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Implementing Common Probability Distributions in Python Programming: Step-by-Step Examples

Posted on by Imarticus

Probability distributions are the mathematical functions that describe the likelihood of different possible outcomes of a random variable. Understanding and applying probability distributions is crucial for statistical modelling, hypothesis testing, and risk assessment in data science and machine learning.

Python, with its rich ecosystem of libraries like NumPy, SciPy, and Matplotlib, provides powerful tools for working with probability distributions. If you wish to learn Python programming and other concepts such as probability distribution, a solid data analytics course can definitely help.

Key Concepts in Probability Distributions

  • Random Variable: A random variable is a variable whose value is a numerical outcome of a random phenomenon. It can be discrete or continuous.
  • Probability Density Function (PDF): The PDF describes the relative likelihood of a random variable taking on a specific value for continuous random variables.
  • Probability Mass Function (PMF): The PMF gives the probability of a random variable taking on a specific value for discrete random variables.
  • Cumulative Distribution Function (CDF): The CDF gives the probability that a random variable is less than or equal to a specific value.

Common Probability Distributions

Discrete Distributions

  1. Bernoulli Distribution: Models a binary random variable with two possible outcomes: success (1) or failure (0).
  2. Binomial Distribution: Models the number of successes in a fixed number of independent Bernoulli trials.
  3. Poisson Distribution: Models the number of events that occur in fixed intervals of time or space.   
  4. Geometric Distribution: Models the number of failures before the first success in a sequence of Bernoulli trials.   
  5. Negative Binomial Distribution: Models the number of failures before a specified number of successes in a sequence of Bernoulli trials.

Continuous Distributions

  1. Uniform Distribution: Models a random variable equally likely to take on any value within a specified range.
  2. Normal Distribution: Models a continuous random variable with a bell-shaped curve. It is widely used in statistics due to the Central Limit Theorem.
  3. Exponential Distribution: Models the time between events in a Poisson process.
  4. Gamma Distribution: Generalises the exponential distribution and is often used to model waiting times.
  5. Beta Distribution: Models a random variable that takes on values between 0 and 1. It is often used to represent probabilities or proportions.

Implementing Probability Distributions in Python

Python programming offers several libraries for working with probability distributions. The most commonly used for probability distributions in Python are NumPy and SciPy.

NumPy

  • Generating Random Variables:
import numpy as np

# Generate 100 random numbers from a standard normal distribution

random_numbers = np.random.randn(100)

  • Calculating Probabilities:
from scipy.stats import norm

# Probability of a z-score less than 1.96

probability = norm.cdf(1.96)

SciPy

  • Probability Density Functions (PDFs):
from scipy.stats import norm

# PDF of a standard normal distribution at x = 1

pdf_value = norm.pdf(1)

  • Cumulative Distribution Functions (CDFs):
from scipy.stats import expon

# CDF of an exponential distribution with rate parameter 2 at x = 3

cdf_value = expon.cdf(3, scale=1/2)

  • Inverse Cumulative Distribution Functions (ICDFs):
from scipy.stats import chi2

# 95th percentile of a chi-squared distribution with 10 degrees of freedom

percentile = chi2.ppf(0.95, 10)

Visualizing Probability Distributions in Python Programming

Matplotlib is a powerful library for visualizing probability distributions Python.

Example:

import matplotlib.pyplot as plt

import numpy as np

from scipy.stats import norm

# Generate x-axis values

x = np.linspace(-3, 3, 100)

# Plot the PDF of a standard normal distribution

plt.plot(x, norm.pdf(x))

plt.xlabel(‘x’)

plt.ylabel(‘PDF’)

plt.title(‘Standard Normal Distribution’)

plt.show()

Applications of Probability Distributions

Probability distributions have a wide range of applications in various fields:   

  • Data Science: Modeling data, generating synthetic data, and making predictions.
  • Machine Learning: Building probabilistic models, Bayesian inference, and generative models.
  • Finance: Risk assessment, portfolio optimisation, and option pricing.
  • Statistics: Hypothesis testing, confidence intervals, and statistical inference.
  • Physics: Quantum mechanics, statistical mechanics, and particle physics.

