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Frame the Future with Digital Marketing

The Industry requires Passion and Desire to Succeed. If you are looking to break into digital marketing industry, there's no better time than now. Digital Marketing is both fast growing and incredibly competitive. It is extremely important, not only because of its rapid growth but also because it is essentially the future of marketing.Why Digital Marketing is ImportantGetting into 2017 and just by looking at the changes that have been occurring in the digital marketing field, it is definitely wise for companies to wonder how to redirect their efforts in order to come up with more accurate marketing campaigns from this point on.There are no. of businesses competing against you for your customer’s attention. It’s not enough to have a knowledge about the product and services of any businesses queries. Case Studies are a great way to learn more about marketing strategy.Businesses can use them to show how their product or service has been implemented successfully by their own customers instead of simply talking about their products. A lot of businesses make the mistake of talking about themselves too much. Don’t be one of them. Case studies are a great way to take the attention off of yourself and focus on the experience of one of your customers. Whether it’s Facebook, HP, IBM, Inbenta, or another organization, you get to show the companies that you've worked with and are willing to back you up and talk about their success with your product.  Career in Digital MarketingDigital Marketing is the promotion of products or brands via one or more forms of electronic media. These are some of the most common forms of digital marketing: Search Engine Optimization (SEO) Search Engine Marketing (SEM) Content Marketing Social Media Marketing (SMM) Pay-Per-Click Advertising (PPC) Email Marketing  Search Engine Optimization (SEO) SEO stands for Search Engine Optimization. It's a organic process of making your website content visible for the people who are looking for that. It is a set of process to (increase the amount of visitors to a websites) and enhances the visibility of the page at the top of search engine. The main focus of the SEO to helps the website achieve higher ranking in search engine results. Search Engine Marketing (SEM)Search Engine Marketing (SEM) is the process of promoting your website online, by using paid advertisement that enable website on the top search engine result page to customers to your business. Advertiser bid on certain keywords in a search engine to make their ads appear in Google search results. Content MarketingContent marketing is also the form of marketing to build a healthy and strong relationship with your targeted customers. The purpose of content marketing is to help the company to create sustainable brand loyalty and provide relevant and valuable information to consumers by adding value to them through your content. VideoVideos are a great way of marketing your content because it is able to Generate or Increase online sales and Increase brand awareness or credibility. There are a lots of video platforms like Twitter, YouTube, Facebook, Snapchat, Instagram and much more High-quality video content can be used to expose your brand to YouTube’s large and active audience. PodcastA podcast is the term related to audio. In podcast marketing, your content convert into an audio form and promote it to a place like iTunes to your audiences that prefer listening to audios. Articles – BloggingBlogging is writing or sharing content (video, images or combination of all media forms) on a website on a regular basis. An individual or business can own a blog. Social Media Marketing (SMM)Social Media Marketing (SMM) is popular form of internet marketing that uses social media networks to help in business growth and brand awareness. Social media sites like Pinterest, Facebook, Twitter, LinkedIn, and Google+ can all be used to increase traffic and attract leads to grow your business. Pay-Per-Click Advertising (PPC)Paid Search: Pay-Per-Click Advertising (PPC) is the most important types of paid campaign that advertisers use to drive traffic to their websites.Pay per click means you only pay the amount you have bid if someone clicks on your ads. In bid strategy Google chooses the bid for you with in your budget. You have to pay to each time when ads is clicked by users.By using Paid Search advertisers see immediate Results. This method is not too much expensive and Organic traffic will take days or weeks to generate traffic to your business.Email MarketingEmail Marketing is a process in which marketers or consumers use to communicate or send commercial content or data to their target customers using email. It is used to promote content and interact audience to their business site. It helps in building customer relationship and brand awareness.I hope this Article will help you to take an overview of what digital marketing is, how it works and its benefits for framing future in the digital world.  

What is Docker in DevOps? How Does it Work? Tutorial Guide For Beginners

There is a lot of technology available today in the market. There are great minds at work daily to bring people the best solutions. This makes competition fierce and allows customers a lot of options from which they can choose. The most popular amongst them is cloud technologies. They are also many DevOps tools and methods that can simplify routine issues in a software development company. You need to learn about such trends that can help you build products faster.  Developers need to deliver products constantly and services even better than their competitors. This means that many firms are embracing cloud practices and concepts like containerization. This makes DevOps tools like Docker in high demand. If you are wondering what is Docker in DevOps, you are not the only one. Here we will try to explain the concept simply. We will also introduce some benefits of using Docker that can be helpful for developers and architects.  What is DevOps?It is a combination of two words that are, “Development” and “Operations”. It is a software engineering practice that combines software development (Dev) and software operations (Ops) in an organization.   DevOps is a type of tools, practices, and cultural philosophies that enable an organization to deliver application and services fast. It is a type of ecosystem needed to automate processes between development, testing, and IT teams. This results in seamless building, testing, and launching of software efficiently. This constant cycle of collaboration and iterative improvement throughout the entire software development life cycle improves the products at a faster rate. This speed of project management leads to improved services to the customer and better performance than the competition.  Learn Devops Some Advantages of DevOps Technical AdvantagesFaster and continuous delivery of softwareReduction in management complexitiesQuicker resolution of problemsBusiness BenefitsFaster delivery of solutionsEnhanced communication and collaborationMore time for innovation and creativityCultural BenefitsHappier, more productive teamsImproved work environmentEmployees are more engagedDiverse growth opportunities  What is Docker? Docker is a type of containerization platform that packages your application and all its dependencies together. This ensures that your application works seamlessly in any environment. Docker is a popular Platform As A Service (PAAS) product. It uses virtualization at the operating system-level and delivers software in packages called containers. These make it easier for a developer to create, run, and deploy applications. What are Virtual Machines? A virtual machine is a great imitation of a personal computer. It has its memory, CPU, network interface, and storage to act as an actual physical computer. To implement them you may need special software, hardware, or a mixture.What are Docker Containers?It is a unit of code that packages up a new code of an application that the developer is writing and all of its dependencies. It makes the application run faster between different computer settings.  A docker image is a small, standalone, workable package of computer code. It has everything you need to run an application on another system. The components include the code, system tools, runtime environment, system libraries, and settings. Advantages of DockerHigh savings and ROIIncrease in productivityEasier maintenanceRapid deploymentContinuous deployment and testing environmentIt uses less memoryFaster and simpler configurationsSeamless portabilityDifferences Between Docker and Virtual Machine ParameterDockerVirtual MachineDefinitionGroup of processes that a shared kernel managesHas an operating system that shares the host hardware via a hypervisorImage Size In MBsIn GBsBoost-up speedVery FastVery SlowEfficiencyHigherLowerScalingEasy to scale upDifficult to scale up Space allocationShare and reuse data volumes among various Docker containersCannot share data volumes with VMsPortabilityDocker is easily portableMay face compatibility issues across different platformsAvailabilityOpen Sourcecontains both paid and open-source (free) softwareWhat is Docker in DevOps? It is a platform that is a perfect fit for the DevOps ecosystem. It is an apt solution for software companies that cannot keep up with the pace of changing technology, business, and customer requirements. This makes Docker an obvious choice to scale up and fasten operations in a company.  The reason for the success of Docker in the DevOps environment is its ability in containerizing the applications. This reduces the time to develop and release a solution for a software development company. It is useful in overcoming the challenges of the 'Dev' and 'Ops' environment. It allows an application to run on any application regardless of the host configurations. This enables all the teams to collaborate while working efficiently and effectively. Docker allows you to streamline and control changes throughout the development cycle. You can use it throughout the development, production, and release stages. If you want to go back to a previous version, you can do that using Docker. You can also ensure that a feature is working in the production environment based on whether it is operational in the development environment.5 Reasons for choosing AWS with DevOps  Why Use Docker?Agility: Container technology like Docker helps in wrapping up the application along with its libraries, binaries, and configuration files. It helps create a single package quickly enough that is deployable on different platforms/environments with no compatibility issues.Less Overhead: Containers share the guest operating system and its resources. This makes the using the container fast, light-weight, and far superior to virtual machines.Version Control: Containers help you control different versions of an application easily. It also helps to move across environments without worrying about specific customizations. How it Fits in DevOps? Docker is a tool that benefits both developers and administrators. This makes it a part of many DevOps (developers + operations) toolchains. Developers can write code without worrying about the system that will be ultimately running the application. They can also use one of the thousands of programs that are developers have already developed to run in a Docker container. For operations staff, Docker gives them the flexibility to test in a unique environment. It also reduces the cost of investing in multiple settings, as Docker files are of small size and have low overhead. Many DevOps applications are already using Docker, such as Ansible, Vagrant, Chef, and Puppet. This helps in automating the use of unique environments and the deployment of those environments. Learn Docker/Kubernetes  What is Docker Used For?Developers can write code and share their work with their colleagues using Docker containers.Docker can be useful in pushing applications into a test environment to execute automated and manual tests.Developers can find and fix bugs in a development environment. Then, redeploy them to the test environment for validation and testing.After testing, you can send the fixed application to the customer easily. You can also push updates to the product environment just as easily.  Let Us Take An Example…Suppose there is a company that is developing an application in Java. For this, they need to install the tomcat server on the developer’s system. After development, it goes to the tester who will again require a tomcat environment to test the application.Once the testing is complete, you will need to deploy it on the production server. This too will require tomcat installed on the system to host the Java application. Ultimately, you will need to install tomcat thrice. This is easily avoidable by using Docker.All you need is to create a tomcat docker image using a base OS like Ubuntu. This is existing online on various websites. Now, you can use this image on all systems, including that of a developer, tester, and system admin. This way you will get the tomcat environment automatically, solving the problem in a software development company. What are the Benefits of Using Docker in DevOps?There are a lot of advantages of using both Docker and DevOps. Both can help you increase collaboration among various teams who are part of the software life cycle. Both offer a broad range of business, development, and technical benefits. Although each has its drawbacks, they fit perfectly together. Here we list some key benefits of using Docker with DevOps:If an application runs in a development environment, it will run successfully in another using Docker.Using Docker with DevOps makes it easier to create applications using unique interconnected components.You also get a high level of control over all the changes during the development life cycle. This makes it easier to go back to a previous version of the application you want. ConclusionDocker is the future of designing, developing, and deploying an application in a simple, affordable, and fast way. It ensures that an application runs post-production, just as it did during development and testing.Through this post, we have tried to explain what is Docker in DevOps in the best way. It should clear any doubts that you have about the technology.If are yet to try Docker for yourself, we highly recommend using it to embrace the digital transformation. You will be a part of the diverse set of users that continue to grow every day and benefit from its advantages. 


