverted behavior.

In object-oriented programming, a class can inherit the common characteristics (data) and behaviors (methods) of another class.

Let’s look at another example and implement it in Python.

Imagine the car. The number of wheels, seat capacity, and maximum speed are all attributes of a car. We can say that the ElectricCar class inherits these same properties from the normal Car class.

The realization of our Car class:

Once initialized, we can use all created instance variables. awesome.

In Python, we inherit the parent class as a child argument. An ElectricCar class can inherit our Car class.

It’s that simple. We do not need to implement any other method because this class has completed the inheritance of the parent class (inherited from the Car class). Let’s prove:

Thats beautiful. Original Source.

Other Python Tutorials

What is Python – A Beginners Guide

Advantages of Python Programming

Advantages of Python And Disadvantages of Python

Advantages of Python And Disadvantages of Python. Here in this blog post Coding compiler sharing a detailed article on python advantages and python disadvantages. Let’s start reading, happy learning.

Advantages of Python

  1. Easy Syntax
  2. Readability
  3. High-Level Language
  4. Object-oriented programming
  5. It’s Opensource and Free
  6. Cross-platform
  7. Widely Supported
  8. It’s Safe
  9. Batteries Included
  10. Extensible

Related Article: What is Python – A Beginners Guide

    Python Advantages

    Let’s discuss about Advantages of Python in detail.

    Easy Syntax of Python

    Python’s syntax is easy to learn, so both non-programmers and programmers can start programming right away.

Very Clear Readability of Python

Python’s syntax is very clear, so it is easy to understand program code. (Python is often referred to as “executable pseudo-code” because its syntax mostly follows the conventions used by programmers to outline their ideas without the formal verbosity of code in most programming languages.
In other words, syntax of Python is almost identical to the simplified “pseudo-code” used by many programmers to prototype and describe their solution to other programmers. Thus Python can be used to prototype and test code which is later to be implemented in other programming languages).

Python High-Level Language

Python looks more like a readable, human language than like a low-level language. This gives you the ability to program at a faster rate than a low-level language will allow you.

Python Object-oriented programming

Object-oriented programming allows you to create data structures that can be re-used, which reduces the amount of repetitive work that you’ll need to do. Programming languages usually define objects with namespaces, like class or def, and objects can edit themselves by using keyword, like this or self.
Most modern programming languages are object-oriented (such as Java, C++, and C#) or have support for OOP features (such as Perl version 5 and later). Additionally, object-oriented techniques can be used in the design of almost any non-trivial software and implemented in almost any programming or scripting language.
Python’s support for object-oriented programming is one of its greatest benefits to new programmers because they will be encountering the same concepts and terminology in their work environment. If you ever decide to switch languages or use any other for that fact, you’ll have a significant chance that you’ll be working with object-oriented programming.

Python Is Open-Source and Free

Python is both free and open-source. The Python Software Foundation distributes pre-made binaries that are freely available for use on all major operating systems called CPython. You can get CPython’s source-code, too. Plus, you can modify the source code and distribute as allowed by CPython’s license.

Python is a Cross-platform

Python runs on all major operating systems like Microsoft Windows, Linux, and Mac OS X.

Python Widely Supported Programming Language

Python has an active support community with many websites, mailing lists, and USENET “netnews” groups that attract a large number of knowledgeable and helpful contributes.

Python is a Safe

Python doesn’t have pointers like other C-based languages, making it much more reliable. Along with that, errors never pass silently unless they’re explicitly silenced. This allows you to see and read why the program crashed and where to correct your error.

Python Batteries Included Language

Python is famous for being the “batteries are included” language. There are over 300 standard library modules which contain modules and classes for a wide variety of programming tasks.
For example the standard library contains modules for safely creating temporary files (named or anonymous), mapping files into memory (including use of shared and anonymous memory mappings), spawning and controlling sub-processes, compressing and decompressing files (compatible with gzip or PK-zip) and archives files (such as Unix/Linux “tar”).
Accessing indexed “DBM” (database) files, interfacing to various graphical user interfaces (such as the TK toolkit and the popular WxWindows multi-platform windowing system), parsing and maintaining CSV (comma-separated values) and “.cfg” or “.ini” configuration files (similar in syntax to the venerable WIN.INI files from MS-DOS and MS-Windows), for sending e-mail, fetching and parsing web pages, etc. It’s possible, for example, to create a custom web server in Python using less than a dozen lines of code, and one of the standard libraries, of course.