Fitting Probability Distributions to Data

One of the essential applications of probability distributions is fitting them to real-world data. This involves estimating the parameters of a distribution that best describes the observed data. Common techniques for parameter estimation include:

  • Maximum Likelihood Estimation (MLE): This method finds the parameter values that maximise the likelihood of observing the given data.
  • Method of Moments: This method equates the theoretical moments of the distribution (e.g., mean, variance) to the corresponding sample moments.

Python’s SciPy library provides functions for fitting various probability distributions. For example, to fit a normal distribution to a dataset:

from scipy.stats import norm

import numpy as np

# Sample data

data = np.random.randn(100)

# Fit a normal distribution

params = norm.fit(data)

mean, std = params

print(“Estimated mean:”, mean)

print(“Estimated standard deviation:”, std)

Simulating Random Variables

Simulating random variables from a specific distribution is useful for various purposes, such as Monte Carlo simulations, statistical testing, and generating synthetic data. Python’s NumPy library provides functions for generating random numbers from many distributions:

import numpy as np

# Generate 100 random numbers from a standard normal distribution

random_numbers = np.random.randn(100)

# Generate 100 random numbers from a uniform distribution between 0 and 1

uniform_numbers = np.random.rand(100)

# Generate 100 random numbers from an exponential distribution with rate parameter 2

exponential_numbers = np.random.exponential(scale=0.5, size=100)

Statistical Inference and Hypothesis Testing

Probability distributions are crucial in statistical inference, which involves concluding a population based on sample data. Hypothesis testing, for instance, involves formulating null and alternative hypotheses and using statistical tests to determine whether to reject or fail to reject the null hypothesis.

Python’s SciPy library provides functions for performing various statistical tests, such as t-tests, chi-squared tests, and ANOVA.

Bayesian Inference

Bayesian inference is a statistical method that uses Bayes’ theorem to update beliefs about a parameter or hypothesis as new evidence is observed. Probability distributions are fundamental to Bayesian inference, representing prior and posterior beliefs.   

Python libraries like PyMC3 and Stan are powerful tools for implementing Bayesian models. They allow you to define probabilistic models, specify prior distributions, and perform Bayesian inference using techniques like Markov Chain Monte Carlo (MCMC).

Wrapping Up

Understanding and applying probability distributions is a fundamental skill for data scientists, machine learning engineers, and statisticians. With its powerful libraries, Python provides an excellent platform for working with probability distributions.

If you wish to become an expert in Python programming and data analytics, enrol in the Postgraduate Program In Data Science And Analytics by Imarticus.

Frequently Asked Questions

What is the difference between a probability density function (PDF) and a probability mass function (PMF)?

A PDF is used for continuous random variables, representing the likelihood of a variable taking on a specific value within a range. Conversely, a PMF is used for discrete random variables, giving the probability of a variable taking on a specific exact value. A Python probability tutorial will help you learn about these two functions.

Why is the normal distribution so important in statistics?

The normal distribution (called the bell curve), is fundamental in statistics due to the Central Limit Theorem. This theorem states that the distribution of sample means tends to be normal, regardless of the underlying population distribution, as the sample size increases.

How can I choose the right probability distribution for my data?

Selecting the appropriate probability distribution depends on the characteristics of your data. Consider factors like the shape of the distribution, the range of possible values, and any underlying assumptions. Visualizing probability distributions Python and using statistical tests can aid in the selection process.

What is the role of probability distributions in machine learning?

Probability distributions are essential in machine learning for tasks like modelling uncertainty, generating data, and making probabilistic predictions. They are used in various algorithms, including Bayesian inference, Gaussian mixture models, and hidden Markov models. You can learn more with the help of a Python probability tutorial.

 Learn The Basics Of Python In Six Minutes

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Learn The Basics Of Python In Six Minutes

Learning to program or begin with a new language is not easy: We’re sure your mind is racing with all of the new concepts you need to absorb, and the onslaught of knowledge might be daunting at times.

Python is currently one of the most popular—and well-paid—programming languages globally.

This guide will teach you the fundamentals of Python. This Python training is ideal if you are new to Python programming. You will be familiar with Python programming after finishing this guide.

Why learn Python? 