Best Python Tutorial For beginnersTo all Python lovers, here I’m writing an easy tutorial for learning the python programming language.As you know with the growth in Machine Learning & Artificial Intelligence, the future of the industry will be dependent on AI-based applications. Python is the stepping stone to start your career in automation and data science. Few real-time application of Python:Web ScrapingImage ProcessingMachine Learning & AIAutomate excel & csv filesScientific & Numeric ComputingEmail AutomationData AnalysisData VisualizationWeb Development using DjangoGaming development Benefits of using Python over other programming languages:Open Source (No license required to buy software) & supports Community developmentExtensively Support multiple LibrariesData Analysis is done using PandasNumerical Calculation is done using NumPyObject-Oriented programming languagePython is easier than C, C++, Java, .Net, etc.High-performance speedBetter productivityUser-friendly data-structures All top companies like Google, Facebook, Microsoft, Youtube, Quora, Yahoo, Salesforce are currently using Python. This tutorial will cover:-Python InstallationPython Data TypesNumbersStringsListTupleDictionarySetsOperatorsConditionals StatementsLoopsFunctionsFile Handling  Python Installation Download python latest version from setup installationInstall visual studio code from code.visualstudio.comStart using Jupyter notebook Variables Python stores data/ values in memory using a variable. A variable is a name given by you, to which you assign a piece of data that is stored in an area of the computer’s memory, allowing you to refer to that data when you need to later in the program a = 10 b = 'Welcome to Gyansetu!' c = 10.11 print(a,b,c) ## output: ## 10 Welcome to Gyansetu! 10.11 Here the variables are a, b, c Python Data Types  Numbers:There are 3 types of Numbers:IntegerFloatComplexInteger (int): Integer is a non-decimal number formed by the combination of 0 – 9 digits.Float (float): A float is a decimal number that can be represented on a number line.Complex (complex): They are the numbers consist of an imaginary number and a real number.Different types of numbers a = 10   #integer b = 43.49     #float c = 1 + 2j       #complex To accept values from user input x = input("Enter any number : ") y = input("Enter any number : ") z = input("Enter any number : ") print(x,y,z) ## output: ## Enter any number : 2 ## Enter any number : 3 ## Enter any number : 1 ## 2 3 1 To know the type of data: print(type(x)) print(type(y)) print(type(z)) ## output: ## ## ##  String A string in Python consists of a series or sequence of characters – letters, numbers, and special characters.Strings can be indexed – often synonymously called subscripted as well. Similar to C, the first character of a string has the index 0.How to print a string  a = "sahil" b = 20 c = 30 print("%d-%d-%d" % (a,b,c)) ## output: ## This will give error because %d is used in print for a and a is a string, %d is used for integer values  ### Another way to solve the error  a = "sahil" b = 20 c = 30 print("%s-%d-%d" % (a,b,c)) ## output: ## sahil-20-30 ## %s is used for string values  How to print a string using .formata = "sahil" b = 20 c = 30 print("{}-{}-{}".format(a,b,c)) ## output: ## sahil-20-30   Indexing in Python: Indexing # starts from 0 to n – 1 where n is the length of string use []Example to understand indexing: s = "Hello World!" print(s[2]) print(s[6]) ## output: ## l ## s  Slicing in Python: Slicing # used to make sub-strings from strings used with start, end and step variable  # s[start:end:step] s = "Hello World!" x = s[3:11] y =  s[:] z = s[:6] p = s[6:] print(x) print(y) print(z) print(p) print(s) ## output: ## lo World ## Hello World! ## Hello ## World! ## Hello World! Basic String Functions: –swapcase, upper, lower, strip, lstrip, rstrip, find, replace, format, center Swapcase:- used to change uppercase letters into lowercase letters and vice versaUpper:- It changes all letters into uppercase and returns the stringLower:- It changes all letters into lowercase and return the stringStrip:- strip is used to remove all leading and trailing spaces from the stringLstrip:- It is used to remove left spaceRstrip:- It is used to remove the right space Example to understand string functions: s = input("Enter a String : ") print(s) print("Swapcase : ",s.swapcase()) print("Upper : ",s.upper()) print("Lower : ",s.lower()) print("Strip : ",s.strip()) print("LStrip : ",s.lstrip()) print("RStrip : ",s.rstrip()) ## output: ## Enter a String : iclass gyansetu ## iclass gyansetu ## Swapcase :  ICLASS GYANSETU ## Upper :  ICLASS GYANSETU ## Lower :  iclass gyansetu ## Strip :  iclass gyansetu ## LStrip :  iclass gyansetu ## RStrip :  iclass gyansetu ListsLists are one of the most powerful tools in Python.They are just like the arrays declared in other languages.But the most powerful thing is that list need not be always homogeneous.A single list can contain strings, integers, as well as objects.Lists can also be used for implementing stacks and queues.Lists are mutable, i.e., they can be altered once declared. Declaring a list: L = [1, "a" , "string" , 1+2] print(L) ## output : [1, 'a', 'string', 3] Homogeneous & Non-Homogeneous List Homogeneous List contains all elements of same data type.Non- Homogeneous List contains elements of different data type. l = [ 'hello','hi','how are you' ] print(l) print(l[2]) l = [ 