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Python Scopes and Their Built-in Functions

What you will learn?What are scopes in Python and their different typesWhich type of scope to use and when, for writing efficient codesWhat is the LEGB rule and how it helps in Python scopesHow to modify the behavior of a Python scopeWhen to use scope-related built-in functionsWhat is Python Scope?In Python, variables are just not accessible from the class they have been declared in. To know where variables exist in a program and how to access them depends on how they have been declared. The part of the program where variables, functions, and objects are easily accessible is commonly referred to as scope in Python.Types of Scopes in Python:In Python, there are four types of scopes, which are as follows:   Global Scope   Local Scope Enclosing Scope  Built-in ScopeGlobal Scope (with example)Global scope refers to the names of variables which are defined in the main body of a program. These are visible and accessed throughout the program. The variables or objects declared in the global scope are easily accessible to all functions within the program. Let’s understand the global scope with the help of a code.message = "Hey" def python_developer():     developer = "Welcome to Python Programming!"     print(message, developer) def developer_name(name):     print(message, name) python_developer() developer_name("Mark!") OUTPUT: Hey Welcome to Python Programming! Hey Mark!Attention: As a good programming habit, it is advisable to avoid global variables as much as possible. Why? Because they are easy to alter and the result could be erroneous output. Global variables increase the security vulnerability of the code as well. That doesn’t mean you will not use global variables at all. As a thumb rule, try to use those variables and objects in the global scope, which are meant to be explicitly used globally like functions and objects. Local ScopeLocal scope refers to the names which are defined within a function and are local to that function. They can be accessed from the point of its definition until the end of the block in which it has been defined. The local scope exists till the time the function has been executed. Let’s understand the local scope with the help of a code.def local_test():     value = 1     # Print statement 1     print("The value defined is: ", value) local_test() OUTPUT: The value defines is:  1 Notice the error if you run the following code.def local_test():     value = 1 print("The first number defined is: ", value)   OUTPUT: Traceback (most recent call last):   File "C:/Projects/untitled/basic.py", line 4, in <module>     print("The value defined is: ", value) NameError: name 'value' is not definedEnclosing Scope or Non-local ScopeEnclosing scope is also known as non-local scope. They refer to the names of a variable defined in the nested function. Simply put, these variables are neither present in the local scope nor in the global scope. To create a non-local variable in an enclosing scope, use a non-local keyword. Let’s understand the enclosing scope with the help of a code.def parent_nest():     initial_value = 5     def child_nest():         next_value = 10                print("Value defined in the parent function: ", initial_value)         print("Value defined in the parent function: ", next_value)     child_nest() parent_nest()   OUTPUT: Value defined in the parent function :  5 Value defined in the parent function :  10Built-in ScopeWhen the variable or object is not found in local, global, or enclosing scope, then Python starts looking for it in the built-in scope. Built-in scopes are one of the widest scopes that cover all the reserved keywords. These are easy to call anywhere in the program prior to using them, without the need to define them.Modifying the Behavior of a Python ScopePython scope's behavior is strict. Though python allows accessibility to global names from anywhere,  their modification is highly restricted. With the help of allowed keywords, you can modify the behavior of a Python scope. The two keywords allowed in Python to modify the behavior of a Python scope are:Global KeywordLocal keywordGlobal KeywordTo define a variable declared inside a function as global, we have to use the ‘global’ keyword. By using a global keyword followed by a variable name, you are asking Python to use the globally defined variable instead of creating a local variable. Let’s understand this concept with a code snippet. You are free to use multiple global statements with a name. All the names that you list in a global statement will be automatically mapped to the global scope in which you define them. Let us understand how to use a global keyword with the help of a code.message = "Hey" def python_developer():     global message1     message1 = "Welcome to Python Programming!"     print("In Function message is: ", message) python_developer() print("Outside Function message is: ", message1) message print("Message is: ", message)   OUTPUT: In Function message is:  Hey Outside Function message is:  Welcome to Python Programming! Message is:  HeyNonlocal KeywordSimilar to the global keyword, Python also allows nonlocal names to be accessed within functions. To use the keyword, type nonlocal followed by the variable name. When using more than one variable, use a comma. Let us learn how to use nonlocal keywords with the help of a code.def my_message():     message = "Hey Programmers!" # A nonlocal variable     def nested():         nonlocal message  # Declare var as nonlocal     nested()     print(message) my_message()   OUTPUT: Hey Programmers!LEGB RuleLEGB is an abbreviation for (Local Enclosing Global Built-in) followed by the Python interpreter when executing a code. The LEGB rule in Python is a  name searching algorithm where Python looks up scopes in a particular order. For instance, if you want to look up a reference name, Python will look after all the scopes following the LEGB rule. That means, the interpreter will look for local scope, then global scope, followed by enclosing tag and then finally looking into built-in scopes. If the name is not present on either of the four scopes, you will perhaps get an error.Using Scope Related Built-In FunctionsPython Built-in functions relate to Python scope and namespace. The most commonly used scopes are  globals(), locals(), dirs(), and vars(), to name a few . These functions make it easy to fetch information about a Python scope or namespace. As they are built-in, they are available for free and you do not need to fetch from any library or import from a module.globals()The globals() function relates to the scope and namespaces in Python. It updates and returns a dictionary representing the current global symbol table. When you call globals() within a function block, names that can be accessed globally from the function will be returned. Let us understand globals() with the help of a code.score = 23 globals()['score'] = 10 print('The Score is:', score) OUTPUT: The Score is: 10locals()Another function is locals(), which is related to scope and namespaces in Python. It updates and returns a dictionary with a copy of the current state of the local Python scope. When you call locals() within a function block, names that can be accessed locally from the function will be returned. Let us understand locals() with the help of a code.# Without using local variables def test_1():     print("No local variable : ", locals()) # Using local variables def test_2():     Language = "Python Programming"     print("Local variable: ", locals()) test_1() test_2() OUTPUT: No local variable :  {} Local variable :  {'Language': 'Python Programming'}Let's have a look at one more code:def test():     score = 10     locals()['score'] = 200     print('The Score is:', score) test() OUTOUT: The Score is: 10dir()An important built-in function, dir () relates to scope and namespaces in python. It returns a list of valid attributes. Moreover, dir() function behavior differs with a different type of object, as it targets to generate the most relevant one instead of complete information. Let us understand dir() with the help of a code.scores = [5, 2, 3] print(dir(scores)) print('\n Return values from the empty dir()') print(dir()) OUTPUT:   ['__add__', '__class__', '__contains__', '__delattr__', '__delitem__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__', '__rmul__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 'append', 'clear', 'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']  Return values from the empty dir() ['__annotations__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'scores']vars()The vars() function is another in-built function related to scope and namespaces in Python. It returns __dict__ attribute for modules, classes, instances, or objects. It is important to note that the __dict__ is a special dictionary used by Python to implement namespaces. Remember, if you miss writing the vars() attribute, it will merely behave like locals(). Let us understand vars() with the help of a code.class Scores:     def __init__(self, Roll_1 = "Alice", Roll_2 = "Bella", Roll_3 = "Cindrella"):         self.Roll_1 = Roll_1         self.Roll_2 = Roll_2         self.Roll_3 = Roll_2 score = Scores() print(vars(score)) OUTPUT: {'Roll_1': 'Alice', 'Roll_2': 'Bella', 'Roll_3': 'Bella'}Conclusion:Python scopes are effective in writing reliable and efficient codes in the most straightforward and tech-savvy way. As the visibility of a name is sophistically defined, the chances of bugs that arise due to name collision or bad use of global names are very low.