It’s a programming language that’s been around for almost 30 years, and millions of individuals worldwide use it. Python is simple to learn, making it an excellent choice for beginning programmers. It also increases your productivity when writing code, so this guide will assist you in wanting to get started with coding or enhancing your current skill set!

Python installation is relatively simple, and many Linux and UNIX systems now contain a modern Python. Some Windows machines (particularly those from HP) now come pre-installed with Python. 

Here we start with the basics of Python Programming:

Syntax

There are no required statement termination characters in Python. To begin a block, indent it, and to end it, dedent it. Statements that need an indentation level get followed by a colon (:). Comments are single-line strings that begin with the pound (#) sign; multi-line strings are for multi-line comments. The equals sign (“=”) to assign values (in actuality, objects with names), whereas the equality sign (“==”) test equality. Using the += and -= operators, you may increase or decrease numbers by the right-hand amount. This works with a variety of data kinds, including strings. 

Data types: 

Python data structures include lists, tuples, and dictionaries. The sets library contains sets (but are built-in in Python 2.5 and later). Lists are one-dimensional arrays, and dictionaries are associative arrays (commonly known as hash tables). Tuples are immutable one-dimensional arrays (Python “arrays” can be of any type so that you can combine integers, texts, and so on). 

Strings

Its strings can contain either single or double quotation marks, and you can put one type of quotation mark inside another (for example, “He said ‘hello.'” is legitimate). Strings with many lines are in _triple, and double-quotes.

Flow control statements

If, for, and while are flow control statements. Instead of using if, there is no switch. Use to iterate over the members of a list. Use range(number>) to get a list of numbers you can iterate through.

Learn data analytics online with Imarticus Learning

This placement-assured data analytics course will teach students how to use Data Science in the real world and build complicated models that yield critical business insights and projections.

Course Benefits for Learners:

  • Students must understand the fundamentals of data analytics and machine learning and the most in-demand data science tools and methodologies.
  • Learners earn a tableau certification by participating in 25 real-world projects and case studies led by business partners.
  • Recent graduates and early-career workers should consider enrolling in data science and analytics programs since they are among the most in-demand talents in today’s industry.

Contact us through chat support, or drive to one of our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, and Gurgaon. 

Top Python Projects You Should Consider Learning?

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Understanding Python

Python is a high-level programming language that is used by programmers for general purpose. There’s a whole lot you can do after learning python, it can be used to develop web applications, websites, etc. You can also develop desktop GUI applications using python. Python is more advanced than other traditional programming languages and provides more flexibility by allowing you to focus on core functionalities and taking care of other common programming tasks.

One of the major benefits of python as a programming language is that the syntax rules of Python is very transparent and allows you to express models without writing any additional codes. Python also focuses on readability of codes. Building custom applications without writing additional code is also an advantage that Python offers. Being an interpreted programming language Python allows you to run the same code on multiple platforms without recompilation.

Why learn Python?

One of the major advantages of python programming language is that it is relatively simple to learn and has a smooth learning curve. It is a beginner-friendly programming language. The simple syntax used by Python makes it easy to learn programming language as compared to programming languages like Java or C++.

Python has a standard library and the external libraries are also available for users. This can help you to develop concrete applications quickly. You can easily learn python by enrolling in python programming course online. Let’s take a look at some of the top python projects that you can easily learn.

Guess the Number

This project will use the random module in Python. Some of the concepts that will be used while doing these projects are random functions, while loops, if/else statements, variables, integers, etc. Here, the program will begin by generating a random number that is unknown to the user. The user will have to provide and input by guessing this number.

If the user’s input doesn’t match the actual random number generated using the program, the output should be provided to indicate how close or far was the guess from the actual number. A correct guess by the user will correspond to a positive indication by the program.

You will need to apply functions to check three parts of this program; the first is the actual input by the user, secondly the difference between the input and the number generated and lastly to make comparisons between the numbers.

Password Generator

This is a very practical project given the use of password generators for everyday applications. You simply need to write a programme that helps to generate a random password for the user. Inputs required from the user are the length of the password to be generated, the frequency of letter and numbers in the password, etc. A mix of upper and lower case letters and symbols is recommended. The minimum length of the password should be 6 characters long.

