DEV Community: satyanageshan

Online Teaching Jobs Part-Time at Simpliv

satyanageshan — Tue, 21 Jul 2020 10:29:44 +0000

How to Become an Expert:
Expand the depths of your knowledge in your area of expertise by becoming an author with us! Simpliv offers abundant opportunities for you to sharpen your teaching skills, pursue your passion, and become part of its global pool of experts.
Choose from the expansive array of topics that interest you. Simpliv has courses on any topic that could interest the human mind. This is a wonderful opportunity to become popular with our community of learners.

Steps:

Choose your Topic: Your interest could be on any topic ranging from photography to geology or from food to Python programming and many more.
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 Upload your Video:
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Prepare a video of a sample lecture and upload it to us. We will review and get back to you.
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 Grow your Community:
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What is more satisfying than having learners craving for your teaching? We will connect you to your audience.

Why Choose Simpliv?

• World’s Best Online Video Courses.
• Students Spread over the Length and Breadth of the Globe from many Age Groups.
• Opportunity to Work for a Reputable Online Platform.
• Set your own Working Hours and Teach Topics of your choice.
• Transparent and Punctual Payments through Secure Online Transactions.
• Earn money and Become famous among students all over the globe.

Become an Instructor: https://www.simplivlearning.com/for-author

Contact for More Information:

USA:
Phone: +510-849-6155
Email: support@simpliv.com

India:
Phone: 080-68439514
Email: support@simpliv.com

Top 11 Python Projects That Can Change Your Career

satyanageshan — Wed, 01 Jul 2020 07:08:25 +0000

Python is undoubtedly the one of the popular programming language of the present IT world. Learning Python programming language is a wonderful experience and being a Python developer opens up the door for number of job opportunities.

One way to learn Python and gain expertise in this programming language is to start working on Python projects. Moreover, project-based learning helps to improve student’s knowledge. When it comes to building your career as a Python developer, it is must for developers to work on their own projects that boost their confidence about the skills set they have learnt.

So this article discusses about top 11 Python projects that every aspiring Python learners can develop themselves to become an efficient Python developer. But before that we recommend you to read this blog mentioned below that discuss in detail about different career opportunities related to Python programming.

Python Career Guidelines: How To Become A Python Professional?
Topics that will be covered in this blog are as follows:

What is Python?
Top 11 Python projects.
What is Python?
Python is one of the popular, general-purpose, object-oriented programming language of the present IT industry. Many surveys say that Python is now one of the popular programming languages. Now organizations are considering this programming language to develop their software projects. As a result this programming language is providing a huge number of job openings for aspiring IT professionals.

Python supports cross-platform operating systems. This feature makes building applications more convenient. Some of the globally known applications including YouTube, BitTorrent etc use this programming language to achieve functionality.

One important aspect of this programming language is that it not only supports Object-Oriented Programming Concept (OOP) but it also supports Procedural Oriented Programming Concept (POP).

Some of the applications of Python programming language are as follows:

Web development: Python is widely used for developing web development. Python provides a number of frameworks that make it easier for developers to build these projects.
Game development: Python provides a lot flexibility for developers to develop game projects. Python provides number of libraries such as PySoy and PyGame can be used widely for game development.
Machine Learning and Artificial Intelligence: Machine Learning and Artificial Intelligence projects are different from traditional software projects. To develop such projects, it requires a programming language that is more stable, secure, platform independent, flexible and it must be equipped with tools that can handle various unique requirements. Python programming language has such features that makes it most suited programming language to develop ML and AI projects.
Desktop GUI: Python offers many GUI toolkits and frameworks which makes it the most preferred programming language to develop desktop applications. Some of the libraries such as PyQt, PyGtk, Tkinter allow developers to create highly functional Graphical User Interfaces.
Audio and video applications: Python offers such features that developers can use this programming language to develop Audio and Video applications. Some of the applications such as TimPlayer and Cplay have been developed using Python.
Business applications: Python can be used to develop different business applications covering different domains such as e-commerce, ERP etc. Since developing such applications require a programming language which is scalable, extensible, readable etc. and Python offers such applications.

Click Here to continue reading: https://blog.simpliv.com/top-11-python-projects-that-can-change-your-career/

Top 11 Machine Learning Frameworks You Need to Know!

satyanageshan — Wed, 01 Jul 2020 07:06:15 +0000

Software Engineering is not limited to developing and implementing applications or tools anymore. With changing technology landscape, software Engineering has come a long way, thanks to the evolving intelligent systems. Machine Learning and Deep Learning Technologies have created avenues to execute tasks efficiently and more intelligently.