Python Scopes and Their Built-in Functions

596
Python Scopes and Their Built-in Functions

What you will learn?

  • What are scopes in Python and their different types
  • Which type of scope to use and when, for writing efficient codes
  • What is the LEGB rule and how it helps in Python scopes
  • How to modify the behavior of a Python scope
  • When to use scope-related built-in functions

What is Python Scope?

In Python, variables are just not accessible from the class they have been declared in. To know where variables exist in a program and how to access them depends on how they have been declared. The part of the program where variables, functions, and objects are easily accessible is commonly referred to as scope in Python.

Types of Scopes in Python:

In Python, there are four types of scopes, which are as follows:  

  • Global Scope   
  • Local Scope 
  • Enclosing Scope  
  • Built-in Scope

Global Scope (with example)

Global scope refers to the names of variables which are defined in the main body of a program. These are visible and accessed throughout the program. The variables or objects declared in the global scope are easily accessible to all functions within the program. Let’s understand the global scope with the help of a code.

message = "Hey"
def python_developer():
    developer = "Welcome to Python Programming!"
    print(message, developer)
def developer_name(name):
    print(message, name)
python_developer()
developer_name("Mark!")
OUTPUT:
Hey Welcome to Python Programming!
Hey Mark!

Attention: As a good programming habit, it is advisable to avoid global variables as much as possible. Why? Because they are easy to alter and the result could be erroneous output. Global variables increase the security vulnerability of the code as well. That doesn’t mean you will not use global variables at all. As a thumb rule, try to use those variables and objects in the global scope, which are meant to be explicitly used globally like functions and objects. 

Local Scope

Local scope refers to the names which are defined within a function and are local to that function. They can be accessed from the point of its definition until the end of the block in which it has been defined. The local scope exists till the time the function has been executed. Let’s understand the local scope with the help of a code.

def local_test():
    value = 1
    # Print statement 1
    print("The value defined is: ", value)
local_test()
OUTPUT:
The value defines is:  1

Notice the error if you run the following code.

def local_test():
    value = 1
print("The first number defined is: ", value)  
OUTPUT:
Traceback (most recent call last):
  File "C:/Projects/untitled/basic.py", line 4, in <module>
    print("The value defined is: ", value)
NameError: name 'value' is not defined

Enclosing Scope or Non-local Scope

Enclosing scope is also known as non-local scope. They refer to the names of a variable defined in the nested function. Simply put, these variables are neither present in the local scope nor in the global scope. To create a non-local variable in an enclosing scope, use a non-local keyword. Let’s understand the enclosing scope with the help of a code.

def parent_nest():
    initial_value = 5
    def child_nest():
        next_value = 10       
        print("Value defined in the parent function: ", initial_value)
        print("Value defined in the parent function: ", next_value)
    child_nest()
parent_nest()  
OUTPUT:
Value defined in the parent function :  5
Value defined in the parent function :  10

Built-in Scope

When the variable or object is not found in local, global, or enclosing scope, then Python starts looking for it in the built-in scope. Built-in scopes are one of the widest scopes that cover all the reserved keywords. These are easy to call anywhere in the program prior to using them, without the need to define them.