Hangman

You are already familiar with “Guess the Number” game, this is more of a “Guess the Word” game. The user has to input letters as guess inputs. A limit is required to be set on the total number of guesses a user can make. It is advisable to give the user 6 attempts at most to guess.  You will need to apply functions to check if the user has made a letter input or not.

You will also need to check if the input shared is there in the hidden word or not. You will have to find a solution to grab a word that will be used for guessing. The main concepts applicable in the Hangman project are variables, Boolean, char, string, length, integer, etc. It is comparatively more complex than the projects mentioned above.

Want to Learn Advanced Analytics For Marketing Purposes, In What Order Do You Need to Learn Big Data?

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Generally, Data Analysis is a comprehensive process that involves taking unstructured information, inspecting it, cleansing, and then transforming key insights in a structured form. With advanced analytics, we use these data to further find specific patterns and draw conclusions that assist a large organization to make precise decisions for growing in a positive direction.

Data analysis nowadays is used across several businesses with a different approach, diverse techniques, and methods to help them make a precise decision for improvising efficiently. At Imarticus Learning, we help new age professionals to learn advanced analytics with dedicated courses to upskill them to match the corporate world requirements. 

A simple data project follows this structure in the form as:

SQL for Extracting and transforming data,

Tableau for Data Visualisation & insights building as Hypothesis building,

R for Statistical Data Analysis with Bivariate & univariate analysis of variables, and

Python for Model development / Hypothesis testing.

Data analyst professionals deal with a very high amount of data daily. The first step is to learn SQL to analyze, extract, aggregate, and transform data for a more purposeful understanding. So, as a professional working in the data analysis field, SQL is the foremost priority to learn and manage data properly.

These datasets can have 1 million+ rows, here Tableau will work on visualizing data to bring insights or hypotheses. With Tableau, one can effectively track a million rows of information in data form to create useful insights.

R is another programming language used specifically for data analysis with the environment suited for statistical computing. One can also visualize data, blend data, build a statistical model, and perform complex transformations. R language is also preferred for developing statistical software so data analysts must have an understanding of its effectiveness.

Python is a general-purpose high-level programming language that most coders prefer to use. Python is used to develop algorithms from these large sets of data variables with scripts that make effective management to find relations and goals from the data itself. One must learn Python programming for building a sophisticated career in data scientists. 

Although a data structure follows a specific path from SQL to Tableau to R to Python, still the goal and objective of the project define the purposeful use of that language. SQL helps us to query data properly; with Tableau, we learn to visualize data, R is better for exploration, while Python works better to get high production.

A well-organized course can help you to understand the right purpose for each of these languages precisely. Though an individual may not have expertise in each of these languages still, if you are opting for a career in Data analysis, you must understand the scope of SQL, Tableau, R, and Python to grow in the right direction.

At Imarticus Learning we offer several programs for professionals to learn advanced analytics and offer their expertise to the corporate world with definite preparations as well as courses to match their expectations. 

Introduction To Python Set and Frozen-set Methods

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Introduction To Python Set and Frozen-set Methods

Python is a widely-used programming language. No doubt that learning Python will bring you better job prospects. The advantage of Python is that it is generally smaller than other languages like Java. As a programmer, you will need to type only fewer indentations, which makes them highly readable highly. Many global market leaders like Google, Facebook, Amazon, Instagram, etc. are using Python. Learning Python will help you find a career in machine learning as well. A program widely used in GUI applications, image processing, multimedia, text processing and many more, Learning Python is highly advisable if you are looking at a career in IT.

Sets in Python

Sets in Python represent a collection of data. This unordered collection is mutable and iterable. It doesn’t have duplicate elements. The Python set is similar to the mathematical set. Using a set is recommended over a list because the set has several advantages. Based on the hash table, it is very easy to check if a particular element is included in the set. This is not possible if you use a list.

Properties of a Python set()

There are no parameters to create an empty set. If you create a dictionary using a set, the values will not remain after conversion. However, the keys will remain.