56,23,45,12,67,12,43,1,6,8,6,33,12] print("Homogeneous List :",l) print(l[5]) l = [ 'hello','hi', 3, 4.5 ] print("Non-Homogeneous List : ",l) print(l[-3]) ## output: ## ['hello', 'hi', 'how are you'] ## how are you ## Homogeneous List : [56, 23, 45, 12, 67, 12, 43, 1, 6, 8, 6, 33, 12] ## 12 ## Non-Homogeneous List :  ['hello', 'hi', 3, 4.5] ## hi  TupleA Tuple is a collection of Python objects separated by commas.In someway a tuple is similar to a list in terms of indexing, nested objects, and repetition but a tuple is immutable, unlike lists which are mutable.Declaring a Tuple tup = 'python', 'gyansetu' print(tup)   ## output: ('python', 'gyansetu')  ### Another way for doing the same tup = ('python', 'gyansetu') print(tup) ## output: ('python', 'gyansetu')  DictionaryIt consists of key-value pairs.The value can be accessed by a unique key in the dictionary.Dictionary Example with Key-Value pair mydict = {     'name':'python',     'build_year':1991,     'Father of Python':"Guido Van Rossum",     'Frame_works':['Django','Flask','Web2PY','Torando','kivi'],     'versions' : [1.0,2.0,3.0],     'latest_version':3.6} print(mydict) print("Name : ", mydict['name']) #returns the value of the key 'name' print("Frame Works : ", mydict['Frame_works']) ## output: ## {'name': 'python', 'build_year': 1991, 'Father of Python': 'Guido Van Rossum', 'Frame_works': ['Django', 'Flask', 'Web2PY', 'Torando', 'kivi'], 'versions': [1.0, 2.0, 3.0], 'latest_version': 3.6} ## Name :  python ## Frame Works :  ['Django', 'Flask', 'Web2PY', 'Torando', 'kivi']  Operators in Python Arithmetic Operators Arithmetic operators are used to perform mathematical operations like addition, subtraction, multiplication and divisionAddition Operator ( + )Subtraction Operator ( – )Multiplication Operator ( * )Division Operator ( / )Modulas Operator ( % )Floor Division ( // )Exponent Operator ( ** ) Example of Arithmetic Operatorx = 10 y = 3 z1 = x+y z2 = x-y z3 = x * y print("Addition of {} and {} is {}.".format(x,y,z1)) print("Subtraction of {} and {} is {}.".format(x,y,z2)) print("Multiplication of {} and {} is {}.".format(x,y,z3)) ## output: ## Addition of 10 and 3 is 13. ## Subtraction of 10 and 3 is 7. ## Multiplication of 10 and 3 is 30. Comparison OperatorsThese operators compare the values on either side of them and decide the relation among them.Less Than ( < )Less Than Equals To ( )Greater Than Equals To ( >= )Equals To Equals To ( == )Not Equals To ( != ) Example of Comparison Operator#returns true if statement is True else returns False print("6 < 8 ", 6 < 8)               ## output: True print("6 6 ", 5 > 6)              ## output: False print("6 != 7 ", 6 != 7)           ## output: True   Logical OperatorsLogical operators are used on conditional statements (either True or False). They perform Logical AND, Logical OR, and Logical NOT operationsAnd            If x is false, return x            else return y OrIf x is false, return yelse xExamples:print( 5>7 and 6-5*3+6)                ## output: False print( 6-4*3+6 and 5>7)                ## output: 0 print( 6-4*3+6 or 6 > 5)                  ## output: True print( 6 > 5 or 6-4*3+6)                  ## output: True  Membership OperatorMembership operators are operators used to validate the membership of a value. It test for membership in a sequence, such as strings, lists, or tuples.innot inExamples: s2 = "Dog is an animal." s1 = "Dog" x = s1 in s2 if x :     print("Pattern Found in step 1") #return this if x=True else :     print("Patten Not Found") p = s1 not in s2 print(x) print(p) ## OUTPUT: ## Pattern Found in step 1 ## True ## False  Identity OperatorPython Membership and Identity Operators · in operator: The ‘in’ operator is used to check if a value exists in a sequence or not.isis notExamples:x = 5 y = 5 print(x is y) print( x is not y ) p = 3 q = 4 if p is q :     print("Both are equal") else :     print("Both are different") Conditional StatementsIn programming and scripting languages, conditional statements or conditional constructs are used to perform different computations or actions depending on whether a condition evaluates to true or false. (Please note that true and false are always written as True and False in Python.) Example to calculate the greatest of 3 numbers using a conditional statement #Greatest among Three numbers a = int(input("A : ")) b = int(input("B : ")) c = int(input("C : ")) if a >= b :     if a >= c :         print("A is greatest ")     else :         print("C is greatest ") elif b >= c :     print("B is Greatest") else :     print("C is Greatest ") ## OUTPUT: ## A : 12 ## B : 32 ## C : 16 ## B is Greatest Loops in PythonLoops can be divided into 2 kinds.Finite: This kind of loop works until a certain condition is metInfinite: This kind of loop works infinitely and does not stop ever. There are 2 kinds of loops:forwhileFor Loops: These loops are used to perform a certain set of statements for a given condition and continue until the condition has failed. You know the number of times that you need to execute the for loop. Example of For loop print("List Iteration") l = ["books", "bags", "pens"] for i in l:     print(i) ## OUTPUT: ## List Iteration ## books ## bags ## pens  For loop using range()  for i in range(5): print(i) ## OUTPUT: ## 0 ## 1 ## 2 ## 3 ## 4 If you want to explore and learn coding skills in Python, then Gyansetu provides you the best platform to learn the Python language. Call:-  8130799520.