Modifying the Behavior of a Python Scope

Python scope's behavior is strict. Though python allows accessibility to global names from anywhere,  their modification is highly restricted. With the help of allowed keywords, you can modify the behavior of a Python scope. The two keywords allowed in Python to modify the behavior of a Python scope are:

  1. Global Keyword
  2. Local keyword

Global Keyword

To define a variable declared inside a function as global, we have to use the ‘global’ keyword. By using a global keyword followed by a variable name, you are asking Python to use the globally defined variable instead of creating a local variable. Let’s understand this concept with a code snippet. 

You are free to use multiple global statements with a name. All the names that you list in a global statement will be automatically mapped to the global scope in which you define them. Let us understand how to use a global keyword with the help of a code.

message = "Hey"
def python_developer():
    global message1
    message1 = "Welcome to Python Programming!"
    print("In Function message is: ", message)
python_developer()
print("Outside Function message is: ", message1)
message
print("Message is: ", message)  
OUTPUT:
In Function message is:  Hey
Outside Function message is:  Welcome to Python Programming!
Message is:  Hey

Nonlocal Keyword

Similar to the global keyword, Python also allows nonlocal names to be accessed within functions. To use the keyword, type nonlocal followed by the variable name. When using more than one variable, use a comma. Let us learn how to use nonlocal keywords with the help of a code.

def my_message():
    message = "Hey Programmers!" # A nonlocal variable
    def nested():
        nonlocal message  # Declare var as nonlocal
    nested()
    print(message)
my_message()  
OUTPUT:
Hey Programmers!

LEGB Rule

LEGB is an abbreviation for (Local Enclosing Global Built-in) followed by the Python interpreter when executing a code.

LEGB Rule​ The LEGB rule in Python is a  name searching algorithm where Python looks up scopes in a particular order. For instance, if you want to look up a reference name, Python will look after all the scopes following the LEGB rule. That means, the interpreter will look for local scope, then global scope, followed by enclosing tag and then finally looking into built-in scopes. If the name is not present on either of the four scopes, you will perhaps get an error.

Using Scope Related Built-In Functions

Python Built-in functions relate to Python scope and namespace. The most commonly used scopes are  globals(), locals(), dirs(), and vars(), to name a few . These functions make it easy to fetch information about a Python scope or namespace. As they are built-in, they are available for free and you do not need to fetch from any library or import from a module.

globals()

The globals() function relates to the scope and namespaces in Python. It updates and returns a dictionary representing the current global symbol table. When you call globals() within a function block, names that can be accessed globally from the function will be returned. Let us understand globals() with the help of a code.

score = 23
globals()['score'] = 10
print('The Score is:', score)
OUTPUT:
The Score is: 10

locals()

Another function is locals(), which is related to scope and namespaces in Python. It updates and returns a dictionary with a copy of the current state of the local Python scope. When you call locals() within a function block, names that can be accessed locally from the function will be returned. Let us understand locals() with the help of a code.

# Without using local variables
def test_1():
    print("No local variable : ", locals())
# Using local variables
def test_2():
    Language = "Python Programming"
    print("Local variable: ", locals())
test_1()
test_2()
OUTPUT:
No local variable :  {}
Local variable :  {'Language': 'Python Programming'}

Let's have a look at one more code:

def test():
    score = 10
    locals()['score'] = 200
    print('The Score is:', score)
test()
OUTOUT:
The Score is: 10

dir()

An important built-in function, dir () relates to scope and namespaces in python. It returns a list of valid attributes. Moreover, dir() function behavior differs with a different type of object, as it targets to generate the most relevant one instead of complete information. Let us understand dir() with the help of a code.

scores = [5, 2, 3]
print(dir(scores))
print('\n Return values from the empty dir()')
print(dir())
OUTPUT:  
['__add__', '__class__', '__contains__', '__delattr__', '__delitem__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__', '__rmul__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 'append', 'clear', 'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']
 Return values from the empty dir()
['__annotations__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'scores']

vars()

The vars() function is another in-built function related to scope and namespaces in Python. It returns __dict__ attribute for modules, classes, instances, or objects. It is important to note that the __dict__ is a special dictionary used by Python to implement namespaces. Remember, if you miss writing the vars() attribute, it will merely behave like locals(). Let us understand vars() with the help of a code.

class Scores:
    def __init__(self, Roll_1 = "Alice", Roll_2 = "Bella", Roll_3 = "Cindrella"):
        self.Roll_1 = Roll_1
        self.Roll_2 = Roll_2
        self.Roll_3 = Roll_2
score = Scores()
print(vars(score))
OUTPUT:
{'Roll_1': 'Alice', 'Roll_2': 'Bella', 'Roll_3': 'Bella'}

Conclusion:

Python scopes are effective in writing reliable and efficient codes in the most straightforward and tech-savvy way. As the visibility of a name is sophistically defined, the chances of bugs that arise due to name collision or bad use of global names are very low. 

