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Category: Analytics

How The Machine Learning Works Behind Your Favorite Google Meet Backgrounds?

Posted on by Imarticus Learning

Google Meet has been a lifesaver for many professionals and students who are unable to step out of their homes for the last few months. This increasing usage of such virtual meeting platforms has improved the technology reach. This and Google AI has now increased the need for Machine Learning training and opened up a whole new world in technology.

Google’s AI of Google Meet now allows the user to change the background and reduce the noise level as well. Instead of the boring or the interiors of the home as the background, Machine Learning has helped customize the backgrounds for such meetings.

The technology behind the backgrounds

Google uses MediaPipe Objectron to get the 3D dimensions of images on mobile devices. It is also useful for background changes as well. They came up with an in-browser version of the Machine Learning model that can blur or replace the backgrounds. With these combined efforts of the ML, MediaPipe, and the OpenGL technology, its performance is better even in the devices with low power available.

Google uses WebGL for rendering, ML such as TFLite, and ZNNPack for web-based interference.

How does it work?

The MediaPipe uses the new low-level format of the binary code of WedAssembly. This can speed up the processing faster than JavaScript and can improve the speed of the tasks as well. The instructions from the WedAssembly are converted into simpler code by the browser.

  • First of all the ML segregates the user and its background.
  • Now, the user is masked by the ML interference into a low-resolution component.
  • The mask undergoes processing to refine its edges to be a smooth blend with the new background.
  • A WebGL2 is used to get the final output for the video where the mask is suitable with the replaced or the blurred background.

The technology here uses a lighter interference that uses less power and smaller storage space.

Refining the results

Although the masking effect is refined so it makes it easier to blend with the background, it could still end up having a halo effect. The light wrapping disables this possibility. The composting technique refines the edges of the mask and also allows the background light to adjust itself to blend the user with itself. The technique allows the light from the background to spill all over the edges of the mask to conceal the halo effect. This results in the fine blending of the background with the foreground image.

Performance in various devices

In the high-end devices, the image transition through the ML system continues at a higher resolution but in the low-end devices, there is a slight change. In the latter, the working mechanism automatically switches through the lighter models of ML so as to maintain the performance speed. Here, it skips the image refining process to send the final output.

The flexible configuration of the MediaPipe enables it to choose the most effective processing method.

Google AI and ML

The regular updates on Google AI and algorithms have opened new scope in the field of Machine Learning and its various prospects. While the Machine Learning Course provides basic knowledge, there is more to it when it is learned properly.

artificial intelligence coursesSince the internet-based virtual meetings are not going to disappear anytime soon, more changes in the working are expected. With each change, there is more to learn which naturally increases the importance of Machine learning and AI.

Bottom Line

Seeing all these, it would be not a bad idea to enroll in a machine learning course to start with the basics. Though this is a field with no limits, there is sure a lot to learn.

The Growing Need of Data Storytelling as Salient Analytical Skill!

Posted on by Imarticus Learning

Data storytelling is a methodology used to convey information to a specific audience with a narrative. It makes the data insights understandable to fellow workers by using natural language statements & storytelling. Three key elements which are data, visuals, and narrative are combined & used for data storytelling.

The data analysis results are converted into layman’s language via data storytelling so that the non-analytical people can also understand it. Data storytelling in a firm keeps the employees more informed and better business decisions can be made. Let us see more about how data storytelling is an important analytical skill & how it will help in building a successful Big Data Career.

Benefits of Data Storytelling

The benefits of data storytelling are as follows:

  • Stories have always been an important part of human civilization. One can understand the context better via a story. Complex data sets can be visualized and then data insights can be shared simply through a story to non-analytical people too.
  • Data storytelling helps in making informed decisions & stakeholders can understand the insights via Data storytelling and you can compel them to make a decision.
  • Data analytics is about numbers and insights but with data storytelling, you make your data analytics results more interesting.
  • The risks associated with any particular process can be explained to the stakeholders, employees in simple terms.
  • According to reports, more data is produced from 2013 than produced in all human history. To manage this big data and to make data insights accessible to all, data storytelling is a must.

