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Python is considered one of the easiest programming languages. It is widely used for web development, gaming applications, data analytics and visualization, and AI and machine learning. Since Python is used for multiple purposes, Python developers enjoy a massive market demand.  If you plan to build a career in Python, you will have to prepare well for interviews. This blog has a list of the most commonly asked Python interview questions and answers. Python Basic Questions for Freshers Freshers are usually not asked too technical questions in the interview. The recruiter will ask you general questions about the Python language to know all theoretical concepts. 1. What is Python? Python is an object-oriented programming language compatible with different platforms like Mac, Windows, Linux, Raspberry, etc. Python is an interpreted language which means we can execute Python codes immediately after writing them. 2. What are the benefits of using Python language? Python is a general-purpose programming language with a simple syntax. It enhances the readability of the code. Since Python is an interpreted language, we can execute the instructions directly without compiling the code. Another significant benefit of Python is that it is an open-source platform that anyone can freely download. Moreover, the built-in data structures in Python like a dictionary, list, and tuple enhance code readability. 3. What are the different scopes in Python? There are four different scope types in Python: local, global, module-level, and the outermost scope. The first one has local objects within it that are being used at the moment. The global scope has objects that can be accessed anytime during the code execution. The global objects in the current module come within the module-level scope. Lastly, outermost scope refers to all the built-in objects that can be called during the program. 4. What is the meaning of break, continue and pass in Python? We use the break statement in Python to terminate the loop or exit from it. The statements after the break get the control. However, the continue statement in Python enables the continuation or execution of the next iterations. The pass statement is used when we do not want any command or code to be executed. It adds a statement for the syntax purpose but does not execute it. 5. What are the keywords in Python? The reserved words in Python like functions, identifiers, or variables are termed as keywords. They are essential for maintaining the syntax of the programming language. Python 3.7 version has 33 keywords, including none, true, and class, continue, def, return, lambda, pass, etc. 6. Explain the difference between lists and tuples. Both lists and tuples are a class of data structure in Python. Lists are used to store different data sets simultaneously. Similar to the arrays, the list is declared in a different language. These are dynamic. On the other hand, tuples also comprise data types separated by commas. They are static. ; 7. Explain different literals in Python. Literals in Python refer to the data that is available in the form of variables and constants. There are mainly four different types of literals in Python: string, boolean, numeric, and special literals. String literals involve a sequence of characters in the code, whereas numeric literals comprise integer or float values. Boolean literals represent true and false with only two values: zero and one, respectively. Special literals are used to represent fields that have not been classified. They are represented by ‘none’ value. Python Questions for Experienced Candidates Interviewers ask technical Python interview questions from experienced candidates.  1. Explain Decorators in Python. Decorators in Python allow the extension of an already existing function. A decorator itself is a function that uses another function as an argument and returns a third function. Developers use decorators in the Python language to modify the function of a class without altering the source code. In simple terms, coders use decorators to change the behavior of a function by extending it but do not allow modification of the original function. 2. Explain the use of lambda in Python. The Lambda function in Python is used to declare those functions in Python that do not have a name, also called Anonymous functions. We use the lambda function whenever we require the nameless function for a short interval while executing the code. Moreover, the lambda function reduces the length of the code because they are usually written in a single line. 3. Explain the difference between NumPy and SciPy. NumPy means Numerical Python. It comprises the array data type, basic element functions like sorting, reshaping, and indexing. SpicyPy means scientific Python. It comprises various sub-packages and a collection of functions. 4. How do we copy a file in Python? We use the copy module to copy a file or an object in Python in two ways. The first method is shallow copy that prepares a replica of the values in the original object. Shallow copy is used to keep the values when a new instance gets created. The second method is deep copy that is used to copy information from the source to the target object. Deep copy stores already copied values and offer reference to the object. 5. Explain the process of compilation and linking in Python. We use compiling and Linking in Python to compile new extensions. In compilation, the source code is saved as a .py file and compiled into bytecode, saved as a .pyc file. Linking is the process wherein all functions with their definitions are linked together. 6. How is memory managed in Python? Memory management in Python occurs in the private heap space that comprises all Python objects and data structures. The programmers do not have access to the private heap space. It is only available to interpreters. Tips to Prepare for Python Interview Questions and Answers Along with asking you theoretical questions related to Python in the interview, the recruiter might also ask you to write down a practical code on a piece of paper. They might also give a situation, ask you to analyze the same, and offer a practical solution with the help of Python.  You must know how to write Python code on paper or whiteboard. Even if you are being interviewed for a fresher position, it is best to practice simple Python codes on a sheet of paper. Many companies might not offer a technical setup to the candidates during the interview. Therefore, they can ask you to write the code on paper or whiteboard. If you have worked on some practical Python projects, plug it subtly while answering the questions. You can explain your answer with the help of examples or personal experiences. It helps the interviewer realize that you have problem-solving skills that make you a suitable candidate for the job. Another crucial tip for Python interview questions is that you should know about the various Python data types like strings, sets, and tuples and when to use each one of them. While preparing for a Python interview question, you should also go through the purpose of generators in Python and how to use them. Also, focus on Python algorithms and data structures. How to Prepare For Python Interview Questions and answers? Python interviews can be tough to crack due to the vast number of technicalities the interviewer might ask you. The best approach to cracking Python interview questions is to first focus on the concepts and then on practical learning.  You can learn both theoretical and practical aspects of Python with upGrad’s Professional Certificate in Data Science and Business Analytics from the University of Maryland. This course helps you learn Python from scratch. Even if you are new to programming and coding, upGrad will offer you a two-week preparatory course so that you can pick up on the basics of programming. you will learn about various tools like Python, SQL,, while working on multiple industry projects. Check out our other data science courses at upGrad. We hope this helps!    