Abhresh

Abhresh Sugandhi

Author

Abhresh is specialized as a corporate trainer, He has a decade of experience in technical training blended with virtual webinars and instructor-led session created courses, tutorials, and articles for organizations. He is also the founder of Nikasio.com, which offers multiple services in technical training, project consulting, content development, etc.

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Why Should You Start a Career in Machine Learning?

If you are even remotely interested in technology you would have heard of machine learning. In fact machine learning is now a buzzword and there are dozens of articles and research papers dedicated to it.  Machine learning is a technique which makes the machine learn from past experiences. Complex domain problems can be resolved quickly and efficiently using Machine Learning techniques.  We are living in an age where huge amounts of data are produced every second. This explosion of data has led to creation of machine learning models which can be used to analyse data and to benefit businesses.  This article tries to answer a few important concepts related to Machine Learning and informs you about the career path in this prestigious and important domain.What is Machine Learning?So, here’s your introduction to Machine Learning. This term was coined in the year 1997. “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at the tasks improves with the experiences.”, as defined in the book on ML written by Mitchell in 1997. The difference between a traditional programming and programming using Machine Learning is depicted here, the first Approach (a) is a traditional approach, and second approach (b) is a Machine Learning based approach.Machine Learning encompasses the techniques in AI which allow the system to learn automatically looking at the data available. While learning, the system tries to improve the experience without making any explicit efforts in programming. Any machine learning application follows the following steps broadlySelecting the training datasetAs the definition indicates, machine learning algorithms require past experience, that is data, for learning. So, selection of appropriate data is the key for any machine learning application.Preparing the dataset by preprocessing the dataOnce the decision about the data is made, it needs to be prepared for use. Machine learning algorithms are very susceptible to the small changes in data. To get the right insights, data must be preprocessed which includes data cleaning and data transformation.  Exploring the basic statistics and properties of dataTo understand what the data wishes to convey, the data engineer or Machine Learning engineer needs to understand the properties of data in detail. These details are understood by studying the statistical properties of data. Visualization is an important process to understand the data in detail.Selecting the appropriate algorithm to apply on the datasetOnce the data is ready and understood in detail, then appropriate Machine Learning algorithms or models are selected. The choice of algorithm depends on characteristics of data as well as type of task to be performed on the data. The choice also depends on what kind of output is required from the data.Checking the performance and fine-tuning the parameters of the algorithmThe model or algorithm chosen is fine-tuned to get improved performance. If multiple models are applied, then they are weighed against the performance. The final algorithm is again fine-tuned to get appropriate output and performance.Why Pursue a Career in Machine Learning in 2021?A recent survey has estimated that the jobs in AI and ML have grown by more than 300%. Even before the pandemic struck, Machine Learning skills were in high demand and the demand is expected to increase two-fold in the near future.A career in machine learning gives you the opportunity to make significant contributions in AI, the future of technology. All the big and small businesses are adopting Machine Learning models to improve their bottom-line margins and return on investment.  The use of Machine Learning has gone beyond just technology and it is now used in diverse industries including healthcare, automobile, manufacturing, government and more. This has greatly enhanced the value of Machine Learning experts who can earn an average salary of $112,000.  Huge numbers of jobs are expected to be created in the coming years.  Here are a few reasons why one should pursue a career in Machine Learning:The global machine learning market is expected to touch $20.83B in 2024, according to Forbes.  We are living in a digital age and this explosion of data has made the use of machine learning models a necessity. Machine Learning is the only way to extract meaning out of data and businesses need Machine Learning engineers to analyze huge data and gain insights from them to improve their businesses.If you like numbers, if you like research, if you like to read and test and if you have a passion to analyse, then machine learning is the career for you. Learning the right tools and programming languages will help you use machine learning to provide appropriate solutions to complex problems, overcome challenges and grow the business.Machine Learning is a great career option for those interested in computer science and mathematics. They can come up with new Machine Learning algorithms and techniques to cater to the needs of various business domains.As explained above, a career in machine learning is both rewarding and lucrative. There are huge number of opportunities available if you have the right expertise and knowledge. On an average, Machine Learning engineers get higher salaries, than other software developers.Years of experience in the Machine Learning domain, helps you break into data scientist roles, which is not just among the hottest careers of our generation but also a highly respected and lucrative career. Right skills in the right business domain helps you progress and make a mark for yourself in your organization. For example, if you have expertise in pharmaceutical industries and experience working in Machine learning, then you may land job roles as a data scientist consultant in big pharmaceutical companies.Statistics on Machine learning growth and the industries that use MLAccording to a research paper in AI Multiple (https://research.aimultiple.com/ml-stats/), the Machine Learning market will grow to 9 Billion USD by the end of 2022. There are various areas where Machine Learning models and solutions are getting deployed, and businesses see an overall increase of 44% investments in this area. North America is one of the leading regions in the adoption of Machine Learning followed by Asia.The Global Machine Learning market will grow by 42% which is evident from the following graph. Image sourceThere is a huge demand for Machine Learning modelling because of the large use of Cloud Based Applications and Services. The pandemic has changed the face of businesses, making them heavily dependent on Cloud and AI based services. Google, IBM, and Amazon are just some of the companies that have invested heavily in AI and Machine Learning based application development, to provide robust solutions for problems faced by small to large scale businesses. Machine Learning and Cloud based solutions are scalable and secure for all types of business.ML analyses and interprets data patterns, computing and developing algorithms for various business purposes.Advantages of Machine Learning courseNow that we have established the advantages of perusing a career in Machine Learning, let’s understand from where to start our machine learning journey. The best option would be to start with a Machine Learning course. There are various platforms which offer popular Machine Learning courses. One can always start with an online course which is both effective and safe in these COVID times.These courses start with an introduction to Machine Learning and then slowly help you to build your skills in the domain. Many courses even start with the basics of programming languages such as Python, which are important for building Machine Learning models. Courses from reputed institutions will hand hold you through the basics. Once the basics are clear, you may switch to an offline course and get the required certification.Online certifications have the same value as offline classes. They are a great way to clear your doubts and get personalized help to grow your knowledge. These courses can be completed along with your normal job or education, as