Tips for Making a Better Data Story 

  • If you are running an organization, make sure to involve stakeholders/investors in data storytelling. This helps in increasing clarity in communication and they do not find a lack of information.
  • Make sure to embed numerical values with interesting plots for a data story. Our brains are designed to conceive visual information faster. Only numerical insights will make the data story boring and more complex to understand. The data insights should be conveyed in a layman’s language through a data story.
  • Data visualization should be used for data storytelling but it should not hide the critical highlights in the data set.
  • Make sure you imbibe all the three aspects of data storytelling which are visuals, data & narrative. The excess of any attribute can hamper the effectiveness of your data story.
  • The outliers/exception in the data set should be analyzed and included in your data story.

The Growing Need for Data Storytelling 

New ways of data analytics like augmented analysis, data storytelling, etc. are surging a lot in recent days due to the high rate of data production by firms/businesses. One can learn analytical skills from a Data Analytics course from Imarticus Learning. To build a successful Big Data Career, you will need to learn these new concepts in data analytics.

big data analytics courses in IndiaConclusion 

Imarticus Learning is one of the leading online course providers in the country. You can learn key skills via a Data Analytics course from industry experts provided by Imarticus Learning. Start learning data storytelling now!

Top 5 data scientists salaries by location in India!

Posted on by Imarticus Learning

“Data is the new oil” – this quote by Mukesh Ambani perfectly captures the radical shift our world is going through in terms of data collection, processing, and utilization. Thus, in the current context, the job of a data scientist has become dearer to a major chunk of individuals who are actively looking to make radical strides in data science career opportunities.

This is even more prominent in a country like India where a big proportion of the population belongs to the tech-savvy young generation who are actively looking for Artificial Intelligence Trainings to earn decent salaries in the future.

Let us have a look at the top 5 locations in India in terms of salaries paid to data scientists and examine the reasons for the same:

  1. Bangalore – The capital of the state of Karnataka, as well as the start-up and IT capital of the country, has a huge base of young IT professionals working in some of the biggest IT companies of the world.

    This city has been nicknamed the Silicon Valley of India, after the Silicon Valley of the USA which is the biggest tech hub of the world. The average salary of a data scientist is highest in Banglore; around INR 10 lakhs p.a. and it also ranks number one in terms of data science job scope in the entire country.

  2. Chennai – After Bangalore, Chennai is the growing hub of outsourcing data jobs in the country. It has huge IT parks which provide immense opportunities to the emerging techies of the country. The average salary of a data scientist here is a little over INR 9.5 lakhs p.a.
  3. Mumbai – It is the financial capital of the country. Given the scope of IT and data analytics in finances, it is predicted that Mumbai may soon become the largest tech hub of India. Moreover, due to the sheer number of prominent business houses headquartered in this region, Mumbai has the potential to comprise the highest paying jobs for data scientists. Currently, the average salary is around INR 9.1 lakhs p.a.
  4. Hyderabad – Hyderabad is the capital city of the state of Andhra Pradesh and is the emerging hub of tech startups in the country. Big names in the global IT sector industries like Amazon and Google have invested heavily to build the tech infrastructure of this city. Hence, it is quickly emerging as an emerging market of data scientist jobs with an average salary of INR 8.5 lakh p.a.
  5. New Delhi – The capital of India is the fifth-largest city in terms of the average salary paid to data scientists. It has the highest concentration of data scientist talents which has made it a major hub for tech companies in India. The average salary of data scientists at New Delhi is around INR 8.3 lakhs p.a.

In conclusion, it can be said that India has one of the fastest-growing job markets for data scientists in the world. The recent boom of talented young professionals and the growth of BPO and KPOs have contributed to the development of this sector.

Tutorial for Data Prep – A Python Library to Prepare the Data Before The Training!

Posted on by Imarticus Learning

To get accurate and correct results of a machine learning model, you must prepare your data before its usage. Various applications like the DataPrep can prove to help complete such a tiresome work quickly and efficiently. Without making many efforts, with just a couple of lines of coding, the data can be prepared.

Applications like DataPrep assist the user to explore the attributes and the properties of the data in use. In the recent modifications of the application, advanced aspects like the EDA, short for Exploratory Data Analysis can be found which has been working like never before.

How to use DataPrep?

To make the best use of DataPrep, follow these simple tips.