In this entire transformations, Machine learning and Deep Learning frameworks have played a huge role allowing innovation to take the centre stage.

In this blog we will unfold following aspects:

What is Machine Learning?
Machine Learning Real-World Examples
How does Machine Learning Help us?
Types of Machine Learning Algorithms
Top 11 Machine Learning Frameworks and Some Other Frameworks Too
Description: C:\Users\satya\Downloads\Machine-Learning-Python-Regression-Modeling (1).jpg

What is Machine Learning?
So much is said about Machine Learning and the multifaceted benefits it offers. But it is actually difficult to comprehend the advantages unless the fundamentals are laid out clearly. So before going into much detail, let’s understand what is Machine Learning?

Machine Learning is the field that deals with educating the machines to make them able to make decisions like humans.

To define it more specifically,

“Machine Learning is the field that is a subset of Artificial Intelligence, is a process that deals with educating a computer system so that it learns from its own feedback, instead of having to explicitly program it for every task.”

For an instance, when you buy something on Amazon, it starts showing you the similar products related to your last purchase and recommends you some additional products that you may prefer. All these are the tasks possible through Recommendation system that is executed through Machine Learning Algorithms.

Let’s take a look at the real-world examples of Machine Learning!

Click Here Continue Reading:https://blog.simpliv.com/top-11-machine-learning-frameworks-you-need-to-know/

Top 12 AWS Certifications | Right AWS Certification Path

satyanageshan — Fri, 19 Jun 2020 11:00:19 +0000

Amazon Web Service (AWS) is a broad concept. AWS is a subsidiary of Amazon that provides on-demand cloud computing platforms and APIs to individuals, companies, and governments, on a metered Pay-As-You-Go Basis (PAYG). Today, AWS is considered one of the best-in-market providers of Infrastructure as a Service (IaaS). It has spread to over190 countries from the time it was launched.

The main benefits of Amazon Web Services are that it is reliable, easily scalable, and inexpensive. One can join it for Free and pay only for what they use!

Now, you might be curious to know what AWS is and what it does. So, read further to unwind this concept.

Topics that we will be discussed in this blog are as follows:

What is Amazon Web Services (AWS) and What Does It Do?
What is AWS Certification?
How Do I Become AWS Certified?
AWS Certificate Path
Let’s get started!

What is Amazon Web Services (AWS) and what does it do?
Amazon Web Services, called AWS in short, is a comprehensive, easy to use computing platform that is provided by Amazon. It is a secure cloud service platform that offers a range of features such as computing power, database storage, content delivery, plus many other functionalities that help businesses to scale and grow.

Traditionally, companies were dependent on in-house Servers which were very expensive and had limited functionalities. As those companies attained growth, they would need more in-house servers and a complex system to maintain them. Having a huge storage system inhouse would mean huge costs. Building a limited storage system was disastrous if the company took off, as the costs would escalate if the needs increased.

AWS has addressed all these problems. Where you can buy storage as per your current needs and scale it on the go! With AWS, companies pay for what they use. They don’t have to face a huge upfront cost to build a storage system which may not be utilized immediately. AWS customers have the flexibility to use services as per their needs and scale them as they grow.

Click Here Continue Reading: https://blog.simpliv.com/top-12-aws-certifications-right-aws-certification-path/

Should I Learn Python or R for Data Science?

satyanageshan — Fri, 19 Jun 2020 10:58:50 +0000

R vs Python
If you are someone who wishes to make a career in Data Science, then the ultimate question you have to face is, which programming language you should learn and why? There have been numerous discussions on public forums with people advocating for R or Python for plenty of reasons.

Though it completely depends on your choice, comparing both the languages on some grounds will surely help you make the right decision.

This blog will highlight the following key areas:

R vs Python
Comparison between R and Python on the basis of following:
Ease of Installation
Robustness and Flexibility
Ease of Learning
Speed of Processing
Statistical and Analytics Ability
Suitable Area
Reasons to Learn R and Python
Which is Better for Data Analysis and Data Science?
Is Python better than R?
Real-life Use Cases of R and Python
Career Opportunities in R and Python
Which Language to Go First?
What are R and Python?
R is an open-source programming language developed for statistical analysis and computations.

Like R, Python is also an open-source programming language that was initially developed as a general-purpose programming language, and later branched out to be a language for Statistical Analysis and Machine Learning Modeling.

