Power Of Automation

One of the very important application areas for automation technology is manufacturing, to several people automation means only the manufacturing automation. In reality there are different kinds of automation which are defined in this blog and the uses of automation is briefly discussed. Generally there are three types, which are automation in production (fixed automation), programmable automation and flexible automation.

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Fixed automation which is also referred to as “hard automation,” refers to an automatic production facility within which the sequence of processing operations is fixed by the equipment configuration. In effect, the programmed commands are contained within the machines within the kind of cams, gears, wiring, and other hardware that could not be easily changed over from one product style to another different style. This kind of automation is usually characterized by high initial investment and high production rates. It’s therefore suitable for products that are made in large volumes. Samples of the fixed automation includes machine transfer lines found within the automotive industry, automatic assembly machines and other certain chemical processes.

Programmable automation could be a style of automation for producing products in batches. The products are generally made in batch quantities starting from numerous dozen to several thousands of units at one time. For every new batch, the assembly equipment must be programmed again and altered over to accommodate the new product style. This reprogramming and changeover takes time to accomplish, and there’s a period of non-productivity followed by a production to endure each new batch. Usually, the production rates in programmable automation are generally less compared to fixed automation, because the equipment is meant to facilitate product changeover instead of product specialization. A number-control machine may be an exemplar for programmable automation. The program is generally coded in most of the computer hardware for every different product style, and therefore the machine is controlled by the system program. Industrial robots could be taken as an example here.

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Mathematics in Data Science

Even though mathematics is different from a field from Machine Learning, it still is greatly involved in the layers and inches of it. We know that logical reasoning is a skill that should be mandatorily possessed by engineers or programmers but factually, specific areas of mathematics are highly important in Machine Learning and those are highly necessary to be learnt by Machine Learning engineers. Read on to know what areas they are and why they are so important.

In a demographic of the mathematical knowledge you need to know to learn Machine Learning. 60% of Mathematics in ML is concerned with Probability, Statistics and Linear Algebra. Algorithms and Complexity is something that you would have learnt in your undergraduate (if you have a CS background) and Multivariate Calculus comes into picture when you deal with a lot of features and very huge data.

Start with Linear Algebra at the Kindergarten level and then focus on basic Probability which covers all Bayesian methods and then move on to Statistics. Once you cover the basics then comes the next biggest thing in ML. Applying the Basics!

All ML algorithms have libraries that make them run in seconds but you don’t know the underlying mathematics that’s happening in the algorithm. Try to implement basic mathematics by coding a ML algorithm from scratch. execute and see line by line to derive the basic mathematical process behind an algorithm. After doing this, you go for core Linear Algebra, Applied Probability(Descriptive and Predictive Modelling) and Applied Statistics.

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Essential Concepts To Study To Become A Data Scientist!

 For a very long time I have been noticing that most people have notions about data science, they think by studying and learning to work on python, R and other tools, they can become a data scientist. Python, R, SQL, are all important in data science but these are all could be learnt easily once you start working in them, it is really the least challenging thing to learn all these tools and languages for a data scientist. What really matters is the knowledge in and about data, a data scientist should have the intelligence to vision through the raw data.

Let us focus on the real thing, we need to think beyond the tools and start concentrating on developing relationships with data because that is the key to become a data scientist. 

A data scientist must mainly possess the skill in understanding the potential of data, its value, threshold and flexibility. We call this as Data Processing. In data science, data will be the dish for which data itself will be the ingredient, which means, the main goal of data processing is to find salutary data by crunching and filtering the raw data.

The process of Data processing:

1.     Gathering of the data. By various platforms, surveys and mediums data(in all forms) will be gathered, this data will not be validated while being picked and that is why it is called as raw data. It is the data in its raw form.

2.     Cleansing of data. The gathered raw data will go through a validation process in which the useless data will be eliminated from the main data, only the useful data will be filtered through this process.

3.     Modification of data. The thoroughly validated data will be rebuilt, manipulated and will be merged with other data if necessary.

4.     Processing phase. This is the ultimate phase where the processing of data takes place, here is where the final solution for a problem will be found. Machine learning algorithms and methods are used in this phase.

5.     Interpretation of data. The final solution of data could be easily read by the data scientists but for the non-data scientists the interpretation of salutary data into an easily readable and understandable way is important. Data visualization is used in this phase of data processing.