by Pavan Vadapalli

08 Aug 2024

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904648

Wondering what is the range of RPA developer salary in India? According to Forrester, if the Robotic Process Automation or RPA market continues to grow at its current rate, it will reach nearly $3 billion in 2023. This growth is leading to the creation of more related jobs. Amongst the most in demand out of all the RPA jobs is that of a RPA developer, also referred to as an RPA or robotics engineer. Before we discuss the responsibilities of an RPA developer and the RPA developer salary, let us first talk about the process itself and how it works. RPA has become one of the most high-in-demand technologies around the world. As a result, an RPA Developer salary in India is significantly increasing. Which is one of the main reasons why more people should consider becoming an RPA developer. Let’s take a look at who exactly is an RPA developer and what is a typical RPA Developer salary in India? What is Robotic Process Automation? RPA is a term that is collectively used for all those software-based tools that completely or partially automate repetitive, manual, rule-based human tasks. They emulate the actions of a real human being by communicating with software tools to perform a host of different tasks that include responding to customer queries, data entry, and processing standard transactions. The chatbot that you now see on almost every website is mostly an RPA tool and rarely a human.  The software robots work to make business processes more efficient by emulating and integrating human actions into digital systems. They can be used for running applications, interacting with other systems, capturing data, and triggering responses. This is just the tip of the ice when it comes to the jobs that RPA robots can be used to perform. With its continuous development, we can only imagine how crucial this technology is going to be in the times to come.  The basic objective of automating business tasks and processes that involve manual labor with robotic process automation tools is to gain a competitive advantage by substantially bringing down the spending that is usually required when humans perform these tasks. This technology is both flexible and versatile and can serve businesses of every size. Established enterprises or start-ups can utilize these tools to enjoy cost and time-saving benefits. You will mostly come across two types of RPA tools in the market – Intelligent bots and programmable bots.  Intelligent bots are capable of analyzing current as well as historical data using artificial intelligence. They help in evaluating the performances of different employees when they are involved in performing a process. Thee robots work on the commands given by users – actions, clicks, and mouse movements amongst others. After analyzing sufficient data, the intelligent bot will use it to move towards the completion of the process. These bots are self-learning with great processes dealing with fluctuating parameters for those that have unstructured data. On the other hand, programmable bots function according to pre-defined instructions and rules. Programmers have first to sit down and define all the parameters that the bot needs to follow. These bots follow a step-by-step mapping out of the process. Often, this is time-consuming, especially when it comes to performing more complex tasks. Must Read: Free nlp online course! Advantages of RPA Work automation One of the most significant advantages of using RPA tools is that they help in automating even the most difficult tasks. This helps in relieving manual workers from performing repetitive tasks like data entry and manipulation. These workers now have a lot more time to focus their energies towards those complex tasks that are directly related to a business’s growth. By automating mundane and repetitive tasks, RPA lets employees spend their time on more value-adding work that requires human judgment, creativity, and strategic thinking. This enables them to unlock their full potential and contribute more meaningfully to business objectives. Additionally, automation helps improve productivity and efficiency as software robots complete tasks faster and more accurately. RPA with rpa salary in India, enables an optimised workforce where human talent is leveraged for complex decision-making rather than repetitive rules-based tasks. No need of new IT infrastructure RPA can use the current IT infrastructure to do what it is supposed to – automate business processes by training the tools. Businesses using legacy systems will benefit from this the most. RPA interacts with the front-end IT infrastructure and performs tasks using the same GUI that a human worker would use. This is why you don’t need to change the IT landscape to start using RPA. No change in the infrastructure also ensures that there is not an addition to the costs.   Since RPA sits on top of existing systems and applications, organisations do not need to invest in new infrastructure or migrate processes to new systems to automate. This presents a huge advantage for companies running legacy or multiple siloed systems, as RPA can help bridge gaps and integrate processes across systems. The non-invasive nature of RPA makes adoption easy, with no need for large-scale IT projects. This reduces disruption to operations while quickly unlocking the benefits of automation. Overall, the ability to automate without infrastructural investments provides a faster and more flexible path to digital transformation, as per rpa developer demand, with an rpa developer average salary in India. Reduced costs and human errors There are certain shortcomings that you associate with the human workers, especially when they are involved in long and repetitive tasks. All of these faults that usually come with boredom, monotony, and tiredness don’t exist when you work with RPA tools. You can expect more consistent, accurate, and timely work. Additionally, you won’t lose money and time while correcting your previous work.  By eliminating human error and fatigue from repetitive tasks, RPA improves accuracy and quality with competitive and handsome rpa salary in India. Software robots perform consistently without deviation, reducing mistakes and the need for rework. This results in considerable cost savings from improved efficiency and higher-quality output with constant rpa developer demand. RPA also enables 24/7 operations with software bots that work tirelessly without breaks, naking this question, is rpa a good career or is rpa in deamand,out of sight. Increased uptime results in more work, faster processing, and improved customer service through around-the-clock operations. Moreover, automated processes are more transparent and auditable, reducing compliance risk with a possibility of of high rpa scope in India. Overall, RPA generates significant cost optimisation and productivity gains by mitigating human limitations in rules-based work, resulting in high rpa scope in India. The reduction in errors and improvement in quality provides a major boost to operational excellence. Now that we know what RPA is and how it works, we will now turn our attention to the roles and responsibilities of RPA developers. Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses What does an RPA developer do? There are certain responsibilities that every RPA developer has to perform. These include designing, developing, and implementing software-based robots in such a way that they work together with human workers to make business processes more efficient. An RPA developer is required to interact with people belonging to different teams and departments and come to terms with the challenges that they face in performing their duties. The objective is to streamline processes by automating those challenging tasks. RPA developers also work directly with business users to create effective automation solutions for them. The interaction between the two is to develop an understanding of the repetitive processes that the business users want the RPA developers to automate. For this, the developer may ask the users to provide service level agreements as well. The role is all about finding answers for the questions that surround the automation of a process or task. They need to understand the process in question, check whether automation is the right solution, and then design and develop the automated state of that process if the answer to the previous question is in the affirmative. Read: Machine Learning Salary in India What are the right skills for an RPA developer? To excel as a robotic process automation developer, you must have technical and soft skills. Here are the essential skills you should master to succeed in this field:  Business Process Management  A solid understanding of business processes is crucial for an RPA developer. You should be able to analyze and identify the processes that can benefit from automation and propose solutions accordingly. It mandates familiarity of business process management approaches such as Six Sigma or Lean.  An RPA developer needs to thoroughly understand the as-is process flow before determining how to transform it through automation and high rpa developer salary in India. This requires working closely with business teams to map processes, identify pain points, and uncover optimisation opportunities instead of wondering is rpa in demand. Strong BPM skills allow an RPA developer to document processes end-to-end, analyse root causes of inefficiencies, and design the to-be automated process for maximum impact with a demand of rpa developer salary for 2 years experience The ability to optimise processes before automation ensures RPA bots mimic best practices when deployed, which can be asked under robotic process automation salary in India, providing rpa developer average salary in India. Overall, expertise in BPM is essential for RPA developers to drive value through automation and ensuring that rpa is a good career, instead of questioning, is rpa a good career. Basic Workflow Skills As an RPA developer, you will design and build workflows for bots. You must have a substantial understanding of decision diamonds, decision trees, decision tables, UML diagrams, state machines and BPMN notations. These skills will enable you to create and test different workflow paths and effectively communicate your design to other team members. Proficiency with workflow design tools is critical for RPA developers to map out the logic and steps the bot needs to follow. Using workflow diagrams, developers can visually represent complex business rules, conditional branching, exceptions, and end-to-end process flows. Strong workflow skills allow developers to break down processes into discrete components that can be automated in the most optimal sequence.  This structured approach enables the creation of flexible bots that can handle various use cases and respond appropriately to different inputs and scenarios. The ability to design robust workflows ensures bots reliably perform exactly as expected when deployed. Overall, workflow mastery empowers RPA developers to build sophisticated automation solutions. Data Analytics Experience Data integration is a critical aspect of RPA development. You should have a solid understanding of SQL queries to efficiently access and manipulate data from relational databases. Also, data analytics skills transform complex data sets into rules for job automation. Familiarity with data mining and data visualization tools enhances your data analytics capabilities. Strong data analytics expertise allows RPA developers to build bots that can draw insights from data to optimise automation. Data analysis helps identify a process’s key inputs and outputs to determine automation feasibility. Additionally, analytics skills enable processing unstructured data like emails or documents into structured data for bot consumption.  RPA developers can leverage analytics to build decision logic based on data patterns and trends making money with robotic process automation developer salary in India. Data visualisation further bolsters analytics by providing intuitive ways to present data insights. Overall, proficiency in data analytics empowers RPA developers to create intelligent automation leveraging data-driven decision-making. This is key to developing bots that evolve through continuous improvement. RPA Platform Skills Proficiency in RPA platforms is essential for an RPA developer. The most popular RPA platforms include Automation Anywhere, Blue Prism, and UiPath. These platforms offer low-code, drag-and-drop functionality for building RPA bots.  Mastery of leading RPA platforms empowers developers to quickly build, test, and deploy software bots and gaining an average salary for rpa developers in India. This requires extensive hands-on experience using the platform’s interface, components, debugging capabilities, control room, and other features. Expert knowledge of RPA platforms allows efficient bot building by leveraging in-built activities rather than coding from scratch.  Developers should continuously expand platform expertise as new versions are released. Certifications in platforms demonstrate comprehensive understanding and bolster credentials. Advanced skills in prominent RPA platforms enable developers to unlock the full capabilities to deliver robust enterprise-grade automation. This expertise is essential to create and maintain scalable bot workflows that provide optimal business value. Debugging, Testing, and Updating Robots As an RPA developer, you need strong problem-solving skills to debug and test bots. You should be able to identify and fix issues within the code, script, or workflow design. Understanding testing methodologies and debugging techniques is crucial for ensuring the smooth operation of automated processes.  Thorough testing and debugging competencies empower developers to deliver high-quality bots that function as expected. Developers must test bots across various scenarios to validate accuracy and reliability. Debugging skills allow systematic identification and resolution of errors in workflows or integration points.  Also, the key is knowing how to update and enhance bots through iterative improvements based on user feedback and changing requirements. Maintaining and upgrading skills on the latest debugging and testing tools is imperative. Sound debugging, testing, and updating abilities enable RPA developers to deliver and sustain seamless, optimised automation over time. This oversight is critical throughout the bot lifecycle. Knowledge of Systems Infrastructure To ensure the successful integration of bots with various systems within an organization, you need a deep understanding of the organization’s systems infrastructure. It includes knowledge of APIs, XML, XSD, REST API, SoapUI, and other technologies. You should be able to interface with critical applications and devices to execute processes seamlessly.  Comprehensive knowledge of an organisation’s infrastructure and systems empowers RPA developers to build effective integrations and interfaces. Developers must understand how to leverage APIs, databases, legacy systems, virtual machines, and more to enable bots to interact smoothly across technologies. Expertise in integration protocols like REST and SOAP is key for connecting disparate applications.  Proficiency in tools like SoapUI used for API testing further bolsters integration capabilities. Staying updated on new systems and emerging technologies allows the creation of future-proof automation. Overall, mastering the technical landscape of an organisation’s infrastructure unlocks an RPA developer’s ability to architect end-to-end automated solutions. This underpins bots’ viability and value. Coding Skills RPA tools are low-code but having coding skills is advantageous for an RPA developer. Understanding the underlying languages of RPA platforms, such as Visual Basic.NET, Python, HTML, JavaScript, and PHP, allows you to develop and maintain complex RPA implementations. Proficiency in coding enables you to customize bots, access libraries, and perform advanced functions efficiently.  