  1. Import required libraries

The first and the foremost step to begin with DataPrep is to install necessary libraries. Generally, different features in DataPrep can be used through different functions and these functions need to be installed before getting started with preparing the data. Initially, a plot function needs to be downloaded which can be effectively used to visualize the properties and other statistical plots of the data under consideration. After this, you will have to import Plotly Express which is further required to download the datasets which you will be working on.

  1. Importing datasets

For importing the datasets, click on the option of import data sets by being on the flow page. For comparison or better presentation of the data, importing is paramount. You can import more than one data at the same time. This can be done by selecting ‘choose a file or folder’ and click the ‘pencil icon’ and insert the desired file. The files inserted can be renamed for a better understanding.

  1. Exploratory data analysis

To begin with, you need to do statistical data exploration and detailed analysis. You can make use of the plot function for this part of statistical data exploration. Generally, the whole data can be converted into a detailed analysis by just using a single line of coding.

After filling in the code you will be able to see the statistical properties, their frequency and their count. In case you wish to get a display of the dataset statistics, you may select the option of ‘Show Stats Info’ on the screen itself.

If you want to explore the data through its individual and separate attributes and not the whole together, it is possible and quite convenient. Exploring individual attributes of the data provides a clear idea about every aspect. Moreover, it supports various plots like the Box Plot etc.

  1. Plot correlation

In the next step, the plot needs to be imported and correlated so that a heat map for different attributes of statistical data can be created out of it. Heatmaps provide a lucid relationship between all the different attributes of the statistical data. DataPrep provides you with three variants of heatmaps.

  1. Finding the missing Data

Lastly, any missing data in the datasets must be searched so that a replacement can be made in case the data found is not required. For finding the data, use of advertising datasets can be made which can highlight at least some of the missing data.

Conclusion

DataPrep works efficiently with python. However, python is not an easy coding language to lay your hands on without having proper Python training.

You may consider Imarticus learning for getting professional assistance for the different subject matter.  A python programming course can also be taken up at Imarticus for a deep insight into python.

How to Start a Career in Machine Learning?

Posted on by Imarticus Learning

The field of Machine learning is expanding fast nowadays with the application of smart algorithms being applied from apps to emails to as far as marketing campaigns. What this means is that machine learning or Artificial Intelligence is the new in-demand career option you can choose.
But being a new field comparatively, you may have many doubts and confusion as to how you can actually get yourself to adopt Machine learning as a career. Let’s ponder over some things you need to master to get your career in machine learning startup.

Understand the Field First

It is an obvious but important fact. Understanding the concept of machine learning and basic math behind it along with the alternative technology while also having hands-on experience with the technology is the key to dive into this field at first.

Covert Problems in Mathematics

Having a logical mind is imperative in machine learning. You need to be able to blend technology, analysis and math together in this field. Your focus on technology must be strong and you must possess curiosity along with openness toward business problems. The ability to pronounce a business problem into a mathematical one will take you long into the field only.

Background in Data Analysis

A background in data analysis is perfect for transitioning or getting into machine learning as a career. An analytical mindset is crucial for success in the field, which means one has to possess the ability to ponder over causes, consequences and discipline to search for the data and digging into it, understand the working and its consequences.

Gain Knowledge of The Industry First

Machine learning, like any other industry, possesses its own unique needs and goals. Therefore, the more you research and learn about your desired industry, the better you’ll do here. You have to study the basic and everyday working of the industry along with all the technicalities involved in it.

Where to Find Work as Machine Learning Expert

Job portals are a good way to find work in your starting days in machine learning. You can apply for a job in portals such as Indeed.com, Monster, Glassdoor, etc. You can sign up on some freelancing site (such as Upwork) too to get your starting assignment as a machine learning expert.

The Best Companies to Work For in The Field

Two types of companies can provide you machine learning job as present:  huge MNCs and established companies, or start-up businesses.  There are two basic markets at present for machine learning experts for you to tap on. First is the Cloud and the other is the logs, which allows companies or analytics to let customers create their own algorithms.
The large companies which dominate the data analysis and Machine learning field include Databricks and IBM Watson Analytics.  Google has also made forays into the AI recently while many of its partners are also looking for professionals to get their machine learning initiative started.