Sets have many methods:

  1. Method of addition (x): This method is used to add one or more items to a set if currently it is not included in the set.
  2. Method of union (s): This method is used to unite two sets. The operator use is ‘|’, which is similar to set1.union(set2)
  3. Method of intersection (s): This method is used for the intersection between two sets. You can also use the ‘&’ sign as the operator here.
  4. Method of difference (s): This method is used to return a set that contains replicates the elements present in the invoking set, but not present in the second set. The operator used here is ‘-‘.
  5. Method of the clear set (): This method is used to empty the whole set.

Operators for sets and frozen sets

Operator Function
key in s containment check
key not in s non-containment check
s1 == s2 s1 is equal to s2
s1 != s2  S1 is not equal to s2
s1 <= s2 s1is subset of s2
s1 < s2 s1 is a proper subset of s2
s1 >= s2 s1is superset of s2
s1 > s2 s1 is a proper superset of s2
s1 | s2 the union of s1 and s2
s1 & s2 the intersection of s1 and s2
s1 – s2 the set of elements in s1 but not s2
s1 ˆ s2 the set of elements in precisely one of s1 or s2

Frozen sets

Frozen sets, unlike the set, are immutable. Once created, the frozen set remains the same. It cannot be changed. This is the reason we use a frozen set as a key in Dictionary. It accepts an iterable object as its input and makes them unchangeable. However, it is not guaranteed that the order of the element will be preserved. In a frozenset, if no parameters are passed to the () function, an empty frozen set is returned. If you change the frozenset object by mistake, you will get an error saying that the item assignment is not supported by frozenset object.

Conclusion

Equivalent to the data set in mathematics, Python set is a data structure similar to the mathematical sets. It consists of several elements, but the order in which the elements should be deployed in the set is undefined.  You can perform addition, deletion, iterate, union, intersection, and differences on the sets.

How AI in The Energy Sector Can Help to Solve The Climate Crisis?

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How AI in the Energy Sector Can Help Solve the Climate Crisis

Have you not complained about the crisis that is looming large in our environment? The news reports of untimely floods, missing rain patterns, fires in forests, carbon emissions and smog affect each and every one of us. The Davos meeting of the World Economic Forum threw up some important measures that we need to take in enabling AI, ML and technology as a whole in symbiotically tackle the climate crisis of all times.

The main cause of the changes in climate is being attributed to emissions of carbon and greenhouse gases. And each and every person in tandem with AI, technology and the big industrial players have a bounden duty to support such measures and immediately move to reduce these emissions if we wish to halt such catastrophic climate changes. Noteworthy is the funding of nearly billion dollars in such ventures by Bill Gates and Facebook’s Mark Zuckerberg.

Here is the list of the top suggestions. In all these measures one looks to technology and artificial intelligence to aid and achieve what we singularly cannot do. This is because the noteworthy improvements brought about by AI are

AI helps compile and process data:

We just are not doing enough to save our planet. The agreement between countries in Paris to be implementable means elimination of all energy sources of fossil-fuel. AI enabled with intelligent ML algorithms can go a long way in processing unthinkable volumes of data and providing us with the insight and forecasts to reverse the climatic changes, use of fossil fuels, reduction of carbon emissions, waste etc, and setting up environment-friendly green systems of operations.

AI can help reduce consumption of energy by ‘server farms’

The widespread use of digitalization has led to server farms meant to store data. According to the Project Manager, Ms. Sims Witherspoon at Deepmind the AI British subsidiary of Alphabet when speaking to DW said that they have developed a bot named Go-playing with algorithms that are “general purpose” in a bid to reduce the cooling energy of data centers of Google by a whopping 40%. This does amount to a path-breaking achievement when you consider that a total of 3 percent of the energy globally used is just used by the ‘server farms’ to maintain data!

Encouraging the big players to be guardians of the climate.
The industrial giants are using technology, AI and ML to reduce their footprints of carbon emissions. AI tools from Microsoft and Google are aiding maximized recovery of natural resources like oil, coal, etc. Though with no particular plans or place in the overall plan-of-action such measures do go a long way in preserving the environment through reduced emissions and set the trend into motion.

Using smartphone assistants to nudge for low-carbon climate-friendly changes.
The rampant use of smartphones and devices of AI makes this option possible and along with zero-click AI enabled purchases the virtual assistant bolstered through ML algorithms and tweaked infrastructure can be used to influence choices of low-carbon climatic and emission-reduction changes.