While RPA platforms are designed for low-code automation, coding skills significantly expand what RPA developers can accomplish. Developers can leverage scripting languages to optimise data handling, implement complex logic, and enhance integration capabilities. Coding proficiency also aids in debugging and adding advanced functionality.  Developers should continuously expand coding skills as new languages emerge. Certifications in languages commonly used with RPA demonstrate well-rounded technical expertise. Strong coding abilities give RPA developers greater flexibility and control when designing sophisticated automation solutions. This empowers the creation of highly customisable, enterprise-grade bots How to become an RPA developer in India? You can check out this article where it is thoroughly elaborated that how to become an RPA developer. However, for now, to give you a brief idea of the pathway, below are some pointers. You are required to have a bachelor’s degree in computer science or related fields. While pursuing this degree, working on various AI and ML-related projects can be added bonus.  In case you did not have the opportunity to attempt such projects you can opt for post-graduation certification courses that mainly focus on robotics, AI, and ML.  Apart from that, if you wish to continue your higher studies after your bachelor’s, then choosing a concentration in ML and AI will be preferable to become an RPA developer in the future.   However, if you are someone who is not going to follow this conventional path or have a different academic background, you can still start your journey of becoming an RPA developer from the scratch. In that case, some of the checklists you need to need to fulfill will include: Having knowledge of programming languages like Java, C, C++, python, etc, and having proper programming skills. Having knowledge of SQL or similar other database query langue. Having hands-on experience with automation tools like UiPath. Having an in-depth understanding of the major AI and ML concepts.  On that note, UiPath is an RPA tool that is used in performing most of the redundant tasks such as windows desktop automation, Citrix automation, Web automation, etc. That is why having practical knowledge of this tool is utterly important. The best part is that to use this tool, you do not require to have any coding skills.  RPA Developer Salary in India According to Ambitionbox, the average salary of an RPA developer in India is around ₹ 6,20,000 per annum. The maximum and minimum limits that this base average salary can reach are ₹ 2,90,000 per annum and ₹ 10,20,000 per annum. This notable difference in salaries can be due to several factors –the experience of an RPA developer, the location where the developer is placed, and the company that the developer is working with.  The demand for RPA developers and other such professionals is increasing because more and more businesses have now started understanding the importance of automating their processes. The jobs in RPA increased as many as fifteen times in the period between 2016 and 2018, according to figures released by technojobs. Source RPA Developer Salary in India: Based on Skills Different skills can impact your salary. Here are the most demanding skills and their impact on the RPA Developer salary in India. Python: 50% Blue Prism: 44% Robotics: 26% SQL: 24% VBA: 24% Automation Scripting: 18% Robotic Process Automation (RPA): 11% Here is the RPA developer salary in India based on different skills in  2024: Robotic Process Automation Developer with Robotics Skills: ₹639,844 Robotic Process Automation Developer with SQL Skills: ₹625,619 RPA Developer with Automation Scripting Skills: ₹599,770 Robotic Process Automation Developer with Blue Prism Skills:₹731,500 Robotic Process Automation (RPA) Developer with Python Skills: ₹758,000 Robotic Process Automation (RPA) Developer with VBA Skills: ₹625,619 Vacancies Location Wise The growth in demand is expected to be even more in the coming years. Here is a table that shows India topping the list that mentions the name of the countries with the most RPA vacancies Location RPA Vacancies India 6,000 USA 5,800 Canada 1,400 United Kingdom 1,300 Germany 800 Australia 600 France 500 Netherlands 400 Source Factors Affecting RPA Developer Salary in India RPA Developer salary in India can be affected by multiple factors. Let’s see some primary salary affecting factors: Location Experience Job Role RPA Developer Salary in India: Based on Location City Salary Bangalore ₹6.5 Lakhs Pune ₹6.2 Lakhs Mumbai ₹6.3 Lakhs Hyderabad ₹6.5 Lakhs New Delhi ₹6.6 Lakhs Chennai ₹6.4 Lakhs Gurgaon ₹6.9 Lakhs Source RPA Developer Salary in India: Based on Experience Experience Salary 1 Year ₹3.9 Lakhs 2 Year ₹5.0 Lakhs 3 Year ₹5.8 Lakhs 4 Year ₹7.1 Lakhs 5 Year ₹8.2 Lakhs Source RPA Developer Salary in India: Based on Industry  Industry Salary IT Services & Consulting ₹6.3 Lakhs Financial Services ₹6.5 Lakhs Real Estate ₹7.1 Lakhs Engineering & Construction ₹6.0 Lakhs Source RPA Developer Salary in India: Based on Job Roles The average salary of RPA Developer in India based on job roles are: RPA Process Transformation Analyst: Lower limit: Rs. 1,500,000 per annum and Higher limit Rs. 2,000,000 per annum RPA Architect: Lower limit Rs. 1,400,000 per annum and Higher limit Rs. 1,800,000 per annum RPA Project Manager: Lower limit Rs. 1,400,000 per annum and Higher limit Rs. 1,800,000 per annum Source The salary may also depend on the knowledge of tools that the candidate has. For example, UiPath is a popular automation tool due to the higher RPA Ui Path developer salary, and the defined RPA Ui Path developer salary in India is around INR 11 LPA (with experience between 2-7 years). RPA Developer Salary in India: Based on Company Size  The company’s size determines RPA developer pay in India. Large IT consulting companies and MNCs have a greater ability to pay higher salaries to combat attrition. Big companies also tend to have more complex automation projects that justify premium payouts. Developers at major firms like Accenture, Capgemini, and Cognizant can earn approximately 40-50% higher salaries than at smaller product companies and startups. RPA Developer Salary in India: Based on Certifications  Certifications demonstrate expertise in specific RPA tools like Automation Anywhere, UiPath, and Blue Prism, which are in high demand. Certified developers are perceived as more capable, so companies are willing to offer higher compensation to them. Domain certifications like in finance or healthcare also add value. Multiple certifications can help experienced developers earn Rs 1 million annually or more. Companies Which Hire Most RPA Developers in India As per NASSCOM, the companies that employ the most RPA developers in Indi include Accenture, IBM, Infosys, Tata Consultancy Services, HCL Technologies Ltd, Cognizant, and Tech Mahindra amongst others. Another company that has worldwide recognition for its contribution to automation robotics is Wonderbotz. Many who are hoping to establish their careers as RPAs wish to join this company. The average Wonderbotz salary of an RPA developer in India is around INR 8 lakhs per annum (with 3 years of experience). However, the Wonderbotz salary can range anywhere between INR 7.2 to 9.2 lakhs per annum.  Apart from that, the company that offers a similar salary package for RPA developers in India would be Kyndryl, which is a USA-based IT service and consulting company. An RPA developer with an experience between 4 to 6 years can expect to get approx INR 9.6 lakhs per annum and with 6-7 years of experience can earn more than double it.  Can RPA developers expect a raise in salary in the near future? Well, the trends are certainly pointing in that direction. According to the Uipath report, India has everything that it takes to become a leader in RPA owing majorly to how quickly RPA talent is growing in the country as compared to other countries. The report also states that India would create as many as 2 lakh jobs in RPA by 2024. These figures may have taken a hit due to the pandemic, but we are still expecting an improvement. The size of the market is increasing in both the US and Europe. So an RPA developer has avenues aplenty to find a suitable job for themselves. If they don’t find it in India, which is tipped to be the top country when it comes to RPA job creation, they can move out and find a high-paying job in Europe or the US. RPA is regarded as the skill of the future, and that is the reason why there will be no shortage of jobs in this field in the future. What are the right skills for an RPA developer? We now have a clear idea about what an RPA developer is required to do on a daily basis. Let us now take a look at the skills required to become an RPA developer. Amongst the more basic skills are strong analytical and problem-solving skills. On the technical skills front, an RPA developer needs to have basic programming skills and a little programming experience to go with it as well. The developer should know how PHP, HTML, and JavaScript work.  The next set of skills takes us to the working knowledge of application development, Len Six Sigma methodologies, and relational databases. Knowledge of BluePrism, UiPath, and other such tools can give RPA developers an edge over others. Those with workflow design skills have more chances of success. An RPA developer needs to have a perfect mix of both technical and analytical skills.  In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Other ways of jumping on the RPA bandwagon RPA is a technology that uses software-based tools to program process automation. Every RPA project needs people that you often associate with projects that have to do with process-related software.  1. Designer or business analyst or product analyst: This role is known by so many different names. The people employed in this role are required to handle those aspects of automation that are associated with a business and its objectives. A process analyst job is to understand how the existing process is like, look for opportunities both in terms of improvement and automation, and perform a back-casting process design. Skills required for this job include the basic skills that an RPA developer needs to have – those related to analytics and problem-solving. These are needed because to redesign a process, it is very important to understand how it works first. Other skills include process analysis and optimisation, RPA software knowledge and experience, design thinking, and others. 2. Project manager: Just like all the other projects, RPA projects also involve project management tasks like the completion of a project within the stipulated budget and schedule. If there are a number of smaller RPA projects, as a PM you will be given the responsibility to manage multiple projects at the same time. Prior project management experience is an added advantage. Understanding of risk assessment and other project management tasks is also very important.  RPA project managers are critical in ensuring successful RPA implementations that deliver ROI. They oversee and coordinate all aspects of an automation project, from planning to post-deployment support. Key responsibilities include: Assembling project teams. Creating budgets and timelines. Managing risks/issues. Overseeing developer and tester work. Ensuring stakeholder alignment. RPA project management requires understanding both technical and business needs. Strong communication skills are vital to set expectations across groups like IT, operations, and business users. Change management expertise also helps drive automation adoption after deployment. Given the rapid RPA scale, managing multiple deployments in parallel is common. The best RPA project managers balance technical mastery with business acumen to drive optimal outcomes. Importance of RPA Development: Why you should choose this profession The basic objective of automating business tasks and processes that involve manual labour with robotic process automation tools is to gain a competitive advantage by substantially bringing down the spending that is usually required when these tasks are performed by humans. This technology is both flexible and versatile and can serve businesses of every size. Whether you are an established enterprise or a start-up, you can utilise these tools to enjoy cost and time-saving benefits. You will mostly come across two types of RPA tools in the market – Intelligent bots and programmable bots.  Intelligent bots are capable of analysing current as well as historical data using artificial intelligence. They help in evaluating the performances of different employees when they are involved in performing a process. These robots work on the commands given by users – actions, clicks, and mouse movements amongst others. After analysing sufficient data, the intelligent bot will use it to move towards the completion of the process. These bots are self-learning that are great processes that deal with fluctuating parameters that those that have unstructured data.  On the other hand, programmable bots function according to pre-defined instructions and rules. Programmers have to first sit down and define all the parameters that the bot needs to follow. These bots follow a step by step mapping out of the process. Oftentimes, this is time-consuming, especially when it comes to performing more complex tasks. Check out: 15 Mind-Boggling RPA Interview Questions & Answers Join the ML and AI courses online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Conclusion RPA adoption is accelerating across industries, creating a high demand for skilled developers. While salaries today are attractive, they will continue rising as supply struggles to match growth. Developing expertise in leading RPA tools through online training and certifications can be a game-changer for career growth and compensation.  Beyond hard skills, successful RPA developers also cultivate business acumen, communication abilities, and a passion for innovation. With the rapid evolution of intelligent automation, developers must stay abreast of emerging technologies like AI/ML through continuous learning. Given the transformational impact of RPA in revolutionising operations, this is an opportune time to build your capabilities and establish yourself in this fast-growing field. The future looks bright for talented RPA developers in India and beyond. We hope you liked our article on RPA developer salary in India. These numbers above are not set in stone. The real influencer of your salary is the skills you have,  the mastery you have attained over them, and how quickly you grow and make the company grow as well. The demand of people with expertise in RPA technology is increasing by the day in India. If you want to make the most of this opportunity, you should seriously consider upskilling yourself. If you are a working professional with very little time on hand, there are online courses available that can help you learn the tricks of the trade and become an RPA developer without having to leave your current job.  If you’re interested to learn more about machine learning, check out IIIT-B & upGrad’s Executive PG Programme in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms. Refer to your Network! If you know someone, who would benefit from our specially curated programs? Kindly fill in this form to register their interest. We would assist them to upskill with the right program, and get them a highest possible pre-applied fee-waiver up to ₹70,000/- You earn referral incentives worth up to ₹80,000 for each friend that signs up for a paid programme! Read more about our referral incentives here.