Related Post : What are The Skills You Need to Become a Machine Learning Engineer?

Top 5 Careers in Data Science You Need to Know About!

Posted on by Imarticus Learning

Data Science is a rapidly growing field with over 2.7 million data science and analytics job openings expectations in 2020 alone. The number of online users is increasing at a tremendous rate, and with it, the need for data science professionals is also increasing.

As per reports, 2.5 quintillions bytes of data are produced every single day. And there is a huge demand for professionals who can clean, monitor, visualize this data and obtain valuable insights from it to innovate Big Data solutions for technological advancements.

The field of data science is very wide and offers plenty of job opportunities for professionals. If you are interested in computers, maths, and data analysis and share a passion for technological discoveries, then Data Science is the perfect career for you. By enrolling in a data science course, you can become a qualified data science professional and help transform the world for the better.

What is Data Science?

Data Science is the science that helps in data-driven decision-making using methods of collecting, storing, computing, analyzing, and managing data of organizations. Every technology that we use today is based on data. Our online purchases, YouTube recommendations, Instagram feed, etc. are all examples of human-data interactions.

Data collection helps improve the user experience while surfing the internet. You might have noticed that while shopping on Amazon, the site recommends other useful products that can help you along with your original purchase. These recommendations are based on your previous purchase history, search history, and payment history. It also sends you a reminder for your habitual purchases like groceries, etc. They act as a trigger to make you purchase the product without being spammy.

In this way, Data Science professionals help both businesses and consumers. It is proven to increase a company’s profit revenue and helping customers spend less money on a purchase with strategic placements of ads of products at discounts. Thus, it creates a win-win situation for both the company and the customers leading to healthier economic development.

Benefits of Data Science

Data Science not only has commercial benefits but also improves the performance of multiple industries such as public health, food, farming, education, airlines. From its wearable trackers to monitor and improve patient’s health to accurately diagnosing diseases and finding cures, data science is contributing a lot to the healthcare sector.

Its image and voice recognition applications give you recommendations for tagging your friends in pictures on social media. With Google and Siri, you get the job done without the need to type on your smartphone. Data Science helps the airline industry by predicting flight delays, identifying weather, and helping in decision-making for airplane buying.

Data Science has also transformed the Education Sector with its features, including analytical evaluations of students, helping in decision-making for student admissions, etc. It has been very useful in lowering the dropout rate and increasing the engagement rate of students in schools.

Data Science course helps farmers with solutions on the right amount of seed planting, fertilizer, water, etc. It also predicts weather conditions that let farmers modify their harvesting plans accordingly.

The benefits of data science are countless. It is impacting almost every industry in one way or the other. It has become such an inherent part of our lives that we do not even recognize its presence. It is at the center of all the industries today. Building a career in Data Science is a wise option. It not only pays well but also lets you drive the transformation of the world towards a better version of itself.

There are a lot of in-demand data science careers these days. So, let us explore the top 5 data science careers that you can build a substantial career in.

Top 5 Data Science Careers

1. Data Scientist

Data Scientist is one of the most popular jobs worldwide. Data Scientists examine Big Data and perform cleaning and organizing of the data. They process a large amount of complex information and find patterns in the data that help them drive strategic decisions. Data Scientists are well-versed with Machine Learning algorithms.

The solutions discovered by Data Scientists are very important for companies for their business growth. These solutions also let companies have an edge over their competitors and achieve all their business goals. Data Scientist is a technical job and demands a variety of skills. The salaries in this career are among the highest-paid jobs.

Prerequisites for Data Scientist Job

  • Problem-solving skills
  • Coding Language
  • Data Visualization
  • Business Awareness
  • Machine Learning Algorithms

2. Machine Learning Engineer

Machine Learning Engineers create data funnels of the cleaned and organized data. They train a predictive model to help predict the target variable. Their developed models analyze the data trends in the future to help businesses make the right decisions.

The data used has a lot of dimensions to it. Machine learning algorithms make the tasks easy. These engineers test and analyze their models for the best possible outcomes. They are good with statistics, programming, and software engineering skills. In addition to training, they also do data analysis at times to better understand the datasets.