Social media can transform education and societal choices.
The biggest influencer of social change is the social media platforms like Instagram, Facebook, Twitter, etc these can be harnessed to publicize, educate and act on choices that help reduce such carbon emissions and use of resources.

The reuse mantra and future design.
Almost all designing is achieved through AI which can help us design right, have default zero-carbon designs, commit to the recycling of aluminium and steel, reward lower carbon footprints, grow and consume optimum foods and groceries and create green and clean smart cities.

Summing up the suggestions to be placed at the UN Global Summit for Good AI at Geneva, it is high time we realize that the future lies in data and its proper use through AI and empowering ML. We need new standards for use of the media and advertising digitally. All countries need to globally work to reduce the use of fossil fuels in automobiles and transportation. We must cut our emissions by half in less than a decade and this is possible through proper use of data, AI, ML, and digitization.

If you care enough to be a part of this pressing solution to environmental change, learn at Imarticus Learning, how AI has the potential to harness data and control the damage to our environment. Act today.

What is the Learning Curve for Python Language?

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What is the Learning Curve for Python Language?

Most people will tell you that Python is the easiest language to learn and should be one of the first languages that you should learn when considering a career in Python programming. Well, they are mostly right, parting with a good piece of advice. And most probably you should take these comments seriously.

However, before you kick start with unclear expectations, you should be clear about what does it truly mean by ‘learning the language’, is it being a pro and acquiring absolute knowledge of Python, or to begin with, working knowledge, that helps you start with the basics, while you can continue to learn and gain additional knowledge on the go. Python is an awesome choice, with a relatively faster learning curve, which is determined by various factors and disclaimers.

best data analytics and machine learning courses

For starters, Python should be your first programming language, simply because not only will you be able to pick up the basics quickly you will also be able to adapt to the mindset of a programmer. Python is easy to learn with a steady learning curve.

Especially when compared to other programming languages that have a very steep learning curve. Mainly because Python is very readable, with considerably easy syntax, thus a new learner will be able to maintain the focus on programming concepts and paradigms, rather than memorizing unfathomable syntax.

For those thinking that Python is said to be too easy to learn, perhaps it might not be sufficient, and hence while it could have a gradual learning curve, in terms of applicability it might not be adequate, don’t be misguided. Python is not easy because it does not have deep programming capabilities, on the contrary, Python is superefficient, so much so that NASA uses it.

So as a beginner, when you start adapting Python to your daily work, you will notice that with a combination of theoretical learning and practical applicability of the same at work, one will be able to accomplish almost anything they desire to, through its use. With the right intent, applicability, and ambition one can even perhaps design a game or perform a complex task, without prior knowledge of the language.

The learning curve for Python also depends on certain obvious factors like your prior knowledge, exposure to the concepts of programming, etc…

If you are a beginner, devoting a couple of hours on understanding the language, then say in a month, you will be able to get a good feel of the language, mostly so if Python is your first language. If you have previous knowledge of programming, Javascript, C++, or if you understand the concepts of variables, control loops, etc…, then your hold on the language is even faster.

Either way, when learning is combined with practical real-life applicability, within a few days or a month you will be able to write programs, mostly expected out of a new learner. If the same method of learning is adapted for a month or two, along with exposure in programming, one will gain knowledge of the built-in functions and general features of the language. This will help and build confidence in the new learner to enhance their capabilities in programming.

Once the basics are in place, a new learner can then delve further to leverage the power of Python’s libraries and modules which are available as an open-source.

To conclude, it is a fact that Python is designed to be used in complex programming, yet at the same time, it is easy to learn and is truly a lightweight language. And once the basics are in place you can take up tutorials and advanced courses, to enhance your understanding.