Click Here to continue: https://blog.simpliv.com/should-i-learn-python-or-r-for-data-science/

How to Write a Resume? | Resume Writing Complete Guide [2020]

satyanageshan — Fri, 19 Jun 2020 10:57:12 +0000

What is a Resume?
I will start this blog with a piece of advice: DON’T read it. That is, if you are either of these: Donald Trump, Tom Hanks, LeBron James, Leonardo DiCaprio, Roger Federer, The Beard, Brad Pitt, Steph Curry, Angelina Jolie, Kim Kardashian, Rafael Nadal, et al!

The personalities mentioned here are some of the one-in-a-million people that don’t need a resume because their name is enough. However, for you and me, a resume is our professional identity. It is that succinct, valuable document that will drive our chances of getting selected for a job of our choice.

This blog is for those among you who face these challenges:

You are convinced that you have a great professional profile, but are frustrated at not getting interview calls
You believe that you have given a good account of your professional profile in your resume, but are not getting noticed by employers and job portals
You are unable to describe all your career highlights and accomplishments in a manner that enables your resume to stand out and get noticed
You are seriously wondering if you need to hire the services of resume writing companies to make your resume more appealing.
This blog will explain the elements and dynamics of a resume that employers cannot take their eyes off. I intend to help you address the issues you may have, such as those listed above, or related ones. I am confident that by the time you have read this blog, you will gained a thorough perspective of what it takes to craft a resume that can no longer be ignored.

Let us get started, then! Let us start by getting an understanding of what a resume is, which will serve as the basis for getting a grasp of how to write one that will land you the job you are looking for.

In the process, you will learn some of the most crucial aspects of rules and conventions concerning how to write a resume that is not only proper and relevant to your profile, but also increases your chances of getting noticed and fished out from among a heap of competing ones.

Click Here Continue Reading: https://blog.simpliv.com/how-to-write-a-resume-resume-writing-complete-guide-2020/

What is Amazon Web Services?

satyanageshan — Wed, 27 Nov 2019 05:23:46 +0000

Cloud computing is one of the latest technologies to make an impact on several business organizations around the globe. Cloud computing, in simple terms, is the provision of a variety of web services remotely, that is, on the cloud. Amazon Web Services is the leading provider of cloud services today.
The evolution of cloud services

Cloud services, as we know them today, have an interesting history. Although the concept has existed in a somewhat hazy form from around the 1960’s, it was only in the early 2000’s that the technology started gaining ground. In 2002, ecommerce major Amazon started offering cloud based services for a few services. By 2006, it had formalized this business by naming its cloud services arm as Amazon Web Services.

The market position today

Building up on the advantage of being an early starter, as of early 2019, Amazon is the undisputed leader of this industry, controlling two-fifths of the entire global cloud services market, followed by Microsoft, which is a distant second at 17% and then Google, which has been able to garner eight percent to this market, which is set to reach a size of over $70 billion by 2020, according to Gartner.

So now, back to our main topic, what is Amazon Web Services. What all does Amazon Web Services offer as part of its remote hosting service? The range of services this secure cloud services platform offers is indicative of its strong market position:

 Computing
 Storage of database
 Content delivery
 Plus many other functionalities

The main advantage that companies that take up these services get are that they don’t have to worry about the whole aspect of these functions. Amazon Web Services offers all of these in a tailor-made and customized fashion, whereby companies can pay for only the services they take.

Another major advantage that Amazon Web Services offers is that its services are end-to-end, meaning that companies can conveniently forget about all the headache of having to take care of any of these elements and instead, can concentrate on the core aspects of their business. These services help them to scale up according to their needs, which means they can grow by leaps and bounds.

What all can you do with Amazon Web Services?

Amazon Web Services offers a host of web services. Some of these include:
 The ability to run web and application servers remotely, on which you can host dynamic websites
 The facility of storing all your files on the cloud and accessing them at any time from any location
 Being able to send bulk email to customers
 Leveraging the power of managed databases MySQL, PostgreSQL, Oracle or SQL Server for storing information on the cloud
 Making use of a Content Delivery Network (CDN), which can help businesses deliver static and dynamic files globally, in very quick time
With its features that are aimed at bettering business outcomes for many kinds and sizes of businesses around the world, Amazon Web Services is today a name that many organizations turn to.

Want to master this highly interesting and rewarding technology? Enroll for Simpliv’s online courses on AWS! It has a package on AWS, from which you can develop and deploy your applications over Amazon Web Services. This course will put you on the road to becoming an AWS professional and helps you grow in your profession.

These courses are designed by professionals who have been working on this technology for a number of years. This is a great opportunity learn from them and share your learning with them through Simpliv, the learning platform.
We would like to know what you think of this blog. Please share your thoughts on this comments section!