Prerequisites for Machine Learning Engineer Job

  • Programming
  • Data Modeling
  • Machine Learning Algorithms
  • Software Engineering & Systems Design

3.  Data Engineer

Data Engineers integrate data from various sources and perform batch processing on it. Every company needs the development and maintenance of its data pipelines. Data Engineers collect big data from various sources and then optimize this data as per the problem statements. They are responsible for writing queries and providing a streamlined flow of big data.

They create an interconnected data ecosystem that helps data scientists by making the information easily accessible to them. Their prime focus is on the system and hardware which assist in the analysis of the data. They deliver effective warehouse methods to the organization as well.

Prerequisites for Data Engineer Job

  • Machine Learning Algorithm
  • Coding
  • Data Warehousing
  • Database knowledge

4. Business Intelligence Developer

Though Business Intelligence (BI) Developer is more of a non-technical job, it is a big role to play in the successful implementation of data science solutions in the organization. Before going into the job responsibilities for this role, let us first understand: what is business intelligence? Business Intelligence is a combination of strategies and technologies that helps in the data analysis of an organization for business information.

BI Developers formulate and implement business policies created using the insights from data analysis done by the technical team. They act as a bridge between the technical and the non-technical team through their ability to understand the technical stuff and presenting that in a simple non-technical way to the clients. They have a good understanding of business. Using their deep understanding of data, they develop BI tools and applications that help the end-users comprehend the system.

Prerequisites for Business Intelligence Developer Job

  • Business Acumen
  • Data Warehousing
  • Business Intelligence Software Integration
  • Communication Skills
  • Problem Solving

5. Data Analyst

Data Analyst is another important career in the field of data science. Data Analysts are responsible for understanding, transforming, and manipulating the data to suit the needs of the company. They store the data of the different departments of the company. Data Analysts help companies to understand the reason behind the success or failure of their projects.

Their roles also involve web analytics and A/B testing analysis for businesses. The results of their analysis help companies understand the loopholes in their plans. They provide solutions to business problems and also assist in the decision-making processes. They are also an important link between the technical and other working departments of the company.

Prerequisites for Data Analyst Job

  • Critical Thinking
  • Machine Learning
  • Data Visualization
  • Communication
  • Process Modelling
  • Microsoft Excel

Data Science careers are in constant demand as businesses are quickly moving towards automation. Data Science professionals are needed in almost every field, be it government organizations or private firms. Owing to their high importance, the data science salary packages are also quite lucrative. The average salary range varies from job role to job role in this field. Nevertheless, a career in data science is among the highest paying careers for professionals.

If you are looking to break into the data science field, there are various ways to prepare yourself. One can join a data science course and gain the required knowledge and skill-set for making a career in this field. There are various certificate courses, degrees, and diplomas available these days, both online and offline that you can pursue. After gaining the required qualification and skill base, you can apply for fresher roles in this field.

Many companies are on the lookout to hire data science professionals. So, finding a job would not be that difficult. Once you gain some experience, you can also take on another career pathway in this field as most of the data science careers are interconnected and complementary to each other. To succeed in this field, you need to have a technical mindset, an eagerness to learn, and a passion to develop solutions to problems.

Bringing AI and Machine Learning Accessible to Enterprises Credit to Cloud!

Posted on by Imarticus Learning

Artificial Intelligence (AI) technology has been a game-changer for businesses. It has revolutionized how businesses operate and get the work done. Artificial intelligence technology imparts machines with the ability to understand and apply intelligence while processing complex data that would’ve earlier required human aid. Machine learning is a part of Artificial Intelligence technology and entails training machines to process information using large data sets.

Let’s discuss a real-life scenario to understand the functioning of machine learning technology better. Have you ever wondered why the prices keep on fluctuating when you book a Cab using Uber? Well, that’s machine learning technology into action for you.

Dynamic pricing is how the machine learning algorithms leverage buyer’s curiosity, demand, traffic congestions, etc. to regulate the cost and price the fare accordingly. Machine learning is increasingly being deployed by organizations to help with complex real-time data processing.

AI & ML Accessibility  

Accessibility has always been a challenge when it comes to adopting AI & ML technology for businesses; cloud solutions have helped paved the way for even smaller businesses to adopt AI & ML technology. Here is a list of few cloud services that is changing the way businesses adapt to AI & ML solutions.