Why Salesforce is biggest hit in Cloud

In 2018, Salesforce is rated the Top IT Technology in US, UK, Latam, India & Australia. Salesforce is seen on the Top of the game & has touched almost $10B revenue from the time of its inception in 1999. Salesforce is propelling the growth of nearly 2 million new jobs by 2020. That’s wonderful! Due to a tremendous increase in demand of Cloud based Solutions, Salesforce CRM has got huge market acceptance. The software is designed to make businesses efficient and profitable by reducing the cost of managing physical infrastructure.“Salesforce is a platform for all of our employees to communicate, react in real time, and solve customer problems.”GREG KEELEY, EVP, American Express is Awarded as- Innovator of the Decade in Forbes, 2016 Most Innovative Companies in the World in Forbes, 2016 World’s most Admired Company in Fortune 2013-2017  The Salesforce was first developed by the American cloud computing company, headquartered in San Francisco, California. The company introduces the term Salesforce to the technology world.  Market Share Salesforce Products   Why Salesforce is the Best Career move If you are still not Convinced by the fact that Salesforce is the hottest skill for Job change, here are few more reasons for you to see a bigger picture 1.Salesforce in Marketing TeamThe Salesforce Professional Edition enables a company’s marketing team to create and track different marketing campaigns to measure success rates and automatically provide advice to the company’s sales force.2.Salesforce in Customer Support TeamSalesforce also tracks customer issues and follows them according to various escalation rules, such as customer importance and elapsed time. This improves customer satisfaction because problems are not solved by failures and move directly to the next level.3.Salesforce in ManagementWith visual dashboards and rich reporting capabilities, Salesforce lets the company see what’s happening on different computers.4.Salesforce in TrainingSalesforce has very good training and support capabilities that exceed industry standards. Salesforce users can easily find the answers to their questions in the online help and installation of the video.5.Salesforce in Application IntegrationSalesforce can be integrated with other systems to extend its functionality through Salesforce Business App Store such as the AppExchange.Advantages of Salesforce Multi-tenant Architecture High Level Security Cloud Based CRM tool Support SaaS & PaaS Architecture No Software Installation No Database Connection Required Work on all OS  Salesforce Training &Certification Salesforce Certified Administrator  Salesforce Certified Advanced Administrator  Salesforce Certified Platform Developer I  Salesforce Certified Platform Developer II Salesforce Certified Sales Cloud Consultant Salesforce Certified Service Cloud Consultant  Salesforce Certified Technical Architect Gyansetu offers a comprehensive Salesforce Certification online as well as a physical classroom training course in Gurgaon, India. This course will provide you to understand the basic concepts of the Salesforce Administration & Development which you can further use in your business. What Will You Learn in This Training?Following are some key points that you will be learning from this course including; Salesforce CRM Fundamentals and Features Data model, features, and security Portal designing, report preparation, and dashboards Application customization, data validation and debugging Salesforce Cloud Business Process Automation Salesforce Licenses & Instances Apex, VisualForce Integration Options like SOAP, REST APIs Deployment & Data Loaders   Who Will Teach You the Salesforce Certification Training Course in Gyansetu? Vartul Mangla, who is a Business Consultant in American Express, will be providing you the Salesforce Certification Training Course. He is an expert in business management; with all the knowledge of basic concepts of Salesforce. What are the Course Objectives? Building an application Creating Objects, Fields, and Tabs Better understand about Pick list, Formulas, Validation rules & Page Layouts Basic knowledge about Relationship Models You can form Security of application Business process automation Learn about Sales Cloud and Service Cloud   Salesforce Job Profiles   Who Should Go For Salesforce Certification Training Course? Graduates who want to start a career in Salesforce CRM Training Software Administrators Developers & Architects Business Analysts Sales Managers/ Executives Testing Professionals Mainframe Professionals BI/ETL/DW Professionals  What are the Career Objectives of Salesforce Certification Training Course?As technology is increasing day-by-day in business, Salesforce has become the fastest growing aspects of technology. So, with the help of the course students as well as working people can take the advantages of this course. So, for fulfilling all the important part of your business, you should join this course and become an expert and take your company to the next level.  

Regression vs Classification in Machine Learning

What is Machine Learning?Machine Learning is a subset of Artificial Intelligence which lets machine to discover pattern from the data, draw insights and helps in decision making.The mathematical model is built and it is trained on the sample data. Different types of learning are Supervised learning, Unsupervised Learning, semi-supervised learning, Reinforcement Learning, Self-Learning etc.In the Supervised Learning the prediction model is build from the set of training data which is labelled (i.e. data contains both the input and output). Regression algorithm and Classification algorithm are the types of supervised learning.What is Regression in Machine LearningFrancis Galton coined the term “Regression” in context of biological phenomenon. The work was later extended to general statistical context by Karl Pearson and Udny Yule. Regression analysis is the statistical method which derives the relationship and determines the strength among dependent variables and one or more independent variables. Regression analysis also indicate the impact of independent variables on dependent variable.The meaning of Regression is any procedure which tries to find the relationship between variables.When Regression is used??The Regression is used when we want to predict the output variable which is continuous or a real value.For example:Predicting the Price of HousePredicting the Height of personPredicting the salary of personPredicting TemperatureThere are different Regression modelling techniques. Among all Linear Regression is the most popular algorithm. It is the simplest form of Regression.The Linear Regression is represented by y=ax+b+e where a is the slope of the line and b is the intercept of the line and e is the error.In the diagram below blue dots are the observed data points and red line is the line of best fit. There are many different types Regression algorithm like Linear Regression, Polynomial Regression, Lasso Regression, Ordinal Regression, Quantile Regression, ElasticNet Regression, Stepwise Regression, Poisson Regression, Cox Regression etc.In multiple regression there is more than 1 independent variables.What is Classification in Machine Learning ?The Classification Algorithm is used when we want to predict the output variable which is discrete. The dependent variable is predicted by analyzing the dependent variables.The main goal of classification is to identify the category of the dependent variables based on training data.For Example:Classification of fruits (by analysing the properties – colour, size, texture etc.)Classification of Animals (input images)Face RecognitionEmail spam identificationSentiment AnalysisThe Different Classification Algorithms are:Logistic Regression (Linear Classifier)Naïve BayesNearest NeighbourSupport Vector MachineDecision TreeBoosted TreesRandom ForestNeural Networks etc.5 MACHINE LEARNING ALGORITHMS EVERY DATA SCIENTIST MUST KNOW BY HEARTThe Logistic Regression finds the probability of certain class or event. Using Logit Function, it simply predicts the probability of the occurrence of an event. Suppose if we want to find whether the person is diabetic or not based on his age, Blood pressure (bp) and sex.More formally it can be written asP(Disease=Age|(Blood Pressure)BP|sex)In this example Diabetes is dependent variable and age, BP, sex is independent variable.A Problem where the outcome is of two classes is known as binary classification problem.A Problem where the outcome is more than two classes is known as Multi-class classification.A problem where a data point is assigned multiple labels is known as Multi-Label Classification ProblemThe Types of Logistic regression is Binary Logistic Regression: The Outcome is of two classes. E.g.: spam or not spamMultinomial Logistic Regression: More than two outcome classes without any order E.g.: shape – rectangle,round,triangleOrdinal Logistic Regression: More than two outcome classes with ordering. E.g.: Grades – Distinction, First class, Second classTo selection right algorithm for modelling it is very important to understand whether the problem is a classification problem or Regression problem.Performance Evaluation of Classification and Regression:It is very important to evaluate the performance of the model. Both classification and Regression has various methods, formulas and techniques to evaluate the performance of an Algorithm.Performance metrics for Regression:The different metrics for Regression problems are:Mean Absolute Error (MAE): average squared difference between the estimated values and the actual value.Root Mean Squared Error (RMSE): Difference between the predicted values and the observed values. It measures the spread of the residuals.R – squared: known as coefficient of determination which tells the percentage of points falls on the regression line.Adjusted R square : It indicates how well the data points fir the curve. It considers the significant data points only.Performance Metrics For Classification :To calculate the performance different metrics are used but apart of metrics specific data is required to calculate the performance of the model that is True positive, True Negative, False positive, False Negative. To get visual matrix python provides confusion matrix which is a skikit-learn library.Based on this information we calculate:Accuracy: Accuracy is number of predictions our model got correct.Accuracy = Correct Predictions  / Total Number of PredictionsPrecision : Ratio of Correct positive observations to total predicted positive observation.Precision = TP/TP+FPRecall : It is the ratio of positive predicted observation to the actual observations. Recall is also known as sensitivity.Recall = TP/TP+FNSpecificity : It measures the True Negative Rate.F1 score : It is a harmonic mean of Precision and Recall.ROC/AUC curve : It shows the performance of the model at thresholds by plotting a graph of True positive rate against False positive rate. AUC is the Area under ROC curve.Log loss : It measures the performance where the prediction input is a probability value between 0 and 1.Classification Vs Regression PARAMETER  CLASSIFICATION  REGRESSION  Prediction  The output variable is discrete in nature  The output variable is continuous in nature  Find  Decision boundary  Best Fit line  Output Data   Unordered  Ordered  Evaluation  calculate accuracy  Calculate the sum of squared errors, R- squared  Example Algorithms   logistic regression, Decision Tree, Random Forest etc  Linear Regression, Polynomial Regression etc. To choose the best model for your specific use case it is really important to understand the difference between the Classification and Regression problem as there are various parameters on the basis of which we train and tune our model.TOP 4 DATA SCIENCE PROJECTS THAT WILL GET YOU HIRED IN 2020