most are self-paced and can be taken at a time of your convenience. There are plenty of online blogs and articles to aid you in completion of your certification.Machine Learning courses include many real time case studies which help you in understanding the basics and application aspects. Learning and applying are both important and are covered in good Machine Learning Courses. So, do your research and pick an online tutorial that is from a reputable institute.What Does the Career Path in Machine Learning Look Like?One can start their career in Machine Learning domain as a developer or application programmer. But the acquisition of the right skills and experience can lead you to various career paths. Following are some of the career options in Machine Learning (not an exhaustive list):Data ScientistA data scientist is a person with rich experience in a particular business field. A person who has a knowledge of domain, as well as machine learning modelling, is a data scientist. Data Scientists’ job is to study the data carefully and suggest accurate models to improve the business.AI and Machine Learning EngineerAn AI engineer is responsible for choosing the proper Machine Learning Algorithm based on natural language processing and neural network. They are responsible for applying it in AI applications like personalized advertising.  A Machine Learning Engineer is responsible for creating the appropriate models for improvement of the businessData EngineerA Data Engineer, as the name suggests, is responsible to collect data and make it ready for the application of Machine Learning models. Identification of the right data and making it ready for extraction of further insights is the main work of a data engineer.Business AnalystA person who studies the business and analyzes the data to get insights from it is a Business Analyst. He or she is responsible for extracting the insights from the data at hand.Business Intelligence (BI) DeveloperA BI developer uses Machine Learning and Data Analytics techniques to work on a large amount of data. Proper representation of data to suit business decisions, using the latest tools for creation of intuitive dashboards is the role of a BI developer.  Human Machine Interface learning engineerCreating tools using machine learning techniques to ease the human machine interaction or automate decisions, is the role of a Human Machine Interface learning engineer. This person helps in generating choices for users to ease their work.Natural Language Processing (NLP) engineer or developerAs the name suggests, this person develops various techniques to process Natural Language constructs. Building applications or systems using machine learning techniques to build Natural Language based applications is their main task. They create multilingual Chatbots for use in websites and other applications.Why are Machine Learning Roles so popular?As mentioned above, the market growth of AI and ML has increased tremendously over the past years. The Machine Learning Techniques are applied in every domain including marketing, sales, product recommendations, brand retention, creating advertising, understanding the sentiments of customer, security, banking and more. Machine learning algorithms are also used in emails to ease the users work. This says a lot, and proves that a career in Machine Learning is in high demand as all businesses are incorporating various machine learning techniques and are improving their business.One can harness this popularity by skilling up with Machine Learning skills. Machine Learning models are now being used by every company, irrespective of their size--small or big, to get insights on their data and use these insights to improve the business. As every company wishes to grow faster, they are deploying more machine learning engineers to get their work done on time. Also, the migration of businesses to Cloud services for better security and scalability, has increased their requirement for more Machine Learning algorithms and models to cater to their needs.Introducing the Machine learning techniques and solutions has brought huge returns for businesses.  Machine Learning solution providers like Google, IBM, Microsoft etc. are investing in human resources for development of Machine Learning models and algorithms. The tools developed by them are popularly used by businesses to get early returns. It has been observed that there is significant increase in patents in Machine Learning domains since the past few years, indicating the quantum of work happening in this domain.Machine Learning SkillsLet’s visit a few important skills one must acquire to work in the domain of Machine Learning.Programming languagesKnowledge of programming is very important for a career in Machine Learning. Languages like Python and R are popularly used to develop applications using Machine Learning models and algorithms. Python, being the simplest and most flexible language, is very popular for AI and Machine Learning applications. These languages provide rich support of libraries for implementation of Machine Learning Algorithms. A person who is good in programming can work very efficiently in this domain.Mathematics and StatisticsThe base for Machine Learning is mathematics and statistics. Statistics applied to data help in understanding it in micro detail. Many machine learning models are based on the probability theory and require knowledge of linear algebra, transformations etc. A good understanding of statistics and probability increases the early adoption to Machine Learning domain.Analytical toolsA plethora of analytical tools are available where machine learning models are already implemented and made available for use. Also, these tools are very good for visualization purposes. Tools like IBM Cognos, PowerBI, Tableue etc are important to pursue a career as a  Machine Learning engineer.Machine Learning Algorithms and librariesTo become a master in this domain, one must master the libraries which are provided with various programming languages. The basic understanding of how machine learning algorithms work and are implemented is crucial.Data Modelling for Machine Learning based systemsData lies at the core of any Machine Learning application. So, modelling the data to suit the application of Machine Learning algorithms is an important task. Data modelling experts are the heart of development teams that develop machine learning based systems. SQL based solutions like Oracle, SQL Server, and NoSQL solutions are important for modelling data required for Machine Learning applications. MongoDB, DynamoDB, Riak are some important NOSQL based solutions available to process unstructured data for Machine Learning applications.Other than these skills, there are two other skills that may prove to be beneficial for those planning on a career in the Machine Learning domain:Natural Language processing techniquesFor E-commerce sites, customer feedback is very important and crucial in determining the roadmap of future products. Many customers give reviews for the products that they have used or give suggestions for improvement. These feedbacks and opinions are analyzed to gain more insights about the customers buying habits as well as about the products. This is part of natural language processing using Machine Learning. The likes of Google, Facebook, Twitter are developing machine learning algorithms for Natural Language Processing and are constantly working on improving their solutions. Knowledge of basics of Natural Language Processing techniques and libraries is must in the domain of Machine Learning.Image ProcessingKnowledge of Image and Video processing is very crucial when a solution is required to be developed in the area of security, weather forecasting, crop prediction etc. Machine Learning based solutions are very effective in these domains. Tools like Matlab, Octave, OpenCV are some important tools available to develop Machine Learning based solutions which require image or video processing.ConclusionMachine Learning is a technique to automate the tasks based on past experiences. This is among the most lucrative career choices right now and will continue to remain so in the future. Job opportunities are increasing day by day in this domain. Acquiring the right skills by opting for a proper Machine Learning course is important to grow in this domain. You can have an impressive career trajectory as a machine learning expert, provided you have the right skills and expertise.
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Why Should You Start a Career in Machine Learning?