6.     Data Storage. It is extremely necessary to store the data especially to store the statutory data so that it could be reused in future whenever necessary. But storing data was a huge concern for the businesses but due to the concept of hadoop in big data this concern has been easily resolved.

This is probably the simplest and shallow explanation on the phases of data processing.

A data scientist must have knowledge in both technical and non-technical aspects of computer science and data science.

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Technical aspects that one must learn to become a Data Scientist:

1. Linear algebra- Singular value decomposition, optimization and probability theory.
2. Mathematics, Stats, Data structure, data analytics and algorithms.
3. Programming languages like python, R, SQL, Java, C++, but python and R are mainly necessary. Python is majorly used in various phases and processes of data science like in importing the SQL tables into code, to create tables, etc. R programming should also be necessarily learnt because 43% of data scientists believe in the potential of the language while solving statistical problems.
4. Data visualization is the other essential part in data science, it is imperative for the data scientists to have thorough knowledge in it.
5. Machine learning and AI algorithms ofcourse.

Always remember that you will not really be a data scientist until you enter into the field in that designation, only after years of proper experience you could be called as the ideal data scientist. So do not expect more from anything, make sure you will keep up with your perseverance towards learning the field.

Learning data science can be tricky, difficult and confusing all at the same time but with the right source of assistance you can easily follow up with the field. I recommend you the data science course of Learnbay, it is really a good source to learn data science as it provides big data analytics training in Bangalore and Data Science Courses In Bangalore.

AI and Analytics in Developing Vaccines for Corona!

The whole world is facing its worst pandemic over the past 100 years due to coronavirus, thousands of vaccines are being created every day but unfortunately none are yet finalized. In such a peril AI and Data Science are serving their parts towards controlling the spread of coronavirus, with their helpful technologies they are helping the government in stopping the contamination. It is obvious that these tech fields are capable of helping collect data but the surprising thing is that they are even serving towards finding a vaccine. How could IT help in finding an antidote for a virus? The answers are down the blog!

AI is identifying, tracking and forecasting outbreaks

AI is living up to the formula of prevention is better than cure, it is helping the government by predicting possible outbursts of virus in places. It analyzes news reports, social media platforms, official documents and other such resources to identify and detect an outbreak. This helps the government greatly to be prior informed about the situation so that they could take precautionary actions and strict lock down about it.

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AI is helping to diagnose virus

An AI company named Infervision has launched a tool or a machine that helps the front-line healthcare workers to detect and identify the virus coherently. Many such inventions are coming up lately like the powerful e-commerce company Alibaba also has built such a system that helps to detect the contamination of disease with 96% accuracy. Such machines to be present in the environment of hospitals can help in various ways possible, mainly by cutting half of the work of nurses and caretakers.

Drones are delivering medical essentials

Stepping out of home could be a risk for big time but it gets mandatory for those who have critical medical conditions, drones are helping such people with utmost safety. It delivers the medical supplies to those who are in need at their doorstep. This is an efficient way of using machines especially if it is helping people to stay safe at home even during medical emergencies.

Developing drugs

Thanks to Machine learning and Deep learning that now we can rely on machines to efficiently work on finding the vaccine. Google's DeepMind is using the latest AI algorithms and great computing support to understand the proteins involved in the case. Machines are always known to deliver the result of a work more accurately and efficiently compared to humans, it is because of their ability to work persistently.

 AI in identifying infected individuals

China has some advanced systems and tools to identify the individuals with viruses without getting in contact with them. Its surveillance system uses facial recognition and body temperature detection software to identify people who might have fever and if possibly have a virus. Similar technology like 'smart helmets' used by officials helps in identifying people who have fever.

There are monitoring systems which use the help of Big Data in identifying the infected people based on their travel history and activity. The amount of time they have spent in a contaminated zone, their conventional activity around the area will be analysed to have an idea upon the potential possibility of them carrying the virus.

Supercomputers with advanced ML working on to find vaccine

Cloud computing resources and advanced systems of various strong tech companies are apparently being used by the researchers to efficiently track the possible development of vaccines. These systems have robust reactive capacity so anything done using these systems could be expected to happen smoothly and quickly.

Artificial Intelligence has again proved its potential and capability during the worst and helpless time. When the technology is used in the right way the consequence will also be right and efficient. These are some examples of ways AI is helping us during COVID-19 pandemic, it is majorly serving through working on finding vaccines for the worst epidemic.