Top Reasons To Learn Python

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Top Reasons To Learn Python

While a college education is of the utmost importance, how many times have we seen a self-taught entrepreneur or an innovator pass us by, while we were busy looking for job vacancies? The major difference between that professional and you is that they were able to successfully learn all those industry-relevant tools, while you were busy studying out of generic books. This trend of acquiring skills in order to fit in, with Industry requirements is catching on really fast. Institutes like Imarticus Learning, which offer a number of comprehensive courses in the field of Analytics and Finance, are successful in training their students, in keeping with all the industry-relevant talents, the HR managers are looking for.
Continuing in the same vein, in the field of Data Science, it is very advantageous to be adept in the data analytics tool, called Python. If you are a data enthusiast, wondering why should you be learning this tool, here’s a list of reasons, just for your benefit;

It’s a Very Easy-to-learn Programming Tool

While we do agree, that learning a programming language is in no way as exciting as learning how to race cars, Python happens to be one of those tools, which was specifically designed for the newbies. Reading this would be as easy, as doing a first-grade math assignment, as this programming language is entirely easy and comfortable for someone from a non-technical background. Another reason why this language is so economical to learn is that it requires much less amount of code, for instance, any Python code is about three to five times shorter than the Java code and about 5-20 times shorter, than the C++ code.

Better for Your Progress

Python can very well be called your stepping stone, to a better career in the field of analytics. HR managers are always on the lookout for well-rounded programmers, and having the knowledge of Python will surely get you there. Like other key programming languages, Python is also an object-oriented language. This will surely help you adapt very easily in any type of environment.

There’s a Micro-computer, Specially Made for Python Language

Raspberry Pi is a card-sized, extremely inexpensive micro-computer, which usually specializes in video game consoles, remote-controlled cars, and the like. Python is considered to be the main programming language here and it’s so easy and comfortable to work with, that even the kids are using it to build arcade machines and pet feeders and the like.

Handsome Rewards

Reports state, that Python has had the largest job demand growth, in the past three years. In the year 2014, while the hiring demand for IT professionals dipped down, on the other hand, the demand for Python programmers increased, and that too by about 8.7%. Prospective employers are Google, Yahoo!, Disney, Nokia, IBM and so on.

It Functions Online as Well

Web Development is all the rage these days and all thanks to Python, you will now be able to take a huge bite of that as well. Django is an open-sourced application framework, which is entirely written in Python and is also the foundation for a number of popular sites like Pinterest, The New York Times, The Guardian, Bit Bucket and so on.
There you go, five extremely appealing reasons why you must learn Python.

How Does Facebook Identify Where You Are From Your Profile Photo?

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We all know that Mark Zuckerberg of Facebook is strongly passionate about Machine Learning and Artificial Intelligence, but how has that impacted our everyday online social life?
You may think you’re just uploading a photo, but facebook knows how many people are there, whether you’re outside or inside, and if you’re smiling.
The technology that Facebook uses, Artificial Intelligence, is a rigorous science that focuses on designing systems that make use of algorithms that are much similar to that of our human brain. AI learns to recognize patterns from large amounts of data and come up with a comprehensive conclusion.

What does that have to do with how Facebook knows if I’m smiling or not?

Facebook is constantly teaching their machines to work better. By using deep learning, they train AI to structure through various processing layers and understanding an abstract representation of what the data could be. By using their system called “convolutional neural network”, the computer is able to go through layers of units and understand whether there is a dog in a photo.

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Facebook works through layers. In the first layer, it is able to identify the edges of objects. In the second layer, it is able to detect combinations and identify it to be an eye in a face or a window in a plane. The next layer combines these further and identifies them to be either an entire face or a wing on a plane. The final layer is able to further detect these combinations and identifies if it is a person or a plane.
The network needs to be able to read the labels on the database and identify which of these are labeled as humans or plants. The system learns to associate the input with the label. The way facebook works is that it is able to now identify not only that there are humans in a photo, but how many humans, whether they are indoors or outdoors, and their actions, i.e. if they are sitting or standing.
However, a photograph that has been uploaded may need to be completely zoomed in for Facebook’s AI to understand intricacies if a person is smiling or not.

It may not always be perfect in its recognition, but it’s getting there.
A lot of information can be extracted from a photograph. Facebook is only going to get better with its AI and making use of big data.

Artificial Intelligence and Machine Learning is a concept that will be looked at in Imarticus’s Data Science Prodegree. This course is a cutting-edge program designed and delivered in collaboration with Genpact, a leader in Analytics solutions. Students get their hands-on learning with 6 industry projects and work with industry mentors.

Written by Tenaz Shanice Cardoz, Marketing & Communications.

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