If you are even remotely interested in technology ... Read More

Types of Probability Distributions Every Data Science Expert Should know

Data Science has become one of the most popular interdisciplinary fields. It uses scientific approaches, methods, algorithms, and operations to obtain facts and insights from unstructured, semi-structured, and structured datasets. Organizations use these collected facts and insights for efficient production, business growth, and to predict user requirements. Probability distribution plays a significant role in performing data analysis equipping a dataset for training a model. In this article, you will learn about the types of Probability Distribution, random variables, types of discrete distributions, and continuous distribution.  What is Probability Distribution? A Probability Distribution is a statistical method that determines all the probable values and possibilities that a random variable can deliver from a particular range. This range of values will have a lower bound and an upper bound, which we call the minimum and the maximum possible values.  Various factors on which plotting of a value depends are standard deviation, mean (or average), skewness, and kurtosis. All of these play a significant role in Data science as well. We can use probability distribution in physics, engineering, finance, data analysis, machine learning, etc. Significance of Probability distributions in Data Science In a way, most of the data science and machine learning operations are dependent on several assumptions about the probability of your data. Probability distribution allows a skilled data analyst to recognize and comprehend patterns from large data sets; that is, otherwise, entirely random variables and values. Thus, it makes probability distribution a toolkit based on which we can summarize a large data set. The density function and distribution techniques can also help in plotting data, thus supporting data analysts to visualize data and extract meaning. General Properties of Probability Distributions Probability distribution determines the likelihood of any outcome. The mathematical expression takes a specific value of x and shows the possibility of a random variable with p(x). Some general properties of the probability distribution are – The total of all probabilities for any possible value becomes equal to 1. In a probability distribution, the possibility of finding any specific value or a range of values must lie between 0 and 1. Probability distributions tell us the dispersal of the values from the random variable. Consequently, the type of variable also helps determine the type of probability distribution.Common Data Types Before jumping directly into explaining the different probability distributions, let us first understand the different types of probability distributions or the main categories of the probability distribution. Data analysts and data engineers have to deal with a broad spectrum of data, such as text, numerical, image, audio, voice, and many more. Each of these have a specific means to be represented and analyzed. Data in a probability distribution can either be discrete or continuous. Numerical data especially takes one of the two forms. Discrete data: They take specific values where the outcome of the data remains fixed. Like, for example, the consequence of rolling two dice or the number of overs in a T-20 match. In the first case, the result lies between 2 and 12. In the second case, the event will be less than 20. Different types of discrete distributions that use discrete data are: Binomial Distribution Hypergeometric Distribution Geometric Distribution Poisson Distribution Negative Binomial Distribution Multinomial Distribution  Continuous data: It can obtain any value irrespective of bound or limit. Example: weight, height, any trigonometric value, age, etc. Different types of continuous distributions that use continuous data are: Beta distribution Cauchy distribution Exponential distribution Gamma distribution Logistic distribution Weibull distribution Types of Probability Distribution explained Here are some of the popular types of Probability distributions used by data science professionals. (Try all the code using Jupyter Notebook) Normal Distribution: It is also known as Gaussian distribution. It is one of the simplest types of continuous distribution. This probability distribution is symmetrical around its mean value. It also shows that data at close proximity of the mean is frequently occurring, compared to data that is away from it. Here, mean = 0, variance = finite valueHere, you can see 0 at the center is the Normal Distribution for different mean and variance values. Here is a code example showing the use of Normal Distribution: from scipy.stats import norm  import matplotlib.pyplot as mpl  import numpy as np  def normalDist() -> None:      fig, ax = mpl.subplots(1, 1)      mean, var, skew, kurt = norm.stats(moments = 'mvsk')      x = np.linspace(norm.ppf(0.01),  norm.ppf(0.99), 100)      ax.plot(x, norm.pdf(x),          'r-', lw = 5, alpha = 0.6, label = 'norm pdf')      ax.plot(x, norm.cdf(x),          'b-', lw = 5, alpha = 0.6, label = 'norm cdf')      vals = norm.ppf([0.001, 0.5, 0.999])      np.allclose([0.001, 0.5, 0.999], norm.cdf(vals))      r = norm.rvs(size = 1000)      ax.hist(r, normed = True, histtype = 'stepfilled', alpha = 0.2)      ax.legend(loc = 'best', frameon = False)      mpl.show()  normalDist() Output: Bernoulli Distribution: It is the simplest type of probability distribution. It is a particular case of Binomial distribution, where n=1. It means a binomial distribution takes 'n' number of trials, where n > 1 whereas, the Bernoulli distribution takes only a single trial.   