What Makes Software Testing so Important ?

“Quality is the ally of schedule and cost, not their adversary. If we have to sacrifice quality to meet schedule, it’s because we are doing the job wrong from the very beginning.” – James A. WardLet’s begin on a simple note, software testing is not important – it’s necessary. The purpose of software development is to provide solutions and make life easy for the people around us with technology. A job as important as this cannot compromise with quality.Software testing is the first and the most important step in quality assurance.Let’s imagine a scenario – if you had to prepare a meal for some guests, would you test it before serving or would you make them eat directly? If you are someone who would want these guests to come over again, you would definitely test the food and assure that it is of the best quality and consistency.This is the entire purpose of testing – to ensure topmost quality, adherence to given standards keeping the client happy.OKAY, So is Software Testing Really this Simple ?The answer to this question is both yes and no.Software testing is an activity which checks that the actual results are same as expected results and that there’re no defects in the final application. It also checks for any errors, gaps and deviation from the actual specification and requirements.While the process seem simple and easy to do, it is much more than just matching point A to B.In order to thoroughly check an Application Under Test (AUT), A QA engineer has to follow these basic steps –Basic Functionality testing:-  where one needs to ensure that every button on the interface and API is working as expected.Code review:-  where bugs are identified at the initial level to avoid discrepancies later.Static code analysis:-  to check completely for any security issues and concurrency issues or loopholes.Unit testing:- to ensure that every unit is working as expected.Single-user performance:-  testing to see if the software is responsive.Thus, it can be said that the premise and purpose of software testing may be simple but the process and its importance is essential and complex.SOUNDS GREAT, BUT WHY SHOULD I ALLOCATE BUDGET AND TIME TO THIS PROCESS?Well, you must consider this an important business decision and in fact, an investment because if you don’t, you will end up spending more. Let us show you few statistics to prove our point –As per a research by Cambridge University’s business school, companies around the world spend more than $300 billion for the process debugging their software. If it did not yield those results, the amount of money spent would be considerably less.For example, the stock price for companies with software failure fell about 4 percent on an average and 7 percent after more failureAs per IBM, it costs around 4 to 5 times more to fix a software bug after the release rather than during the development process.Check out Our Selenium Testing CourseThe process of software development is much more than just making an application work, it is also about providing a smooth experience to the users. Software testing is the only through which one can ensure quality and hence, it needs a big place in your budget and schedule.What are the Types of Software TestingIn order to find errors, bugs and discrepancies in the functioning of a software, one must don the hat of an end-user. You could say that testing is just executing a software to find out if it is working like it is expected too.  There’re different types of testing which have a different purpose.Let’s go over them one by one –UNIT TESTING:  This type verifies the most basic and smallest part of an application. In this type of testing, either a single unit or a group on inter-related units are tested. It is conducted by testing a sample input and then examining the output. For example, checking if loop is working fine or if there’s incorrect initialization.INTEGRATION TESTING: It’s one step –up from unit testing. In this type, various unit-test components are used to build a program which produces a particular output. There are two types corresponding to this, namely, a) Black Box testing where the focus is on what is the output?, and b) white Box testing which focuses on how the output is achieved.REGRESSION TESTING: A software cannot remain static. With change in business model or requirements, new modules have to be added. This type of testing checks whether the application works fine even after a new module is added. For example, if different types of events are added in an event management app, will it still work fine?SMOKE TESTING: This is a basic test to ensure that one part of the software is working fine before proceeding to the next one. This means that if an application has 22 modules, one must make sure that module 1 is okay before adding module 2.ALPHA TETSING: This is a type of acceptance testing, i.e., testing the application within the organization before releasing it.BETA TETSING: This implies testing it for a limited number of users for feedback before releasing it to the entire market.SYSTEM TESTING: Here, the application is tested in different operating system to check its viability.STRESS TESTING: The application is exposed to unfavorable condition to check if it persists. PERFORMANCE TESTING: The final check to see the speed and effectiveness of application.The process of software testing is a web of logical steps woven with technical nuances. A QA engineer or a software professional must behave like an end-user but think like a software developer.ALRIGHT, ONE LAST QUERY, HOW WOULD SOFTWARE TESTING HELP ME IN THE LONG RUN?As previously discussed in our article, software testing is the most important step towards fulfilling the ultimate goal of software development process – providing quality solutions to clients and users.Let us give you an overview on the importance of software testingCOST-CUTTING: Yes, you read it right. Software testing or Quality Assurance (QA) is the least favorite topic during budget allocation, especially in startups. However, it is the most beneficial investment you would do in the long run. As mentioned before, bugs found out in the earlier stages take lesser money to be fixed than those found later. Technically educated testers and QA engineers may charge more money but provide a better solution leading to no rework.SECURITY REASONS: The biggest issue of World Wide Web presently is the theft of privacy and data. Users share a lot of their confidential data on banking apps and on payment gateways. A smallest technical glitch can lead to leakage of data and loss of reputation to a software company. A QA verified and tested software application provides a trustworthy product to the client and to the end users as well.PRODUCT QUALITY: The entire process of software development is carried out to bring to life a vision of a particular software application. Software testing ensures that the product being developed is as per the vision, idea and instructions of the client. The development plan looks simple in theory but its execution throws in new challenges every day. Software testing helps in finding out unexpected glitches and finding their solution. Details like device and operating system compatibility is also checked to ensure complete functionality and great quality.CUSTOMER SATISFACTION: We function in a saturated market where every idea is being repackaged and represented every week. What makes a product stand out is its quality. Software testing and quality assurance helps you in providing a smooth, glitch-free experience to your customers. Your trustworthiness and reliability depends upon the quality of the software application you’ve made; the only way to earn a goodwill is to test the waters and also, the product.We can probably write 5 more points about why should you care about your own product but the simplest way to put it is this way – nobody likes to pay for a product which doesn’t live up to its claims and it is your duty to verify whether your product is, at least, as per what you had promised.CONCLUSION:Software testing is an extremely important but often overlooked aspect of the software development process. However, the things are now changing.Especially, as a career, software testers or QA engineers are in high demand.Quality has taken a center stage in the software development process after much-needed reality checks. Thus, QA engineers are always required in organizations.Also, as opposed to the myth, the pay scale for a developer and a tester are same at an initial level. The future scope and growth depends on the caliber and skills shown by the candidate.One of the biggest reason why a career in Software testing is in demand is that it is a relatively easier skill to obtain. All you need is an analytical bed of mind and hunger to learn in order to get started with your QA career.There are many ways through which you can learn about the tools and methodologies of testing. You can check out our website to get more information on testing and QA.In conclusion, we would just say that no end result can be achieved without few obstacles. Software testing makes sure that these obstacles are removed and your final output is smooth and swift.