by Pavan Vadapalli

29 Jul 2024

Blogs

25662

Cloud Computing is a technology that uses computing resources, including hardware and software, offering services over a network. The technology uses networks, applications, services, servers, and storage to provide convenient and on-demand services with minimal service provider interaction and management effort. In simple terms, cloud computing stores or accesses data or applications over the internet instead of a hard drive. By using the cloud as a technology, services like software, storage, and infrastructure can be provided. This makes cloud computing more like a business model where the service provider can store and maintain the assets. Out of the many benefits that the technology offers, the three big reasons that make cloud computing a big deal are: Low on maintenance and management from the user’s point Infinite in size, so never run out of capacity With a device and internet connection, the services can be accessed at any time from anywhere However, to leverage the best from this business model, both commercially and technologically, it is important to dig deeper and understand the characteristics of cloud computing. Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career. Why Use Cloud Computing? In my experience, I’ve witnessed how cloud computing serves as a pivotal asset for companies. Its advantages, such as flexibility, scalability, and cost-effectiveness, are undeniable. The ability to access data and applications from anywhere has not only facilitated seamless collaboration within my teams but has also significantly reduced IT infrastructure expenses. Moreover, the robust data security measures, automated updates, and disaster recovery capabilities provided by cloud computing have consistently proven to be invaluable. They not only ensure the safety of critical data but also minimize downtime in case of unforeseen disruptions. As a mid-career professional, I cannot emphasize enough how cloud computing has become an integral part of my business strategy. Its role in optimizing operations, reducing costs, and enhancing overall efficiency is undeniable. Embracing cloud technology is not just an option; it’s a necessity in today’s competitive business landscape. Characteristics of Cloud Computing Cloud computing is composed of 5 essential characteristics, viz: On-demand Self Service Broad Network Access Resource Pooling Rapid Elasticity Measured Service These 5 characteristics of cloud computing are what make the technology the most buzzing and in-demand technology of today. Let’s learn about each characteristic in detail. Read: Cloud Computing Ultimate Guide On-demand Self Service Cloud Computing services are available on-demand and do not require much human interaction. The user himself can provision, manage, and monitor the resources as per his requirement. This is done through a web-based self-service management console. The customer can create the service on his own, like creating a new mailbox or adding additional disk space to a virtual machine, etc.  For example, for booking a ticket on a travel portal, a passenger gets the flexibility to book his ticket by himself without any human interaction. Right from choosing the flight to preference class, the process is entirely automated and does not require any salesperson in between. Check out our free courses to get an edge over the competition. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript upGrad’s Exclusive Software and Tech Webinar for you – SAAS Business – What is So Different? document.createElement('video'); https://cdn.upgrad.com/blog/mausmi-ambastha.mp4   Broad Network Access Cloud computing is accessible from a network, generally over the internet. Similarly, private cloud services can be accessed from anywhere within the enterprise. The services are provided over heterogeneous devices such as mobile phones, laptops, tablets, office computers, etc. Check out upGrad’s Advanced Certification in Cloud Computing The user can access the existing data on a cloud platform or upload new data on the cloud from anywhere using a device and internet connection. In the above example, the passenger can book his ticket via the internet from any device like a smartphone, laptop, tablet, etc., which has access to a network. Also Read: Top Cloud Computing Models Explained Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses Resource Pooling and Multi-tenancy Computing resources like networks, servers, storage, applications, and service can be pooled to serve multiple consumers by securely separating the resources on a logical level. This is done using a multi-tenant model, which allows multiple customers to share the same application or physical infrastructure while retaining data security and privacy. Check out upGrad’s Advanced Certification in Cyber Security It is the same as an apartment building where many people share the same building infrastructure but still have the privacy of their apartment. If we take the same example of the travel portal, the flights can carry several passengers in a single trip. These passengers travel to the same destination, board the same flight, and are allotted separate seats as per the demand and requirement. Rapid Elasticity and Scalability Resource capabilities can be elastically provisioned and released to meet immediate requirements. Similarly, they can be removed or scaled-down when not required. In many cases, this can even happen automatically in response to business demands. This makes sure that the application has the capacity it needs at any point in time. Scalability adds a cost-effectiveness aspect to cloud technology. When the demand or workload is high, more servers can be added for that particular period. For example, to meet the demand of the increasing number of passengers, an airline can increase the number of flights for a particular time and stop the flights when the demand goes down. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Measured Service The utilization of resources is tracked, monitored, controlled, and reported for each occupant. This gives transparency to both the service provider and the consumer. The cloud system has a metering capability, which is leveraged to monitor billing, use of resources, and pay only for what has been used. When a passenger is traveling by train, he has to pay only for the distance traveled by him and not for the entire journey that the train takes. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Benefits of Cloud Computing Drawing from my experience, I can affirm that embracing cloud computing brings about a multitude of advantages that mid-career professionals should be aware of:  Cost Efficiency: Cloud computing significantly reduces upfront hardware and software expenses, making it a financially prudent choice.  Scalability: The ease with which resources can be adjusted to match changing needs is remarkable and has been instrumental in my operations.  Accessibility: It empowers remote work and fosters collaboration from anywhere with an internet connection, enhancing overall productivity.  Data Security: Robust security measures are in place to protect sensitive data, ensuring its integrity and confidentiality.  Automated Updates: Cloud computing handles software and security updates automatically, alleviating the burden of manual maintenance.  Disaster Recovery: I’ve experienced firsthand the reliability of cloud-based backup and recovery options during unexpected disruptions.  Environmental Sustainability: Cloud computing contributes to environmental sustainability by reducing energy consumption and waste compared to traditional data centers.  Business Continuity: It ensures uninterrupted operations, safeguarding against downtime and revenue loss.  Competitive Edge: The ability to deploy new services and applications swiftly has given my business a significant competitive edge.  Flexibility: Cloud technology accommodates various workloads and adapts to diverse business requirements seamlessly.  Incorporating these benefits into your business strategy can lead to enhanced efficiency, cost savings, and a competitive edge in today’s dynamic professional landscape.   Drawbacks of Cloud Computing   Based on my extensive experience, I’d like to shed light on the challenges that mid-career professionals should be prepared for when delving into cloud computing:  Security Concerns: The potential for data breaches and security risks is a critical consideration, necessitating robust protective measures.  Downtime: Reliance on internet connectivity means that downtime can disrupt operations, impacting productivity and efficiency.  Limited Control: Cloud solutions often entail less control over infrastructure and data management, which may not align with specific business needs.  Compliance Challenges: Ensuring that cloud services meet specific regulatory requirements can be a complex undertaking, requiring careful navigation.  Cost Overruns: Unexpected costs can accumulate with increased usage, potentially straining budgets if not monitored diligently.  Data Privacy: Concerns about the location and methods of data storage must be addressed to maintain data privacy and compliance.  Dependence on Service Providers: Relying on third-party providers for service availability means that your business operations are intertwined with their performance.  Data Transfer Costs: Transferring large volumes of data can incur additional fees, which should be factored into cost assessments.  Lack of Customization: The limited ability to tailor infrastructure to unique business needs can present challenges when seeking specific solutions.  Potential Vendor Lock-In: Committing to a cloud provider or platform may pose difficulties when contemplating a change in the future, potentially leading to vendor lock-in.  Navigating these challenges requires a well-informed approach and strategic planning to ensure that the benefits of cloud computing outweigh the associated risks.  Summary Nowadays, almost everything is either running on the cloud, stored on the cloud, or accessed from the cloud, making cloud computing the most in-demand technology in all sectors of the economy and business. With measurable costs, controlled risks, and flexible usage, cloud computing has become the platform of choice for large applications, enterprise technology development, and data storage. Our course will teach you the basic and advanced concepts of cloud computing along with the applications of these concepts. You will learn from industry experts through videos, live lectures, and assignments. Moreover, you’ll get access to upGrad’s exclusive career preparation, resume feedback, and many other advantages. Be sure to check it out.