Probability Mass Function of a Bernoulli’s Distribution is:  where p = probability of success and q = probability of failureHere is a code example showing the use of Bernoulli Distribution: from scipy.stats import bernoulli  import seaborn as sb    def bernoulliDist():      data_bern = bernoulli.rvs(size=1200, p = 0.7)      ax = sb.distplot(          data_bern,           kde = True,           color = 'g',           hist_kws = {'alpha' : 1},          kde_kws = {'color': 'y', 'lw': 3, 'label': 'KDE'})      ax.set(xlabel = 'Bernouli Values', ylabel = 'Frequency Distribution')  bernoulliDist() Output:Continuous Uniform Distribution: In this type of continuous distribution, all outcomes are equally possible; each variable gets the same probability of hit as a consequence. This symmetric probabilistic distribution has random variables at an equal interval, with the probability of 1/(b-a). Here is a code example showing the use of Uniform Distribution: from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb  def uniformDist():      sb.distplot(random.uniform(size = 1200), hist = True)      mpl.show()  uniformDist() Output: Log-Normal Distribution: A Log-Normal distribution is another type of continuous distribution of logarithmic values that form a normal distribution. We can transform a log-normal distribution into a normal distribution. Here is a code example showing the use of Log-Normal Distribution import matplotlib.pyplot as mpl  def lognormalDist():      muu, sig = 3, 1      s = np.random.lognormal(muu, sig, 1000)      cnt, bins, ignored = mpl.hist(s, 80, normed = True, align ='mid', color = 'y')      x = np.linspace(min(bins), max(bins), 10000)      calc = (np.exp( -(np.log(x) - muu) **2 / (2 * sig**2))             / (x * sig * np.sqrt(2 * np.pi)))      mpl.plot(x, calc, linewidth = 2.5, color = 'g')      mpl.axis('tight')      mpl.show()  lognormalDist() Output: Pareto Distribution: It is one of the most critical types of continuous distribution. The Pareto Distribution is a skewed statistical distribution that uses power-law to describe quality control, scientific, social, geophysical, actuarial, and many other types of observable phenomena. The distribution shows slow or heavy-decaying tails in the plot, where much of the data reside at its extreme end. Here is a code example showing the use of Pareto Distribution – import numpy as np  from matplotlib import pyplot as plt  from scipy.stats import pareto  def paretoDist():      xm = 1.5        alp = [2, 4, 6]       x = np.linspace(0, 4, 800)      output = np.array([pareto.pdf(x, scale = xm, b = a) for a in alp])      plt.plot(x, output.T)      plt.show()  paretoDist() Output:Exponential Distribution: It is a type of continuous distribution that determines the time elapsed between events (in a Poisson process). Let’s suppose, that you have the Poisson distribution model that holds the number of events happening in a given period. We can model the time between each birth using an exponential distribution.Here is a code example showing the use of Pareto Distribution – from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb  def expDist():      sb.distplot(random.exponential(size = 1200), hist = True)      mpl.show()   expDist()Output:Types of the Discrete probability distribution – There are various types of Discrete Probability Distribution a Data science aspirant should know about. Some of them are – Binomial Distribution: It is one of the popular discrete distributions that determine the probability of x success in the 'n' trial. We can use Binomial distribution in situations where we want to extract the probability of SUCCESS or FAILURE from an experiment or survey which went through multiple repetitions. A Binomial distribution holds a fixed number of trials. Also, a binomial event should be independent, and the probability of obtaining failure or success should remain the same. Here is a code example showing the use of Binomial Distribution – from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb    def binomialDist():      sb.distplot(random.normal(loc = 50, scale = 6, size = 1200), hist = False, label = 'normal')      sb.distplot(random.binomial(n = 100, p = 0.6, size = 1200), hist = False, label = 'binomial')      plt.show()    binomialDist() Output:Geometric Distribution: The geometric probability distribution is one of the crucial types of continuous distributions that determine the probability