Big Data Hadoop Tutorial for Beginners

Introduction to Big DataBig data refer to all the data generated through various platforms across the world. A data is classified as big if the total size is more than 1 GB/TB/PB/EX.Categories of BigData:1) Structured2) Unstructured3) Semi-structuredExample of BigData:1) New York Exchange generates about 1 TB of new trade data per day. 2) Social Media :  Statistics shows that 500+ terabytes of data get ingested into the database of social media site Facebook, every day.Data mainly generated in terms ofa) Photos & video uploadsb) Message exchangesc) Putting comments etc.3) Jet Engine /Travel Portals:Single Jet Engine generates 10+ terabytes(TB) of data in 30 minutes of a flights per day.  Generation of data reaches up to many Petabytes (PB).What is Hadoop?Hadoop is an open source framework managed by The Apache Software Foundation. Open source implies that it is freely available and its source code can be changed as per the requirement. Apache Hadoop is designed to store & process big data efficiently. Hadoop is used for data storing, processing, analyzing, accessing, governance, operations & security.Large organization with a huge amount of data uses Hadoop software, processed with the help of a large cluster of commodity hardware. Cluster term refers to a group of systems which are connected via LAN and multiple nodes on this cluster helps in performing the jobs. Hadoop has gained popularity worldwide in managing big data and at present, it has covered nearly 90% market of big data.Suggested Read:- DIFFERENCE BETWEEN DATA SCIENCE, DATA ANALYTICS AND MACHINE LEARNING Features of Hadoop:Cost Effective: Hadoop system is very cost effective as it does not require any specialized hardware and thus requires low investment. Use of simple hardware known as commodity hardware is sufficient for the system.Supports Large Cluster of Nodes: A Hadoop structure can be made of thousands of nodes making a large cluster. Large cluster helps in expanding the storage system & offers more computing power.Parallel Processing of Data: Hadoop system supports parallel processing of the data across all nodes in the cluster, and thus it reduces the storage & processing time.Distribution of Data(Distributed Processing): Hadoop efficiently distributes the data across all the nodes in a cluster. Moreover, it replicates the data over the entire cluster in order to retrieve the data other nodes, if a particular node is busy or fails to operate.Automatic Fail over Management (Fault Tolerance): An important feature of Hadoop is that it automatically resolves the problem in case a node in the cluster fails. The framework itself replaces the failed system with another system along with configuring the replicated settings & data on the new machine.Support Heterogeneous Cluster: the Heterogeneous cluster is one which accounts for nodes or machines which are from a different vendor, different operating system and running at different versions. For instance, if a Hadoop cluster has three systems, one IBM machine that runs on RHEL Linux, the second is INTEL machine running on Ubuntu Linux and third is AMD machine running on FEDORA Linux. All of these different systems are capable to run simultaneously on a single cluster.Scalability: A Hadoop system has the ability to add or remove node/nodes and hardware components from a cluster, without affecting the operations of the cluster. This refers to scalability, which is one of the important features of the Hadoop system.Must Read:- WHY BIG DATA WITH PYTHON IS TOP TECH JOB SKILL Overview of Hadoop Ecosystem:The Hadoop ecosystem consists of1) HDFS (Hadoop Distributed File System)2) Apache MapReduce3) Apache PIG4) Apache HBase5) Apache Hive6) Apache Sqoop7) Apache Flume, and 8) Apache Zookeeper9) Apache Kafka10) Apache OOZIEThese components of Hadoop ecosystem are explained as follows:HDFS (Hadoop Distributed File System): HDFS has the most important job to perform in the Hadoop framework. It distributes the data and stores them on each node present in a cluster simultaneously. This process reduces the total time to store data onto the disk.MapReduce (Read/Write Large Datasets into/from Hadoop using MR) : Hadoop MapReduce another most important part of the system that processes the huge volume of data stored in a cluster. It allows parallel processing of all the data stored by HDFS. Moreover, it resolves the issue of high cost of processing through the massive scalability in a cluster.Apache PIG (PIG is kind of ETL for Hadoop Ecosystem): It is the high level scripting language to write the data analysis programmes for huge data sets in the Hadoop cluster. Pig enables the developers to generate query execution routines for analysis of large data sets. The scripting language is known is Pig Latin which one key part of Pig & the second key part is aApache HBase (OLTP/NoSQL) sources: It is a column oriented database that supports the working of HDFS on real time basis. It is enabled to process large database tables i.e. a file containing millions of rows & columns. An important use of HBase is the efficiently use of master nodes for managing region servers.Apache Hive (HIVE is SQL Engine on Hadoop): It is a language similar to SQL, which allows the squaring of data from HDFS. The Hive version of SQL language is called as HiveQL. Must Read:- Top 5 Most in demand IT Job Skills You Need for the FutureApache Sqoop(Data Import/Export from RDBMS(SQL sources) into Hadoop): It is an application that helps in import & export of data from Hadoop to other relational database management system. It can transfer the bulk of data. Sqoop is based on connector architecture that backs the plugins for establishing connectivity to new external systems.Apache Flume(Data Import from Unstructured(Social Media sites)/Structured into  Hadoop)  : It is an application it allows the storage of streaming data into Hadoop cluster, such as data being written to log files is a good example of streaming data.Apache Zookeeper ( Co-ordination tool used in Clustered environment (Hadoop)): Its role is to manage the coordination between the above-mentioned applications for their efficient functioning in the Hadoop ecosystem.Functioning of Hadoop – HDFS DaemonsHadoop system works on the principle of master-slave architecture. HDFS daemons consist of following:Name Node: It is the master node, and is single in the It is responsible for storing HDFS Metadata that keeps track of all the files that are stored in the HDFS.  The information stored on Metadata is like the file name, file permission it has, authorized user of the file & the location where it is stored. This information is stored on RAM which is generally called as file system Metadata.Data Nodes: It is the slave node, and is present in multiple numbers. Data nodes are responsible for storing & retrieving the data as instructed by the name node. Data nodes intermittently report to the name node with their present status & all the files stored with them. The data nodes keep multiple copies of each file stored in them.Secondary Name Node: Secondary name node is present to support the primary name node in storing the Metadata. On the failure of name node due to corrupt Meta data or any other reason secondary name nodes prevent the dysfunctioning of the complete cluster. The secondary name node instructs the name node to create & send fsimage & editlog file, upon which the compacted fsimage file is created by the secondary name node. This compacted file is then transferred back to name node and it is renamed. This process repeats after every 1 hour or when the size of editlog file exceeds 64MB.The functioning Hadoop system can be better understood with the help of a live example. Let us take the example of a banking system.Banks are required to analyse the loads of unstructured information with them which is collected through various sources such as social media profiles, calls, complaint logs, emails, discussion forums, and also through traditional sources of collecting information like cash and equity, transactional data, trade and lending, etc. for better understanding & analyzing the customers.The financial firms are now adopting the Hadoop system in order to structurally store the data, access the data and analysing & extracting the key information from the data that will provide comprehensive insights to help to make the right & informed decision.