by Pavan Vadapalli

14 Jul 2024

Blogs

82995

Diving into the world of engineering and data science, I’ve discovered the potential of MATLAB as an indispensable tool. It has accelerated my career and ignited my passion for innovative problem-solving. In this article, MATLAB project ideas for beginners, I aim to share my journey and expertise with those embarking on their path in this dynamic field. My intention is to provide a foundational guide for aspiring professionals eager to delve into technical computing and algorithm development. Through a detailed exploration of MATLAB fundamentals, the acquisition of critical skills via project work, and an understanding of the importance of MATLAB projects for professional development, I hope to simplify the path to mastering MATLAB. This article is designed to make MATLAB approachable and prove its value as a cornerstone in your arsenal of professional tools.  We have MATLAB projects for beginners a gentle approach of multiple skill levels. Whether you’re a beginner or an expert, you’d find a brain-teasing project here. What is MATLAB? “Matrix Laboratory” is the full name of MATLAB. It is a high-performance programming language for technical computing that combines programming, calculation, and visualization in a user-friendly environment. MATLAB is a programming platform for scientists and engineers. It uses the MATLAB language, combining matrix and array mathematics with design processes and iterative analysis. By using MATLAB, you can create algorithms, analyze data, build models, and apply them. MATLAB’s apps, built-in functions, and language allow you to use different methods to solve a particular problem. MATLAB finds applications in many areas, including control systems, communications, machine learning, computational biology, and deep learning.  Top MATLAB Project Ideas with source code The following are some of the most exciting MATLAB projects with source code so that you can test your skills. Let’s get started: 1. Build a Car Parking Indicator Parking a car can be tricky. It requires precision and a lot of practice. You can use MATLAB to make things easier for the driver, however, by building a car parking indicator. You can take inspiration from various parking indicator systems.  An automated car parking indicator would alert the driver when the car is too close to an object. This way, the driver can avoid those objects and turn the vehicle accordingly. You can build a car parking indicator for private parking spaces or open spaces. Such a system can have many benefits: The driver would save time and park his/her car more efficiently. Parking spaces would also be used more efficiently. The chances of a vehicle getting damaged would decrease drastically. Your system can guide the driver to a nearby suitable parking space. You can take it a step further and add the functionality of suggesting a parking space only if it’s available. Maybe your system can determine if a car park has open slots or not, and it can indicate a parking space to the driver of the vehicle accordingly. The sensors can co-ordinate and help in guiding the driver to an open and nearby parking slot. Source Code: Car Parking Indicator 2. Use Artificial Neural Network for Image Encryption Privacy issues have become highly prevalent in recent years. This is one of the best MATLAB project ideas for mechanical engineering for you on this list if you take an interest in cybersecurity and cryptography. You can perform image encryption by taking the help of Artificial Neural Networks (ANNs in short).  Image encryption can prevent unauthorized parties from viewing and accessing images. This way, your data can remain safe. In simple terms, image encryption hides its information. In image encryption, you convert the original plaintext into ciphertext (which can seem like a bunch of nonsense). You can save and transmit this ciphertext over your network, and at the receiver’s end, the ciphertext would convert into the original plaintext.  Neural Networks are machines that behave similarly to how a human brain functions. You can encrypt images on the sender’s end through one ANN and use another ANN to decrypt the image on the receiver’s end. You can use MATLAB to build a complete image encryption system that uses Artificial Neural Networks. After completing this project, you’d be familiar with cryptography as well.  Source Code: Image Encryption 3. Design and Apply an Electronic Differential System An Electronic Differential System allows vehicles to balance them better while turning or running on curved paths. Automotive manufacturers use this system in place of the mechanical differential. This system provides every driving wheel with the required torque and enables multiple wheel speeds.  In a curved path, the vehicle’s inner and outer wheels would have different rotation speeds as the inner wheels would require a smaller radius. An Electronic Differential System uses the motor speed signals and steering wheel command signal to determine the required power for every wheel, so they get the necessary torque. Must Read: Free nlp online course! It’s an advanced technology that offers many advantages, which its mechanical counterpart fails in providing. For example, the electronic differential is lighter than mechanical differential in terms of weight. The wheel with the least traction wouldn’t limit the torque as it would with a mechanic differential. These systems respond faster and offer many functionalities unavailable in the other one, such as traction control. You can use ml projects for final year to design and implement an electronic differential system. You’ll need to create an embedded system design as well for better application. Source Code: Electronic Differential System Also try: 13 Exciting IoT Project Ideas & Topics For Beginners 4. Build a MATLAB Based Inspection System with Image Processing In this project, you’ll build a MATLAB-based inspection system. Machine vision is becoming an accessible technology in the manufacturing industry because of its versatility. And one of the most significant areas where machine vision can find use is in the inspection stage of product development. Quality inspection is necessary to make sure the product doesn’t have any defects.  You can use MATLAB to create an automated inspection system, and you’ll have to employ image processing. With machine vision image processing, you can perform multiple tasks at once: Counting the number of dark and light pixels Discovering blobs of joined pixels in an image Segmenting a part of an image or change the representation Recognizing patterns in an image by matching templates Reading barcode and 2D code. You can perform many other tasks with machine vision. Your automated inspection system would have to determine whether to accept the final product or reject it. It will make the manufacturing process far more efficient and effective.  Source Code: Inspection System with Image Processing Read : 5 Ways Intelligent Automation Helps Your Business Grow 5. Perform Image Encryption and Verification with Chaotic Maps The project is a little different from the one we’ve discussed previously. In this project, you’ll use chaotic maps to encrypt images on the block and steam levels. There is n number of chaotic maps present that generate keys for encryption, so there would be n number of equations involved. Every equation can have n number of constants.  All of these constants would have specific values (random numbers). You can use a neural network to produce a particular series of numbers for image encryption. For image authentication, you’d have to create a simple algorithm to ensure that the sender and receivers are the right people.  Chaotic maps would make the encryption secure through substituting the image with the cover image and encrypting the former n times. Such secure encryption would ensure that your end product remains free from brute force attacks and differential attacks.  Source Code: Image Encryption Using Chaotic Map Also try: Python Project Ideas and Topics 6. Measure an Object’s Diameter in an Image by using MATLAB Computer vision is a prominent field of study. It finds applications in many areas due to its unique utility. You can use MATLAB to measure an object’s diameter in an image.  This application can find uses in many areas where you can’t find the diameter of an object physically. For example, suppose you need to measure the size of a building. In this case, the physical measurement would be nearly impossible, so you’ll need to use computer vision. Your MATLAB script should first import the image, separate the required object from the background, and in the end, use MATLAB functions to find the object’s diameter. While this project might seem quite simple, it will help you showcase your image processing skills while also highlighting your knowledge of multiple MATLAB functions. Source Code: Object’s Diameter in an Image Using MATLAB Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses 7. Use MATLAB to Automate Certificate Generation This project is also among the beginner-level MATLAB project ideas for students. In this project, you’ll create an automated certificate generator using MATLAB. Many institutions certify companies based on their performance and achievements. Educational institutions also generate report cards and certificates for their students. You can create an automated certificate generator, which will make this process efficient and straightforward. This project idea might seem too simple, but you can make it complicated by adding the functionality of generating detailed reports for large datasets.  Source Code: Automate Certificate Generation 8. Create Light Animations with MATLAB and Arduino This is one of the beginner level MATLAB projects on our list. In this project, you’ll use MATLAB and Arduino to create a graphical user interface to control the lighting patterns of multiple lights. By controlling their lighting pattern, you can create various light animations. Using a GUI will allow you to perform many other tasks while running the animation.  We recommend using Arduino Uno for this project. It’d be the hardware of this project, and the software would be the Arduino IDE. You can connect the Arduino Uno board with the required lights. After you’ve connected Arduino Uno with MATLAB, you’ll be able to create simple light animations with the same.  It’s an easy project, but it’ll surely help you explore real-life MATLAB applications and help you realize its versatility. After you’ve made simple light animations, you can take this project a step further and add more lights to create more complex animations.  Source Code: Create Light Animations 9. Log Sensor Data in MS Excel This project requires you to use Arduino Uno with MATLAB to log sensor data in MS Excel. You can add LM35 (a temperature sensor) to your Arduino interface, which would connect to MATLAB through ArduinoIO.  Once you’ve connected Arduino with MATLAB, you’ll need to create a program that transmits the sensor’s data into an Excel sheet. You’ll need to have MS Excel installed on your PC to complete this project. Once you’ve finished this project, you’d have a graphic user interface that allows you to see the logs of the sensor data. To take it a step further, you can add more sensors and log their data into the same excel file (or in multiple different files). This project will give you plenty of experience in using GUI with MATLAB.  