  1. Amazon Web Services (AWS)

Amazon needs no introduction; it has always been about boosting customer satisfaction and improving business practices. AWS is a cloud solution offering from Amazon that provides a diverse range of machine learning solutions including Amazon SageMaker that simplifies the process of creating, training, and deploying machine learning models to work. Other machine learning-related solutions by AWS includes dynamic pricing models, search recommendations, automated customer service, etc.

  1. Google Cloud

Google’s cloud solution is second in this list of cloud services that have made machine learning more accessible for companies. After the development of an open-source platform named TensorFlow, Google has achieved new heights in the AI & ML arena. In addition to its indigenous open-source application, it is also associated with DeepMind, one of the most prominent players in the machine learning space. AlphaGo is a flagship program by DeepMind that has revolutionized the machine learning and AI space.

  1. Azure by Microsoft

Azure by Microsoft is another prominent name in the list of cloud platforms that have made machine learning more accessible for organizations of all sizes. Azure boasts of in-built machine learning services for organizations that want to leverage machine learning models into their business operations. To make it more easily and user-friendly it has both code-based and drag and drop functions. Azure aims to revolutionize the machine learning space by focusing on building a bias-free responsible machine learning solution.

Conclusion

Machine learning is an indispensable tool for businesses in the contemporary that rely on the use of sophisticated technology to operate and reach new customers. Machine learning career is in huge demand as more and more businesses are leveraging this remarkable technology to grow their business and optimize their operations.

One can opt for a machine learning course from reputed institutions like Imarticus Learning to obtain comprehensive knowledge about this technology and obtain a job with some of the most reputed organizations.

How Big Data Analytics Course Help to Achieve Better Data Management In Banking?

Posted on by Imarticus Learning

What is Big Data Analytics Course?

Banks create a huge amount of data regularly. The speed of data creation is slower than the speed of processing this information. The Big Data Analytics course can help the banks to diversify the data into Big Data that can be stored in a divided manner for better understanding and longevity.

Big Data Analytics Course focuses on the Collection and organization of the data and its conversion into such information that is worth analyzing and studying to draw meaningful conclusions. It educates about the ways to handle Big Data that cannot be used making use of the traditional methods.

Companies require specialized personnel of Big Data Analysts specifically for this job. Jobs in this particular field are shooting because of the usage of the internet and technology at large. This amalgamation of Finance and Technology can give rise to Fintech (Financial Technology)

What are the sources of Big Data?

Analysts can find Big Data whenever they want to make use of it. Some of the most important sources of Big Data are mention below:

  • Sensors- Used in Cars, Industrial machinery, Space, Technology and CCTV Footages, etc.
  • Social Networking Site- Facebook, Twitter, Instagram, Google, etc.
  • Transportation Services- Data from Aviation, Railways, Shipping, etc.
  • Online Shopping Portals- Data from Amazon, Flipkart, Snapdeal, eBay, etc.
  • Institutions- Data from Hospitals, Banks, Software Companies, Educational Institutions, etc.

Characteristics of Big Data

Big Data has been characterized by 3Vs. All the Vs stand for the following:

  • Volume- Data in Tera Bytes, Zeta Bytes, Giga Bytes, etc.
  • Velocity- The speed at which the data grows fast.
  • Variety- Includes the unstructured and Semi-structured data.

Advantages of Big Data Analytics Course in Banking

Big Data Analytics Course has been proved advantageous in numerous fields and industries but the Banking Sector has been able to make the best use out of it so far. The following points show how Big Data Analytics Course can help Banking Sector to achieve Better Data Management:

  • Boosting the Overall Performance

data analytics courses in IndiaAs far as the performance is concerned, both the employees’ and the bank’s performance can be analyzed through Performance Analytics. The Big Data Analytics Course helps to ascertain the loopholes in the performances that can be corrected in the future course of action.

  • Providing Personalized Banking Services to the Customers

The deposit or withdrawal of money in a bank account or the usage of bank cards at shopping sites, all are activities or information of the customers that a bank has. By using this information and the tools from the Big Data Analytics Course, banks can design some personalized services for their specific customers. This can benefit the banks by the way of increased customer loyalty.