Top 4 Data Science Projects that will get you hired in 2020

If you have been studying your data science notes and getting good grades in assessments, you are made to feel like that world is waiting to hire you.In reality, things to be a little different. In the real world, getting a job in the field of data science requires one to go beyond textbook knowledge. Your project work has more credentials than your grade.In this article, we will discuss 5 Data Science Projects that will get you hired in 2020.More than your resume, it is your portfolio that matters.Thus, you need to include as many projects as possible under your name. So, without any further ado, let’s divulge into the most beneficial data science projects for beginners.Machine Learning with Python CourseSOME AMAZING DATA SCIENCE PROJECT IDEAS FOR YOUText To Speech Analytics (DATA SCRAPPING) :You will be surprised to know that the first task you may get as a data scientist will that be of data extraction & cleansing. As per reports, a data scientist generally spends 80% of his/her time on cleaning data, especially during new projects. A team is always on the lookout for people who can take this responsibility off their shoulder. Your proven capability at this task may get you instant entry in different projects and teams.As you already know, the most popular languages in the world of data science are Python and R. So, if you decide to make your data cleaning project in Python, we suggest you use Pandas Similarly, if it is R which is your choice, then try the dplyr package.Also, ensure that your project gives priority to the following skills–  Data Import– Working with multiple datasets– Finding out missing values– Finding out anomalies– Maintaining Data Quality and assurance.Statistics Analysis (EXPLORATORY DATA ANALYSIS(EDA)):Data science is all about extracting insights and visualizing solutions to business problems. EDA or exploratory data analysis helps a data scientist to derive meaningful conclusions from the pool of data which help in complex decision-making. With EDA, a data scientist can learn behavioral patterns of certain customer segments or the way sales trends behave.Our suggestion is to go through Pandasand Matplotlib for creating a Python project is exploratory data analysis. If it is R which you prefer, you can ggplot2.Ideally,  Your EDA project should be able to prove the following objective –– Formulation of relevant questions for further investigation.– Ability to identify and judge trends.– Ability to identify co-variation between variables.– Ability to use data visualization for presetting and communicating results.Consumer-Facing Visualizations (DATA VISUALIZATION):       Data visualization is the culmination of every data analysis technique into concrete and actionable insights.        One of the most important tools of data visualization has been dashboards.They are universally useful but are preferred by business users as they give them a visual representation of data. Also, dashboards help in team collaboration as many data scientists can share their input collectively on it. They are also highly interactive; a must-have for business users. Business analysis is meant for strategic decision-making rather than technical details. You will discover that the output of a data science project is delivered to the client in a dashboard.Your project must be able to showcase the following skills:– Identifying metrics specific to the customer’s requirement.– Developing useful and relevant features.– An understandable layout for easy and quick scanning,– Generating an optimum refresh rate.– Preparing reports on automated actions.A Python user can take help from the Bokehand Plotly But, if you prefer R as your data science language than you can go through RStudio’s Shiny package.MACHINE LEARNING Projects (Predictive Analytics):Needless to say, machine learning can surely be an added advantage for your portfolio. When we say machine learning project, we do not expect you to dive straight into deep learning with complex algorithms. You can always begin with simple algorithms which are equally useful and easy to explain. For example, you can begin with basics like linear regression and logistic regression. Also, ensure that you choose projects which have some real-life implication in the business world. You can take basic projects like fraud detection and loan default.An Example of Logistic and Linear RegressionFor those working with Python, we recommend Scikit-learn For those who are more comfortable with R, we recommend the Caret package.Your project should be able to convey the following –– Your reasons for choosing the specific algorithm and model.– Data splitting into test-set including k-fold cross-validation.– Feature engineering and selection.– Hyper parameter tuningAlso, it is important to understand your target audience.You must choose your communication toll based on the audience; a tool for an ML expert and business manager will be different.Your project should be able to display the following skills:– Ability to understand the audience.– Ability to present with visualization.– Ability to make simple and concise slides.– Ability to maintain the flow of the presentation.– Ability to connect the result with a business decision.Also, let us give you an additional tip – document your project in Jupyter Notebooks or RMarkdown and the covert them to websites for your GitHUb profile.FINAL TAKE ON DATA SCIENCE PROJECTSData science projects are an effective and essential way to showcase your worth.Your portfolio should speak for itself and prove your knowledge.Also, a great idea is to choose courses which have extensive project work in their curriculum.They help you apply your knowledge right away in the projects.If you would like to discuss more such courses, feel free to drop us a mail and help you find the right course for your need.Until then, happy coding.

What is Neural Network in AI ?