Source Code: Log Sensor Data in MS Excel 10. Simulate an Artificial Neural Network Artificial Neural Networks are machines that imitate the functioning of a human brain. Their purpose is to mimic the behavior of a mind and act accordingly. In this project, you can simulate an ANN by creating models and training them.  Before you work on this project, you should be familiar with the basic concepts of artificial intelligence and machine learning. You’ll first need to create a data model that takes particular input and generates a particular output. First, you’ll need to train the model by giving it a list of inputs and outputs. Once you’ve prepared the model, you’d give the model a data list with no outputs.  After completing this project, you’d be familiar with artificial intelligence, machine learning, and relevant technologies.  Source Code: Simulate an Artificial Neural Network 11. Analyze and Design an Antenna While everything is becoming wireless, their connectivity relies largely on antennas. An antenna’s design can have a significant impact on its connection, power consumption, and data retention capabilities. The design should make the antenna compact while allowing it to have a substantial beam width to perform information transmission without any loss.  It’s an excellent project for anyone interested in electronics and communications. You should be familiar with the workings of antennas before you work on this project, however. For example, you should know about the ideal antenna pattern and how a real antenna works. You should also be familiar with the Yagi-Uda antenna, which is the most common TV antenna you see on rooftops. You can estimate (approximately) the operating frequency of such an antenna by viewing its length. You can build a MATLAB program that can perform such estimation with high accuracy and give you the required results.  Source Code: Analyze and Design an Antenna 12. Build a Circuit Design Calculator To build a circuit, you must calculate the component values by using the circuit theory and its formulae. Circuit theory is among the oldest and essential branches of electrical engineering. And its calculations take a lot of time and effort. You can create a MATLAB program that can perform those calculations and help an engineer design a better circuit. Not only will such a system save the user a lot of time, but it will also enhance the accuracy of circuit analysis by minimizing human error.  Your program can analyze and figure out circuit design with inductors, transistors, diodes, capacitors, and other critical components. The program can design highly complex circuits and solve problems accordingly.  Source Code: Circuit Design Calculator In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses 13. Compress Images without Loss Modern cameras have become capable of taking highly detailed images. But an increase in an image’s level of detail also leads to a rise in its size. That’s why image compression technologies have become prevalent. You can use MATLAB to perform image compression as well.  In this project, you would aim to compress an image without compromising its quality. In other words, you’ll have to perform lossless image compression. To do so, you can use the discrete cosine transform algorithm. To find out how much loss took place while compressing the image, you can derive the mean-square error (also known as MSE) of your process. To implement these algorithms in MATLAB, you’ll have to use the required functions.  Source Code: Compress Images without Loss Also Read: Machine Learning Project Ideas 14. Perform Real-Time Face Detection with MATLAB Face detection can find applications in many areas. You can use face detection capabilities for image enhancement, security, as well as surveillance. While it’s quite natural for us humans to detect faces, we can’t say the same about computers. A simple change in lighting can cause various intra-class variations, that’s why it’s a complicated issue for machines.  You can build a MATLAB-based face detection system, and you can use the Viola-Jones algorithm. There are many other facial recognition algorithms, but we have chosen the viola-jones algorithm for this project.  It first creates a detector object, then takes the primary image, finds the necessary features, and annotates them. This project will give you experience working with facial recognition technology, which has gained popularity in many fields.  Source Code: Real-Time Face Detection with MATLAB Know more: TensorFlow Object Detection Tutorial For Beginners 15. Build Laser Guidance for a Vehicle In this project, you’d develop a program that can use lasers to inform the vehicle of upcoming road conditions. This technology can be really helpful for harsh terrains (such as snowy roads, dirt roads, etc.). You’d need to develop an algorithm in MATLAB that converts the scan sequences into readable data so the user can see what kind of terrain is up ahead. This way, the driver can prepare him or herself accordingly and drive safely. An autonomous vehicle can use this technology, as well.  This project will help you get familiar with the application of MATLAB in automotive engineering. It’ll also help you understand how autonomous vehicles work. You can learn more about this project here.  What are the Skills That You Will Acquire Through MATLAB Projects? Engaging in MATLAB for beginners projects offers a diverse range of skills that are valuable across various industries and fields of study. MATLAB, a powerful programming and numerical computing platform, enables individuals to tackle complex problems, conduct data analysis, and develop innovative solutions. Here are some skills you can acquire through MATLAB project ideas: 1. Programming Proficiency MATLAB simulation projects involve writing code, which helps you develop strong programming skills. You’ll learn about variables, data structures, loops, and conditional statements, which are fundamental concepts in programming. 2. Data Analysis and Visualization It helps in excels in data analysis and visualization. Through projects, you’ll gain expertise in importing, processing, and visualizing data, which is crucial in fields like data science, finance, and engineering. 3. Algorithm Development It allows individual to develop and implement algorithms efficiently. On top of that, you’ll also learn about designing and optimizing algorithms for tasks like, image processing, signal processing, and machine learning. 4. Mathematical Modeling ML is widely used for mathematical modeling and simulations. You’ll acquire skills in creating mathematical models of real-world phenomena and simulating their behavior. 5. Image and Signal Processing MATLAB is renowned for its capabilities in image and signal processing. You’ll learn how to enhance images, analyze signals, and extract meaningful information from them. 6. Machine Learning It offers extensive tools and libraries for machine learning. Through projects, you can develop skills in building and training machine learning models for tasks like classification, regression, and clustering. 7. Numerical Optimization MATLAB is ideal for solving optimization problems. You’ll gain experience in formulating and solving optimization problems, which are valuable in engineering and operations research. 8. Simulink Simulink, a MATLAB toolbox, is used for modeling and simulating dynamic systems. You can acquire skills in system modeling and control design, which are essential in fields like robotics and control engineering. 9. Parallel and Distributed Computing MATLAB allows you to leverage parallel and distributed computing resources. Learning to distribute your computations efficiently is valuable for handling large datasets and complex simulations. 10. Problem-Solving Skills The projects often involve tackling real-world problems. You’ll develop problem-solving skills by breaking down complex challenges into manageable tasks and finding creative solutions. 11. Collaboration and Documentation Working on projects in MATLAB encourages collaboration and the documentation of your code and findings, which are essential skills for teamwork and knowledge sharing. 12. Project Management Managing and completing MATLAB projects requires organizational skills, time management, and goal setting, which are transferable to various professional settings. Why Opt for MATLAB Projects? Engaging in MATLAB project ideas offers several compelling reasons: 1. Practical Application MATLAB is a versatile platform used in academia and industry for solving real-world issues. Through projects, you can apply theoretical knowledge to practical scenarios, enhancing your understanding and skills. 2. Skill Development MATLAB projects cultivate a wide range of skills, including programming, data analysis, and mathematical modeling, which are highly transferable and sought after in many professions. 3. Interdisciplinary Applications MATLAB is not limited to a specific field; it’s used in diverse domains such as engineering, finance, biology, and physics. This versatility allows you to explore various areas of interest and adapt your skills to different contexts. 4. Research Opportunities MATLAB is a common tool in research. Engaging in MATLAB projects can open doors to research collaborations, enabling you to contribute to cutting-edge advancements in your field of study. 5. Career Advancement Proficiency in MATLAB can be a valuable asset on your resume, making you more attractive to employers in technical and scientific fields. 6. Problem-Solving MATLAB projects often involve complex problem-solving, honing your ability to analyze challenges, devise solutions, and make informed decisions. 7. Portfolio Building Completing MATLAB projects creates a portfolio showcasing your practical skills and problem-solving abilities, which can impress potential employers or academic institutions. 8. Personal Growth Working on projects in MATLAB fosters perseverance, creativity, and self-confidence as you overcome obstacles and see tangible results. Join the ML Courses online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Learn More About MATLAB Exploring MATLAB Project Ideas for beginners equips you with a practical understanding of MATLAB and significantly enhances your analytical and computational skills. Delving into projects ranging from simple calculations to complex data analysis and visualization offers an invaluable hands-on experience in today’s data-driven world. Whether you‘re a student stepping into programming and engineering or a professional seeking to refine your skills, MATLAB projects offer a versatile platform for learning and innovation. This guide aims to inspire and equip beginners with a diverse range of project ideas, showcasing the potential of MATLAB in solving real-world problems. As you embark on this journey, remember that each project is a step towards mastering a tool that is indispensable in engineering and science.  If you’re interested to learn more about MATLAB, machine learning, and its relevant topics, check out IIIT-B & upGrad’s Executive PG Programme in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms. You’ll find plenty of valuable resources to answer your questions. Refer to your Network! If you know someone, who would benefit from our specially curated programs? Kindly fill in this form to register their interest. We would assist them to upskill with the right program, and get them a highest possible pre-applied fee-waiver up to ₹70,000/- You earn referral incentives worth up to ₹80,000 for each friend that signs up for a paid programme! Read more about our referral incentives here.