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Artificial Intelligence in Cyber Security

In all sorts of organizations and companies as well as consumers who make use of it, cyber criminals pose a threat. Companies do their best to tackle cyber attacks, but it is hard to predict what new campaigns will come into existence and how they will function. Your perimeter can't be defended against unknown threats, and cyber criminals benefit from that.

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How Artificial Intelligence comes into the role?

Cyber security is increasingly influenced by artificial intelligence and machine learning (ML), using security tools for analysis and identifying potential threats from millions of cyber reported incidents. 

Artificial Intelligence allows us to react smartly and to understand the relevance, consequences, and responses of a violation or a change of behavior.

Using machine learning, a violation can almost be detected immediately, preventing possible damage from a malicious intrusion, stolen login credentials, deploy malware, or otherwise allows attackers to access the network.

Usage of Artificial Intelligence on the positive side

Threats and other potentially malicious activities may also be detected using AI. Conventional systems simply can not keep pace with a large amount of malware created each month, which is why AI can take action to tackle this problem. Cyber Securities companies teach AI systems the use of complex algorithms to detect viruses and malware, allowing AI to perform pattern recognition in software. AI systems can be trained to identify even the smallest ransomware and malware attack behavior before it gets into the system and isolate it. They also can use predictive functions that exceed the speed of conventional approaches.

In multi-factor authentication situations, AI systems can also be used to provide users with access. Different users of a company have various levels of privileges for authentication that also depend on the location of data access. The authentication frame can be much more dynamic and real-time when using AI, and it can change access privileges based on the user 's network and location. Multi-factor authentication gathers user information to understand this person's behavior and to determine the access rights of the user.

It is important that the right cyber-safety firms who are well acquainted with their functioning use AI to its fullest capabilities. Whereas malware attacks have been occurring in the past without any indication of the weakness that they used, AI may intervene to safeguard cyber security firms and their customers from attacks, even if many qualified attacks are involved.

Conclusion: AI will be a technology capable of transforming one 's life in an ideal future world. It will make everything "smarter" and more efficient, integrated into our homes, cars, and devices. Insecurity, it can detect malware in a network instantly and guide the response of incidents and detect intrusion before starting. In short, it will permit us to develop powerful partnerships between humans and machines which will transcend our limits, enrich our lives, and drive cyber security more than the total of their components.

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Big Data Analytics & Machine Learning Course in Bangalore

Big Data is the very essential part in business field because it is the only technology that utilizes all kinds of data. Big Data analytics will help to have clear vision over the solution for any problem, it could be used to have different solutions for different perceptions of a problem. Big data has high potential to decode the essentials of any sector, it is especially used to upgrade the customer experience in business sector. The methods of big data is disciplinary, interesting, reliable and highly classified.

It deals with the data that is in big size so hence, Big data. It contains the process of gathering data from various different platforms, analyzing the gathered data and to taming down the big storms of it into solution. The methods of gathering, analyzing and evaluating happens in a very sophisticated manner of steps.

As the India's very own IT-hub, pursuing a Big Data Analytics course in Bangalore will be beneficial in many technical ways.

Let me start with the BD’s organised way of dealing with all of our data. [ Data in here refers every kind of data that happens over the internet, even a simple search in any browser will also considered as a data ] When all kinds of data gets gathered it will be difficult to differentiate between the useful data and non useful data. To solve this issue, Big Data has is very specific, it differentiates data as, Structured data, Unstructured data and semi structured data.

1. Structured data: Some data are obvious, static and easily fits into some category. For example, if a electronic transaction has happened using Gpay it will specifically fall into its location of file. Usually structured data will be very important among all the data and it is also very easy to work with because such data will be generated by a easily detectable source of application.

2. Unstructured data: This data is completely unpredictable and random. Sometimes the data of this category will be considered useless. For example, if a search topic is misspelled then there will be no intention behind that search, such search could not be decoded because it doesn’t has no foundation. But on the other side of the coin, the unstructured data also conceals some unseen treasure. Sometimes among the unstructured data there lies a pattern that will clearly represents the expectations, needs, necessities of people. This may not happen everytime but when it happens, it gives a big deal out of it. Hidden patterns and correlations between the data says lot than the expected.

3. Semi structured data: It is half of both structured and unstructured, so it contains both static data and random data. Semi structured data can be highly useful in many different business sectors because it mostly possesses truthful data which could be relied upon. For example, If any college university is searched then such data will be of semi structured data. It cannot be decided whether the user has searched the topic to lead onto it or it was a search to compare between other topic.