What is Neural Network?Artificial Intelligence – A device takes in information , do some processing to complete the task successfully.A system than perceive information from the environment , understand and interpret the data to take required action is known as Artificial intelligence machines.A system which maximizes its chance of success by properly analyzing data is the core of Artificial intelligence. To develop Artificial intelligence (AI) products hardcoded instructions or program is of no use as the data around us is huge.So as to develop a generic solution algorithms are designed in such a way that enables machine to learn the required pattern , gain intelligence overtime to take make good decision.How Human Learn?Acquiring new things and updating our skills , knowledge is the process of learning.God blessed humans and every creature on the planet with this skill. With experience we learn and grow.Due to continuous interaction with society and environment  consciously and unconsciously we all are learning.Five traditionally recognized senses of humans are: sight (eyes), smell (nose), touch (skin), hearing, taste (tongue). All the species in the world has multitude of senses.With these senses we continuously gather information, neurons carry information our brain interprets the signals, processes, integrates and coordinates to take decision.How AI mimics biological Neuron??The brain basic working unit is neuron. Human brain is made up of 100 billion neurons. These neurons transmits information to and from the brain and to the various parts of the body.Biological Neurons:Deep Learning is a subset of Artificial Intelligence which consists of an algorithms inspired by the function and structure of the brain.Artificial neurons are inspired by the biological neurons.The neurons are connected to each other to control body functions, emotions and movements.The key components of biological neurons are: Dendrites bring information to soma so dentrites accept stimuli from an external environment.Soma is the spherical part of neuron, the incoming signals are summed up by the soma when sufficient input is received neurons fires up(i.e. when threshold is exceeded) and axon sends information to other neuron depending upon the strength of the signal . If input is not sufficient no potential action is taken.Artificial Neuron:An elementary unit in Artificial Neural Network is Artificial Neuron. The artificial neuron is primarily composed of Inputs, weights, Activation function and Output. Each input has an associated weight (w).The input is summed up and non-linear activation function is applied to it. The output is given at the output line.Working of Artificial Neural Network :Multiply inputs by its weight. For example : x1.w1jCalculate the weighted sum for each input and weight. ?wjThe netput is given to activation function to determine the output. If the weighted sum is greater than threshold value assign 1 and else 0 as output.Artificial Neural Network (ANN):Artificial Neural network are composed of multiple nodes which takes input process them and give output. Each node output is known as activation or node value.ANN consists of input layer, hidden layers and output layers. At hidden layer input is transformed to derive some pattern which can be given at the output layer.Types of Neural Network in Artificial Intelligence: Types of Neural Network:The Different types of Neural network are:Convolutional Neural NetworkFeed Forward Neural NetworkRadial basis Function Neural NetworkMultilayer PerceptronRecurrent Neural NetworkLong short-term memoryLong short-term memoryLet’s understand Convolutional Neural Network (CNN) with Example:Convolutional Neural Network works best for Images and videos which is a class of Deep Neural Networks. It has input layers, output layers and multiple hidden layers which are series of convolutional layers.The building blocks areInput LayerConvolutional LayersPooling LayersFully connected LayersAs we can see in the diagram first input image with its height and weight parameter is feed at the input layer. The image is convoluted and passed to the next layer. Pooling layers is generally use to manage the dimensions of the data. Finally, the output is flattened and given at the output layer.Feed Forward Neural NetworkIt is one of the simplest form of the network where data (input) travels in only direction. In this network there is no back propagation and connections between nodes do not form a cycle or loops. Perception is the simplest feed forward neural network. Recurrent Neural Network (RNN)It is a network where the flow of the data is not restricted to one direction. It has more capability and greater learning speed which is used to solve complex tasks.  In this network output from the next step is feedback to the previous state. RNN has memory which retains information for processingBayesian Network:The draw a probabilistic relationship between set of the variables Bayesian network is used.They are used for wide variety of tasks such as decision making, prediction, anomaly detection etc.It is a cyclic graph that denotes both random variables and conditional dependencies.Modular Neural Network:It is a collection of neural network working independently where each neural network has a set of inputs.The advantage of Modular Neural network is that it divides the large computational task into smaller modules and thus decreases the complexity.Applications of Artificial Neural Network:Hand writing RecognitionFace Detection and Recognition : In the upcoming days face Recognition will be a popular biometric. To extract features from millions of faces is a big task which can be accomplished by Artificial neural network.Image Compression : In this era data is generated every second. Most of the applications and sites are using images either data transfer takes place or images are loaded on the websites so using neural network to reduce the size of image is worth.Speech Recognition: To eliminate the communication barrier between humans and computer sophisticated neural networks can be made to understand spoken language of humans. Attempts to make speech recognition system are made using Multi-layer networks, Kohonen self-organizing maps etc.Learn Deep Learning and Artificial IntelligenceIn the similar way numerous applications can be made using Artificial Neural network.Artificial Neural Network model is very good for problem-solving which are flexible and powerful. Without doing any hard-coded instruction just by training network with examples we can reap good results.Deep Learning is the subset of Artificial Intelligence and it is a blooming field of this decade.

5 AWS features I wish I had known earlier

AWS is the topmost cloud platform in the world right now. Being a subsidiary of Amazon, it has succeeded in deeply penetrating the DevOps and Cloud industry. At present, it has become an important niche in itself.The path to becoming an AWS practitioner goes through several certification courses. However, there are many tips and tricks which only an expert would know.In this blog article, we will discuss some important AWS features which we wish we knew at the beginning of our career. So, let’s begin with the basics.WHAT IS AWS?Before we jump into the features, let us talk a bit about what is it.AWS or Amazon Web Services is an on-demand cloud platform which provides scalable and flexible APIs and cloud services to the people. You can use AWS as an individual or a company.Why AWS Cloud is Best for Your CareerThere are many cloud-platforms available, why should I Learn AWS?AWS is growing Fast: There’s no denying the fact that it dominates the cloud platform You will be better equipped to compete in the job market with this certification.Top Tech Skill for 2020 and Beyond: Since its inception, AWS has managed to dethrone other cloud platforms with its ease and cost-friendliness. The reports suggest that AWS certification is much in demand and will only grow in future.A Shift to Cloud Computing: With changing times, more and more organizations are shifting their processes to cloud computing. As a result, your proficiency in a popular platform like AWS will only help your career grown.“The cloud-managed service landscape is becoming increasingly sophisticated and competitive. In fact, by 2022, up to 60% of organizations will use an external service provider’s cloud-managed service offering, which is double the percentage of organizations from 2018,” – Sid Nag, Research Vice President at GartnerBetter Pay: The average salary of DevOps engineer with an AWS certification is about INR649K and a senior software engineer earns up to m INR every year. AWS certifications help you add a skill set and an extra zero to your payslipAWS certifications can help you build a career you had always dreamt about. But, do you think only a certification can help you? To help you ace the AWS, we have compiled a list of features which nobody would tell you about. So, without wasting time, here you go5 Essential AWS Features Nobody Tells You AboutAWS Mobile Hub and SDKMobile applications are the most in-demand service in the software industry. AWS makes the development of mobile application easy with its features like AWS mobile hub and SDK. With the help of the mobile hub, you can develop, test and monitor the entire mobile application through a single console.It also makes the inclusion of features like push notification simpler and quicker.Similarly, AWS SDK helps you access DynamoDB, Lambda and S3 directly. It supports iOS, Android, React Native, Web and Unity. The presence of these features enhances the development experience and the output immensely. If you work with AWS and do not use its mobile hub and SDK properly, you’re not utilising its full potential.Serverless Cloud FunctionsA developer spends most of his or her time on managing and updating the server for their application. A serverless cloud function relieves you from this responsibility so that you can concentrate on developing the best possible app.Unlike traditional systems, serverless cloud function is a model which lets the provider be responsible for dynamic allocation and provision of servers. There are stateless compute containers which are event-triggered. They are mainly dependent on client-side logic and third-party services. The cloud provider manages this entire process.AWS Lambda is the most popular serverless cloud function today. Most of the famous organizations depend upon this technology and AWS is paving way for it. If you’re an AWS practitioner, you must understand this and implement it into your projects.StorageStoring data is economical and flexible with AWS. There are three main storage options given by AWS. They can be used independently or in a combination with each other. These are – – Amazon Glacier —  for long-term storage.– Amazon Simple Storage Service — scalable object storage for archival, analytics and data backup.– The Amazon EBS — Provides block-level storage volumes for persistent data storage for use with EC-2 instances.SecurityAWS provides maximum security to the data with the flexibility to scale and innovate. AWS’ security groups are associated with EC2 instances. With AWS’s features, you will find security at the port and the protocol level. In fact, some rules that even filter the incoming and outgoing traffic. These rules comprise of the following fields 1) type 2) protocol 3) Port Range 4) Source.AWS MarketplaceAWS is a product of Amazon, thus, they have created a marketplace for soft wares called AWS marketplace. It is an online store where one can browse, select and buy any software for their business. This software come with the flexibility of one-click deployment.As a software developer, you can list your software on this marketplace to get better visibility and can expose your product to a wide range of customers. Similarly, people can take advantage of flexible pricing and get the best software for the best prices.Check Out:- AWS Certification CourseWrapping Up:AWS is increasing in popularity every day because of the ease, safety and flexibility it provides at economical prices. Sooner or later, every software process will be on various cloud platforms. Thus, it is only beneficial to learn more about AWS and its unique features.The right certifications and expert guidance can help you become an ace AWS practitioner. If you want to get more knowledge about AWS and its certifications, feel free to contact us by dropping us an email.

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