by Pavan Vadapalli

09 Jul 2024

Blogs

47385

The reliability and quality of your research depend upon several factors such as determination of target audience, the survey of a sample population, choice of technique and methods for research, and analysis of results. The answer to all these questions is the research design. An effective research design creates minimum discrepancies in data and improves trust in the research information that is collected and analyzed. An impactful research design offers a very less margin for errors and ensures accuracy in all types of design. This blog decodes the types of research design, its elements, and salient features. Check out our artificial intelligence free courses to get an edge over the competition. Research design, simply put, is a specification related to the processes and methods for gaining information needed. It is an operational pattern that is overall a framework of any project. This states precisely what information could be gathered or collected from a source by the use of specific methods. The research design also helps to gain insights into the methods to be employed for the same. This is why you must be wondering if and which research design is diagnostic in nature? What is Research Design? To begin every important research, the researcher chooses a structure of techniques and methods to be applied to the research process. This structure or framework is called research design. Research design enables researchers to hone the ideal research methods for the topic at hand and establish an environment for successful research studies.  Benefits Of Research Design Any research design functions like a bridge that connects what has already been established, that is, research objectives, to what actually has to be performed as part of the conducted study. The final goal is to achieve the specific objectives. It tries to gauge what a client will need regarding findings and arrive at the analytical work on gathered data that will convert these into relevant findings. Are There Different Types Of Research Design? If there is no explicit design, a researcher will have ideas that are fogged around what needs to be performed. Thus, it is extremely advisable that any design gets put up on paper since it is obvious that any study might go down the drain if the concepts exist in a researcher’s mind only. Unless the research design is clear and written or drawn up well, the research actually becomes flaky in terms of structure. This is why there must be clarity to help understand the research design types. The research design topic explains the type of research, whether it is experimental, correlational, survey, semi-experimental, or review. It also defines the sub-type, be it research problem, experimental design, descriptive case study, etc. You can also consider doing our Python Bootcamp course from upGrad to upskill your career. The three most common research design types are data Collection, measurement, and analysis. The research design type is determined by the research problem faced by the organization, and the design phase of the research study determines what and how to use research tools.  What Are The Features Of Research Design? Any research design is a strategy that suggests the specific means by which you can provide answers for any of the research questions, test any hypotheses, and also achieve research purposes that help select from the apt decision alternatives. This helps solve any management problem and even take advantage of an existing market opportunity, besides helping understand which research design is diagnostic in nature and the research design types. Among the types of research design, a formal design has advantages that are specifically appreciated when an investigator decides on the required data. If the data collected among types of research design, leads to being irrelevant, the result is inefficient as well as puzzling. It is more serious when you end up ignoring some data that might have been necessary. This could be revealed only very later in the stage of analysis. While understanding what are the features of research design, remember that all research design, including research design is diagnostic in nature, actually provides a number of advantages while studying data and also understanding their meaning and research design types. This helps in keeping calculations and also thinking on a path to recommendations and solutions. Remember, this in no way means that design must be in a rigid framework. A good design will guide and never dictate or conduct any research. Main Elements of Research design Research design is a blueprint for data collection, measurement, and analysis. It is the stepping stone for decisions regarding what, where, how much, when, and steps for research design are undertaken.  Featured Program for you: Fullstack Development Bootcamp Course A research design is a schedule of conditions for data collection and evaluation designed to align with the research objectives and available resources.  Join the Artificial Intelligence Course online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. Research design is a plan that recognizes the kinds and sources of information related to the research problem. Research design is a strategy that indicates the methods to be adopted for collecting and analyzing data.  The research design also includes the time and cost budgets constraints that are primary to any research study. FYI: Free nlp course! Although all types of research design have some common characteristics, the key elements that every good research design have are: Purpose statement Data collection techniques Methods of research data analysis Type of research methodologies Possible obstacles to the research Settings for research study Time of the research study Analysis measurement Salient features of Research Design Accurate and reliable results drive the validity of research design. But unfortunately, many companies tend to make crucial decisions driven by skewed research. The main reason for this is the failure of researchers to consider biased research. To achieve 100% accuracy in your research data, you must do everything to safeguard your research against any bias.  Moreover, your research outcome must apply to a wide population rather than a small sample. To guarantee a wide reach, you must get accurate models and account for any margin of error.  The essential characteristics of research design are: 1. Neutrality  When you set up the research study, you may need to make some assumptions about the data collected, and the results projected must remain neutral and free of any bias. 2. Reliability  In regularly conducted research, the researcher will expect similar results each time. Your research design must ensure that the research questions help maintain the standard of results. You can attain the desired results only if your research design is reliable.  3. Validity  The researcher must leverage the right measuring tools to gauge the accuracy of the research results and check whether the results align with the research objectives.  Also visit upGrad’s Degree Counselling page for all undergraduate and postgraduate programs. 4. Generalization  The outcome of your research design must apply to an entire population and not just a confined sample. A generalized design ensures a full-proof design that can be applied to any population with the same level of accuracy. Five Common Types of Research Design  Research design can be categorized into several main types based on their method and purpose: exploratory, descriptive, causal, diagnostic, and experimental studies. 1. Descriptive Design  In this hypothesis-based design methodology, the researcher primarily describes the subject matter central to the research. Descriptive research design applies to natural observations, case studies, and surveys. This method involves data collection, data analysis, and its presentation. It allows the researcher to put forth the problem to persuade others to comprehend the necessity for the research.  2. Correlational Design  True to its name, correlational research design enables the researchers to establish relationships between two related variables. This type of research design method needs at least two data groups. This method can be utilized for observational studies.   Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses 3. Experimental Design  Be it a quasi-experiment, a field, or a controlled experiment, this research design type establishes a clear cause and the effect of any event. The researcher studies the impact of the independent variable on the dependent variable.  Typically, this research design type is applied to solve a specific issue by altering the independent variables and observing the changes to the dependent ones. For instance, an experiment can be performed on the price change, and its effect on customer gratification can be observed. 4. Diagnostic Design  In diagnostic research design, the researchers strive to explore the underlying reason for the occurrence of certain circumstances. This method can assist you in examining in depth the elements that cause specific challenges that your customers may face. The design generally comprises three phases, namely the problem inception phase, problem diagnosis phase, and the problem solution phase.     5. Explanatory Design  As the name suggests, explanatory design is utilized by researchers to explore, expand and explain theories and innovative ideas. This design method is applied to find the missing pieces of a puzzle or obtain clarity on vague aspects of a certain topic.  Qualitative and Quantitative Research Design Here’s a table summarizing the differences between qualitative and quantitative research design based on the provided descriptions: Aspect Qualitative Design Quantitative Design Purpose Understand how and why events occur. Answer questions like who, what, where, how many, and when. Questions Open-ended questions. Closed-ended questions. Data Nature Non-numeric, descriptive data. Numeric, can be quantified. Data Collection Gathered through detailed surveys, interviews, or observations. Collected via structured surveys, tests, or experiments. Analysis Explains concepts, ideas, and behaviors. Analyzed through statistics, graphs, and charts. Use in Research Ideal for exploring ideas and understanding behaviors. Best for measuring and comparing numerical data. Business Application Understanding customer mindsets and behavior. Gathering actionable data for decision-making. Output Rich, in-depth insights and narratives. Quantifiable data and statistical information. Examples Ethnography, case studies, focus groups. Surveys with rating scales, experiments, and polls. Flexible and Fixed Research Design Yet another research design classification is the flexible and fixed research design. Fixed research design is based on quantitative data collection, and flexible research design is based on qualitative data collection. In a static design method, the design is settled and fixed before data collection. With a flexible research design, you have more freedom for collecting data.  For instance, survey questions with multiple choice answers are a part of fixed research design, while surveys, where respondents need to type their answers, are a part of flexible research design.   what are the 4 types of research design based on grouping? Another allocation of research design is made based on the grouping of participants. Mostly, grouping is based on the hypothesis of the research and the sampling of participants. Given below are the four main research design types based on grouping: 1. Cohort Design Study A cohort research design study is a longitudinal research study that takes samples of a cohort group of people (those with similar characteristics). In this kind of panel study, the panel individuals share certain common characteristics. 2. Cross-sectional Design Study In a cross-sectional research study, data is analyzed either from an entire population or from a typical sample at one instance.  3. Longitudinal Design Study A longitudinal research design study comprises of similar observations of the same variants for a short period or a longer period. A longitudinal method is often touted as a kind of observational research study though they may also be characterized as random experiments.   In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses 4. Cross-Sequential Design Study Cross-sequential design study combines both cross-sectional and longitudinal analysis, working to make up for some inherent issues present in both research design methodologies.    An exploratory research design example could involve conducting in-depth interviews with industry experts to uncover emerging market trends and understand customer preferences, laying the groundwork for future detailed studies. How upGrad can help you in this? upGrad offers Masters’ and Doctorate courses in Business Administration with topics in research design studies and methodologies.  The programs offer best-in-class content instructed by leading faculty and industry leaders. upGrad has contributed to successful career transitions for over 100+ learners partnering with over 300+ hiring partners. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Conclusion In summing up our exploration of the Types of Research Design, it becomes clear that understanding the various frameworks is crucial for conducting thorough and impactful research. From qualitative to quantitative methodologies and from flexible to fixed designs, each type offers unique advantages tailored to specific research objectives. Elements such as the research question, data collection methods, and analysis techniques are foundational to selecting an appropriate design that aligns with the study’s goals. Additionally, grouping-based designs provide a structured approach to experimental and non-experimental research. As we have seen, the choice of research design profoundly influences the validity, reliability, and overall success of a study. Embracing the diversity of these designs is essential for researchers aiming to contribute valuable insights into their fields. 