Python is Extensible

In addition to the standard libraries there are extensive collections of freely available add-on modules, libraries, frameworks, and tool-kits. These generally conform to similar standards and conventions.
For example, almost all of the database adapters (to talk to almost any client-server RDBMS engine such as MySQL, Postgres, Oracle, etc) conform to the Python DBAPI and thus can mostly be accessed using the same code. So it’s usually easy to modify a Python program to support any database engine.

Disadvantages of Python

  1. Slower Speed
  2. Too Easy

Python is Slower Speed

Python is executed by an interpreter instead of compilation, which causes it to be slower than if it was compiled and then executed. However, for most applications, it is by far fast enough. One Python idiom is “Speed isn’t a problem until it’s a problem.”

Python is Too Easy

When one has mastered Python one can become so accustomed to its features, particularly its dynamic late-binding model and its many libraries, that it can be difficult to learn and become comfortable in other programming languages.
Specifically, the need to declare variable “types” and to “cast” values from one type to another and the syntactic requirements for adding semi-colons and curly braces used by other programming languages can be viewed as tedious or onerous by experienced Python programmers.
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What is Python? – What is Python Used For?

What is Python? – What is Python Used For? A Comprehensive Guide on Python for beginners. Here in this blog post Coding compiler sharing, complete beginners guide on Python programming. After going through this article you will understand exactly what is Python language, what are the uses of Python and what it used for. Let’s start reading about Python. Happy learning.

What is Python?

Python is a general-purpose programming language which can be used for a wide variety of applications. A great language for beginners because of its readability and other structural elements designed to make it easy to understand, Python is not limited to basic usage. In fact, it powers some of the world’s most complex applications and website.

What is Python Programming?

Python is an interpreted language, meaning that programs written in Python don’t need to be compiled in advance in order to run, making it easy to test small snippets of code and making code written in Python easier to move between platforms. Since Python is most operating systems in common use, Python is a universal language found in a variety of different applications.

Who Invented Python?

First developed in the late 80s by Guido van Rossum, Python is currently in its third version, released in 2008, although the second version originally released in 2000 is still in common usage.

Why use Python?

There are several reasons why Python could be a good choice for your next programming project, whether it’s your first attempt at coding or if you’re a seasoned developer looking for a new frontier.

Perhaps most importantly, Python has an enormous user community. This means that no matter what problem you’re trying to solve, chances are there is already strong documentation, tutorials, guides, and examples to help you along your way.

There are numerous integrated development environments and other development tools to choose from, and thousands of open source packages available to extend Python to do just about anything you can think of.

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Integrated Development Environments For Python

Benefits of Python

Python is widely used programming language and it is used by big companies like Google, Pinterest, Instagram, Disney, Yahoo!, Nokia, IBM, and many other big companies uses this Python language for their applications.
The Raspberry Pi majorly relies on Python as it’s main programming language too. Learning Python can increase your chances of reaching more hieghts in your career.
Other benefits include:
  1. Python can be used to develop prototypes, and quickly because it is so easy to work with and read.
  2. Most automation, data mining, and big data platforms rely on Python.
  3. Python allows for a more productive coding environment than massive languages like C# and Java. It will save the time of developers.
  4. Python is easy to read and write, even if you’re not a skilled programmer. Anyone can begin working with the Python programming language, all you have to do is practice. Practice makes perfect in Python.
  5. Python powers Django, a complete and open source web application framework.
  6. Python has a massive support from the community across the globe.

What is Python used for?

Python’s ease of use and compatibility across a variety of operating systems makes it an ideal language for a number of uses. Many complex websites either currently or historically have used Python to power their back ends, from YouTube to Instagram to Reddit, and thousands of other well-known examples. But Python isn’t only a web language.

Python is the primary language used for the massive cloud computing project OpenStack, powering private and public clouds in data centers all over the world.

It’s also used to write desktop software, like Calibre, OpenShot, and the original client for BitTorrent. Many application written in other languages, such as Blender, allow for scripting by users in Python. It’s also a popular language for machine learning, scientific, statistical, mathematical, and other types of specialized computing.

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Is Python open source?

The Python language itself is managed by the Python Software Foundation, who offer a reference implementation of Python, called, CPython, under an open source license. You can even download the Python source code.

Besides the Python implementation itself is open source, many open source projects make use of Python, and Python has many libraries available for developers under open source licenses.