  • Managing the risks to the Data

With a discreet vision of the market, banks can regulate their policies or can bring changes in their framework. If the return from the market keeps running low, after analyzing, banks can raise the loan interests for the customers in that respect.

To avoid frauds, banks can turn down or withdraw payments from questionable Investments in the market.

  • Sentiment Analytics

Under this, the banks analyze the data through social media and understand the patterns and behaviors of the customers on social media platforms. This helps to know the sentiments of people about a brand, firm, Company, or product.

Conclusion

Anyone aspiring to be a Big Data Analyst must take up a  Big Data Analytics Course. Considering the current scenario where every company deals with its data through Information Technology, the use of Big Data Career is on the rise.

Related Article:

How To Upskill Your Career In Big Data Analysis

 

What’s Machine Learning All About?

Posted on by Imarticus Learning

All you data enthusiasts out there, you surely must have heard of this term ‘machine learning’ in the past few years. While not many know what exactly this term refers to, which has been leading to quite some amount of befuddlement. Let’s start small firstly by decoding what is exactly meant by Machine Learning. It refers to a method of data analysis which automates analytical model building. In other words, the process of machine learning assists computers in unearthing hidden insights without the need of having to train or program them to accomplish the same. What this concept means today and what it meant in the past, are entirely different things, courtesy the rapid developments in computing technologies.

Machine Learning emerged from pattern recognition, with generous helpings of a certain theory, which firmly believed that computers were capable of learning to perform certain tasks, without being programmed. Those experts, who were either working with or were highly intrigued by the concept of Artificial Intelligence, believed this to be the next step in the existence of smart machines.

There were various efforts takes, albeit with numerous trials and errors, in order to check whether any computer, could independently learn from data. While the earlier, pre-formed concepts and untested theories, existed for quite some time; but machine learning as we know it today has been quite a recent development.

Whether you belong to the IT field or not, you surely have experienced the marvel, that machine learning is, in your daily life. Don’t think that is possible? Well, machine learning the way it exists today is not really a very complex concept, nor is it made up of the most complicated algorithms. For instance, the new and polished, Google car that is capable of driving itself, online recommendations from your favourite websites, the feedback mechanisms, which almost all businesses depend on nowadays, are all examples of the working of machine learning.

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Most industries have already come to the conclusion that Machine Learning is essential for their growth and development. This is the reason why many industries, which work with a large amount of data are looking for ways and means to inculcate machine learning. This would majorly benefit the industries because of the fact that, data can be gleaned most efficiently, without any human intervention. Financial Services which include banks and other related business have begun to use machine learning, in order to accomplish two primary purposes, namely the identification of important insights as well as detecting frauds, if any. Government agencies have also begun to make use of this concept, in order to minimize identity theft, as well as to increase the efficiency of their daily work and saving money. Machine learning is sought after as a growing trend, especially in the healthcare industry, which is a result of the popular usage of wearable devices and sensors. Other fields which are increasingly making use of Machine Learning are Marketing and Sales, Oil and Gas. This is an even more reason for the increasing demand for Data Scientists well versed in machine learning.

Imarticus Learning is hosting a webinar where our industry expert Dr Nisha Arora will discuss Machine Learning: What it is and why it matters. “Machine Learning” is a term trending not only within the IT industry but also in industries such as healthcare, marketing, finance, human resource and education.

MATLAB: 10 Important Reasons Why MATLAB is Important for Every Engineer!

Posted on by Imarticus Learning

MATLAB is an irreplaceable resource for researchers, specialists, and architects. The extravagance of the MATLAB computational climate joined with a coordinated advancement climate (IDE) and direct interface, tool stash, and reenactment and demonstrating abilities, makes an innovative work apparatus that has no equivalent.

From fast code prototyping to all-out deployable applications, MATLAB remains as a true advancement language and climate serving the specialized necessities of a wide scope of clients. As an assortment of different applications, each book section presents a novel application and utilization of MATLAB for a particular outcome.

1. Designing NEW SOLUTIONS: MATLAB

Many designing disciplines depend on different sorts of math to guarantee that the aftereffects of any plan interaction or new hypothesis of how the universe functions really bode well. Another structure isn’t a lot of utilization in the event that it can’t hold up to the anxieties set on it.