Similar type the data will be analysed in ML, some of Machine Learning courses in Bangalore.

The above mentioned are the way how data is categorized, now let’s see what will be done with such categorized data, how it the process of Big Data is handled. There is a special 3 V’s in Big Data: Volume, Velocity, Variety. In meantime, another V is added to it, which is Veracity.

Volume: Volume determines the size of the data. Did you know the fact that every day 2.5 Quintilian data is generated? In which I, You have also contributed, because if you are reading my content then you must be reading it by any electronic device with the facility of internet connection, even this will be contributed to the total number of generating data. Handling such number of data is nearly impossible and irritable only for one day, to handle it for everyday is a task of its own. Big Data has the powerful Data base platform which apparently is handling every data generated data.

Velocity: Big Data Velocity deals with the pace at which data flows in from sources like business processes, machines, networks and human interaction with things like social media sites, mobile devices, etc. The flow of data is massive and continuous. This real-time data can help researchers and businesses make valuable decisions that provide strategic competitive advantages.

Variety: Variety refers to the many sources and types of data both structured and unstructured. We used to store data from sources like spreadsheets and databases. Now data comes in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. This variety of unstructured data creates problems for storage, mining and analyzing data.

Veracity: Big Data Veracity refers to the biases, noise and abnormality in data. Is the data that is being stored, and mined meaningful to the problem being analyzed. Inderpal feel veracity in data analysis is the biggest challenge when compares to things like volume and velocity. In scoping out your big data strategy you need to have your team and partners work to help keep your data clean and processes to keep ‘dirty data’ from accumulating in your systems.

Upon the information of such different types and category of data, Big Data specialists uses languages like Apache Hadoop, Cassandra, etc, to find a valuable information out of all these data.

Big Data is interesting, organised, classified and big. If you think you would fit into the field of Big Data then help yourself by registering in Learnbay. It is an institute that provides Big Data analytics training in Bangalore and Machine Learning courses in Bangalore.

Learnbay is recommended because it do gives good coaching upon the subject, after the completion of the course the students will be certified by IBM, which will weigh as high recommendation during the placements. 

Big Data Analytics

Technology is the topic of trend of this decade, from food ordering to money transferring everything happens in seconds with the help of it, due to the steadiness of technology total number of internet users got increased and so did the huge piles of everyday data. Data Specialists were taking care of gathered data until it was bearable but as the data grew bigger there was the necessity of a concept that would handle mighty sets of data in easy and organised manner and thus, the evolution of Big Data happened.

Big Data is the often complex process of examining large and varied data sets to uncover information such as hidden patterns, unknown correlations, market trends and customer preferences that can help organization make informed business decisions.

Importance of Big Data

In this technology driven generation every minute to major operation happens over the internet, it processes each operation as Data irrespective of whether it is a random one or a highly important data. Data in here refers to every operation done by each and every user in the web like logging in to the account, using browser, liking any post in social platforms, commenting opinions, sharing views and perspectives, personal and private details to bank account details, everything is considered. Only due to processing of such gathered data various useful applications could emerge, using such sensitive data it is easy to be precisely informative about the needs of customers, their expectations and validations. Major businesses, IOT, managements, organizations will build the products only according to the report of the analysis, because the report speaks the inside insight of majority of the people, any product built based on the essential needs and expectations of the customers would never fear failure.

All of this could happen only if the report of analysis is available, Big Data becomes highly essential and important in here because it provides the analysis report which contains business strategies for various different businesses. There are sets of organized process in Big Data for fetching the absolute analysis out of a given data set. The data will go through the processes of verifying, polishing, modification and implementation to reach to a specific solution. Big Data is quirky as it believes that the useless data may sometimes hold on to the concealed important information so it also filters the random data and strives to find if any indirect data are available. Hence Big Data is needed in the field of technology.

Learning Big Data

It will be quite tricky to understand the business oriented moves laying in the Data Analytics, you can always learn it by a good institute. Learnbay is one of the best place to study Big Data in because the course provided in here covers all the fundamental concepts of the subject, it helps aspirants to effectively understand and practice the concepts of Big Data.

Learnbay teaming up with IBM is striving to flash the opportunity of effective and easily affordable courses to the technical aspirants.