by Pavan Vadapalli

07 Jul 2024

Blogs

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Humans have made several attempts to mimic the biological systems, and one of them is artificial neural networks inspired by the biological neural networks in living organisms. However, they are very much different in several ways. For example, the birds had inspired humans to create airplanes, and the four-legged animals inspired us to develop cars. The artificial counterparts are definitely more powerful and make our life better. The perceptrons, who are the predecessors of artificial neurons, were created to mimic certain parts of a biological neuron such as dendrite, axon, and cell body using mathematical models, electronics, and whatever limited information we have of biological neural networks. Checkout: Artificial Intelligence Project Ideas Components and Working of Biological Neural Networks Image caption: Parts of a biological neural network Image source A Biological Neural Network diagram illustrates the interconnected neurons in the brain, highlighting synapses, dendrites, and axons. This model aids in understanding neural processing and learning in biological systems. In living organisms, the brain is the control unit of the neural network, and it has different subunits that take care of vision, senses, movement, and hearing. The brain is connected with a dense network of nerves to the rest of the body’s sensors and actors. There are approximately 10¹¹ neurons in the brain, and these are the building blocks of the complete central nervous system of the living body. The neuron is the fundamental building block of neural networks. In the biological systems, a neuron is a cell just like any other cell of the body, which has a DNA code and is generated in the same way as the other cells. Though it might have different DNA, the function is similar in all the organisms. A neuron comprises three major parts: the cell body (also called Soma), the dendrites, and the axon. The dendrites are like fibers branched in different directions and are connected to many cells in that cluster. Dendrites receive the signals from surrounding neurons, and the axon transmits the signal to the other neurons. At the ending terminal of the axon, the contact with the dendrite is made through a synapse. Axon is a long fiber that transports the output signal as electric impulses along its length. Each neuron has one axon. Axons pass impulses from one neuron to another like a domino effect. Biological neural networks in machine learning aim to replicate the brain’s structure and function using artificial neurons and learning algorithms like backpropagation. They model complex cognitive processes but lack biological fidelity whereas Biological neural networks in soft computing focus on mimicking brain-inspired computing for tasks requiring human-like decision-making or pattern recognition, integrating fuzzy logic, genetic algorithms, and neural networks to enhance adaptability and intelligence in systems. Learn AI Courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Why Understand Biological Neural Networks? For creating mathematical models for artificial neural networks, theoretical analysis of biological neural networks is essential as they have a very close relationship. And this understanding of the brain’s neural networks has opened horizons for the development of artificial neural network systems and adaptive systems designed to learn and adapt to the situations and inputs. Image caption: An artificial neuron Image source Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses Biological Neural Networks vs Artificial Neural Networks The human brain consists of about 86 billion neurons and more than 100 trillion synapses. In artificial neural networks, the number of neurons is about 10 to 1000. But we cannot compare biological and artificial neural networks’ capabilities based on just the number of neurons. There are other factors also that need to be considered. There are many layers in artificial neural networks, and they are interconnected to solve classification problems. Biological neural networks tolerate a great deal of ambiguity in data. However, artificial neural networks require somewhat precise, structured, and formatted data to tolerate ambiguity. Biological neural networks are fault-tolerant to a certain level, and the minor failures will not always result in memory loss. FYI: Free nlp course! The brain can recover and heal to an extent. But the artificial neural networks are not designed for fault tolerance or self-regeneration. We can still sometimes recover by saving the model’s current weight values and continuing the training from the saved state.  Talking about power consumption, the brain requires about 20% of all the human body’s energy, equivalent to about 20 watts, which is exceptionally efficient. But computers need an enormous amount of computational power to solve the same problem, and they also generate a lot of heat during computation. Artificial neural networks were inspired by the biological neural networks of the human body. The modeling of biological neural networks was a crucial step in the development of artificial neural networks. Many scientists attempted to understand the working of the brain. Artificial neural networks today are being used for various applications, some are biologically related, and most of them are engineering related. Even though biological neural networks and artificial neural networks are similar in function, they still have many differences. Many attempts have been made to understand the complex mechanism of biological neural networks. Yet, they still hold many secrets to unlock and inspire the future of artificial intelligence. Differences Between Biological Neural Networks (BNNs) and Artificial Neural Networks (ANNs) Parameter Biological Neural Networks (BNNs) Artificial Neural Networks (ANNs) Basic Unit Neuron: The fundamental cell responsible for processing and transmitting information in the brain. Artificial Neuron (Node): Simplified mathematical models that simulate neuron functions. Signal Transmission Electrochemical signals via synapses: Neurons communicate using electrical impulses and chemical signals across synapses. Numerical values via weighted connections: Nodes transmit information through numerical weights. Learning Mechanism Hebbian learning, synaptic plasticity: Learning involves changes in the strength of synapses based on activity patterns. Backpropagation, gradient descent, etc.: Learning adjusts weights using algorithms to minimize error. Processing Speed Relatively slow (milliseconds to seconds per signal): Biological neurons have slower transmission speeds due to the nature of chemical and electrical processes. Fast (microseconds to milliseconds per computation): Computational nodes process information rapidly, limited by hardware. Energy Efficiency Very efficient, low power consumption: The brain operates on about 20 watts, highly efficient for its complexity. Less efficient, high computational power required: ANNs, especially large models, consume significant power. Scalability Naturally scalable and self-organizing: BNNs can grow and reorganize their connections as needed. Requires significant resources for large-scale models: Scaling ANNs involves more hardware and computational power. Plasticity High, can rewire and adapt over time: The brain’s structure and function can change significantly through neuroplasticity. Low, fixed structure during inference after training: Once trained, the ANN’s structure remains mostly static. Information Encoding Spike timing and frequency (temporal coding): Information is encoded in the timing and frequency of neuron spikes. Continuous numerical values (usually in range [-1, 1]): Information is represented as numerical values in a continuous range. Robustness Highly robust, capable of coping with damage: The brain can often function effectively despite damage or loss of neurons. Susceptible to performance degradation with changes: ANNs can be sensitive to changes in input data or architecture. Parallel Processing Naturally parallel, many neurons firing simultaneously: BNNs process information in a massively parallel manner. Simulated parallelism using CPUs or GPUs: ANNs achieve parallelism through hardware that simulates concurrent operations. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Conclusion If you are curious to master Machine learning and AI, boost your career with an our Master of Science in Machine Learning & AI with IIIT-B & Liverpool John Moores University. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau

by Pavan Vadapalli

04 Jul 2024