of any event having likelihood ‘p’ and will happen (occur) after 'n' number of Bernoulli trials. Here 'n' is a discrete random variable. In this distribution, the experiment goes on until we encounter either a success or a failure. The experiment does not depend on the number of trials. Here is a code example showing the use of Geometric Distribution – import matplotlib.pyplot as mpl  def probability_to_occur_at(attempt, probability):      return (1-p)**(attempt - 1) * probability  p = 0.3  attempt = 4  attempts_to_show = range(21)[1:]  print('Possibility that this event will occur on the 7th try: ', probability_to_occur_at(attempt, p))  mpl.xlabel('Number of Trials')  mpl.ylabel('Probability of the Event')  barlist = mpl.bar(attempts_to_show, height=[probability_to_occur_at(x, p) for x in attempts_to_show], tick_label=attempts_to_show)  barlist[attempt].set_color('g')  mpl.show() Output:Poisson Distribution: Poisson distribution is one of the popular types of discrete distribution that shows how many times an event has the possibility of occurrence in a specific set of time. We can obtain this by limiting the Bernoulli distribution from 0 to infinity. Data analysts often use the Poisson distributions to comprehend independent events occurring at a steady rate in a given time interval. Here is a code example showing the use of Poisson Distribution from scipy.stats import poisson  import seaborn as sb  import numpy as np  import matplotlib.pyplot as mpl  def poissonDist():       mpl.figure(figsize = (10, 10))      data_binom = poisson.rvs(mu = 3, size = 5000)      ax = sb.distplot(data_binom, kde=True, color = 'g',                       bins=np.arange(data_binom.min(), data_binom.max() + 1),                       kde_kws={'color': 'y', 'lw': 4, 'label': 'KDE'})      ax.set(xlabel = 'Poisson Distribution', ylabel='Data Frequency')      mpl.show()      poissonDist() Output:Multinomial Distribution: A multinomial distribution is another popular type of discrete probability distribution that calculates the outcome of an event having two or more variables. The term multi means more than one. The Binomial distribution is a particular type of multinomial distribution with two possible outcomes - true/false or heads/tails. Here is a code example showing the use of Multinomial Distribution – import numpy as np  import matplotlib.pyplot as mpl  np.random.seed(99)   n = 12                      pvalue = [0.3, 0.46, 0.22]     s = []  p = []     for size in np.logspace(2, 3):      outcomes = np.random.multinomial(n, pvalue, size=int(size))        prob = sum((outcomes[:,0] == 7) & (outcomes[:,1] == 2) & (outcomes[:,2] == 3))/len(outcomes)      p.append(prob)      s.append(int(size))  fig1 = mpl.figure()  mpl.plot(s, p, 'o-')  mpl.plot(s, [0.0248]*len(s), '--r')  mpl.grid()  mpl.xlim(xmin = 0)  mpl.xlabel('Number of Events')  mpl.ylabel('Function p(X = K)') Output:Negative Binomial Distribution: It is also a type of discrete probability distribution for random variables having negative binomial events. It is also known as the Pascal distribution, where the random variable tells us the number of repeated trials produced during a specific number of experiments.  Here is a code example showing the use of Negative Binomial Distribution – import matplotlib.pyplot as mpl   import numpy as np   from scipy.stats import nbinom    x = np.linspace(0, 6, 70)   gr, kr = 0.3, 0.7        g = nbinom.ppf(x, gr, kr)   s = nbinom.pmf(x, gr, kr)   mpl.plot(x, g, "*", x, s, "r--") Output: Apart from these mentioned distribution types, various other types of probability distributions exist that data science professionals can use to extract reliable datasets. In the next topic, we will understand some interconnections & relationships between various types of probability distributions. Relationship between various Probability distributions – It is surprising to see that different types of probability distributions are interconnected. In the chart shown below, the dashed line is for limited connections between two families of distribution, whereas the solid lines show the exact relationship between them in terms of transformation, variable, type, etc. Conclusion  Probability distributions are prevalent among data analysts and data science professionals because of their wide usage. Today, companies and enterprises hire data science professionals in many sectors, namely, computer science, health, insurance, engineering, and even social science, where probability distributions appear as fundamental tools for application. It is essential for Data analysts and data scientists. to know the core of statistics. Probability Distributions perform a requisite role in analyzing data and cooking a dataset to train the algorithms efficiently. If you want to learn more about data science - particularly probability distributions and their uses, check out KnowledgeHut's comprehensive Data science course. 
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Types of Probability Distributions Every Data Scie...

Data Science has become one of the most popular in... Read More