2. GETTING AN EDUCATION

Mathworks places a solid accentuation on schooling. Truth be told, you can track down a unique spot for instruction-related materials at MathWorks.com. Regardless of whether the association that utilizes you doesn’t utilize MATLAB, the standards you learn by working through issues with MATLAB keep principles that apply similarly well to different items.

In case you’re an understudy and you need a duplicate of MATLAB, you can undoubtedly buy it. A few variants of MATLAB are accessible for understudy use, so ensure that you pick the bundle that best suits your necessities.

3. WORKING WITH LINEAR ALGEBRA In MATLAB

It could be difficult to accept, yet direct variable-based math truly is important for the work environment. For instance, to compute Return on Investment (ROI), you should know polynomial math. Similar remains constant for the accompanying:

Anticipating the measure of turnover an organization will have

Deciding the number of things to keep in stock

Settling on life and business choices, for example, regardless of whether it’s less expensive to lease a vehicle or to get one through and through

Making a monetary arrangement, such a deciding if it bodes well to square away a Visa or develops investment funds

4. PERFORMING NUMERICAL ANALYSIS

Mathematical investigation depends on estimation instead of the exactness you see in emblematic math. Playing out certain structure development undertakings is unthinkable without applying mathematical examination, and cosmology appears to require hefty utilization of it too. You most likely will not see a woodworker applying mathematical investigation, yet you will see draftsmen who may have to do as such.

5. Engaging IN SCIENCE

MATLAB is probably going to be utilized to investigate new speculations. When applied to science, MATLAB helps you perform “imagine a scenario in which” investigation that assists you with affirming the reasonability of a hypothesis.

Obviously, science is utilized from numerous points of view. For instance, you may be engaged with the wellbeing business and utilizing science to discover a solution for disease or the Ebola infection. A PC researcher may search for another approach to utilize PC innovation to help those with openness needs.

6. Connecting with MATHEMATICS

A few groups basically appreciate playing with math. It’s the explanation that such countless hypotheses are accessible today to take care of issues. These individuals are locked in with math such that a couple of others can promptly comprehend. MATLAB makes it conceivable to play with math, to make better approaches for utilizing numbers to perform valuable assignments.

7. Investigating RESEARCH WITH MATLAB

After an inquiry is posed and an answer is given, an analyst should persuade associates that the appropriate response is right and afterward feasible to incorporate. MATLAB allows you to check the appropriate response and confirm that it does, truth be told, fill in as the specialist recommends. After an answer is demonstrated, the scientist can utilize MATLAB further to characterize unequivocally how the appropriate response is utilized.

8. Strolling THROUGH A SIMULATION

Utilizing a recreation as opposed to a true partner is a minimal expense way to deal with testing that is a fundamental piece of any kind of logical or designing undertaking today, consequently:

Saves human lives

Saves time

Upgrades the capacity of individuals required to attempt different arrangements

Decreases costs

Works on the odds of another innovation succeeding

Builds the security encompassing another innovation

MATLAB makes recreations conceivable in a few distinctive manners. It may not generally give a total arrangement, yet you can utilize it to play out these sorts of assignments:

Characterize the first mathematical model used to characterize the innovation and hence the recreation

Make singular depictions showing how the innovation will function dependent on the model

Show the work process for an innovation utilizing activity strategies so that even less-gifted partners can see the innovation at work

9. Utilizing IMAGE PROCESSING

Picture preparing is the demonstration of dealing with the pixels in a picture utilizing math procedures to adjust the grid esteems. Procedures, for example, adding two networks together are normal when performing picture preparation.

10. Accepting PROGRAMMING USING COMPUTER SCIENCE

PC researchers depend intensely on math to perform assignments. MATLAB, with its rich tool kit, can be utilized to quickly model a calculation prior to submitting the improvement assets to carrying out the calculation in another dialect, like C++ or Java. Developers usually rely upon MATLAB to upgrade their efficiency.

While making an application, you should guarantee that the yield is legitimate. Be that as it may, confirmation is only one use. PC researchers manage an almost endless inventory of questions that might actually profit from the utilization of MATLAB.