Introduction to Different Machine Leaning Tools

satyanageshan — Tue, 26 Nov 2019 06:35:25 +0000

We have heard about tools used in many professions. A carpenter, a tailor, a cobbler, an electrician, a sportsman and most others, including you and me, have used our own set of tools in our professions at some or another point of time. In the same manner, Machine Learning, too, has its set of tools. They perform much of the same functions that the tools in all these other professions serve: they make work faster, smother, and easier.

With regard to Machine Learning tools, the primary goal of a tool is to help the programmer or user deliver results from a Machine Learning project in a smooth manner. In this sense, Machine Learning tools go beyond being just algorithms, although implementing algorithms is a function of Machine Learning tools. More importantly, they can arm the user with vital capabilities that can be used at any stage of a Machine Learning project to facilitate work.

Seen in this sense, Machine Learning tools work best when they:

 Are adaptive;
 Have been developed by using industry best practices, and
 A well-knit community contributes to their development.

Now, a look at a few popular Machine Learning tools

So, which are the Machine Learning tools that are widely used across the technological world today? This is our pick of the Machine Learning tools that are both popular and meet the criteria listed above:

Scikit-learn:

Scikit-learn originated in 2007, and is essentially developed for Machine Learning. This Open Source Machine Learning tool, written in Python, can be used for a number of Machine Learning models such as classification, clustering, regression and so on.

Google Cloud ML Engine:

Best-known for its suitability for training complex models; Google Cloud ML Engine offers all the elements of Machine Learning, such as predictive modeling, deep learning, and model building and training. It cuts down companies’ response time to customer emails.

Microsoft Cognitive Toolkit:

Microsoft claims that the Microsoft Cognitive Toolkit can train deep learning algorithms to think like humans. The Microsoft Cognitive Toolkit can handle data from BrainScript, C++, or Python. It comes with a few other features, such as integrability with Azure, interoperability with NumPy, and efficient utilization of resources.

Amazon Machine Learning:

This managed service is essentially suited for helping to make predictions out of Machine Learning models. Towards facilitating this end, it uses visualization tools and wizards. Batch predictions, Data sources, Machine Learning models, Real-time predictions and Evaluations are some of its core concepts.

PyTorch:

Torch based PyTorch uses the Autograd Module for building neural networks, to do which, it provides a variety of optimization algorithms. It can be used on the cloud, and comes with tools, libraries, and distributed learning.

Do Machine Learning tools fascinate you? Are you looking for learning that will take you headlong into this field? Try this vast spread of online courses in Machine Learning from Simpliv, the learning platform. You can enroll for any of these courses to start making an impact in the area of Machine Learning. These course are designed to help you find your feet in Machine Learning.
And, before leaving, could we understand how you felt reading this blog on Machine Learning tools? Did you find it useful? Do you think anything else needed to explained here for your benefit? We want to hear from you on these.

Know what is Data Visualization, its types and different tools

satyanageshan — Mon, 25 Nov 2019 05:53:39 +0000

We are living in a world where images speak louder than words. Advanced technologies help companies do more than just present attractive images. They help them to collect data from different sources, which augments their business in a big way.

This is how one explains data visualization: Businesses collect an enormous amount of data from a number of sources. Though companies collect the required data quickly and efficiently, challenges arise while explaining this data in a way that makes sense to both business owners and stakeholders. This is the challenge that data visualization helps them to address.

Data Visualization techniques help organizations overcome this difficulty in presenting data. Data Visualization is a graphical representation of information and data. It lets you interact with the data and it goes beyond analysis.
Data visualization professionals use several visual elements like charts, graphs, and maps to provide an accessible way to see and understand trends and patterns in data.

As Data Visualization has shown its impact on many fields, it has resulted in a huge number of job opportunities for IT professionals. Today many professionals are looking to taking up Data Visualization courses to pursue a career in this field.

Different types of Data Visualization:
Now let us look into some of the different types of Data Visualization techniques.

Bar chart: Bar charts are very popular graph visualization technique that allows the audience to quickly scan them for quick information. They are used to compare two or more values in the same category.

Line chart: Line charts are a simple way to represent the data. They are used to plot the dependence of one variable on another.

Pie chart: Pie charts are represented in a circular statistical graphic, which is divided into slices that illustrate numerical proportion. Pie charts help in a way to organize the data to see the size of a component relative to the whole. They are good at showing percentage or proportional data.

Maps: Maps Data Visualization techniques are the most widely used in different industries. Maps can be used to position elements on relevant objects and areas – geographical maps and building plans, etc.

Scatterplot: Scatterplot is another Data Visualization technique which is a two-dimensional plot representing the joint variation of two data items. This technique can be used when there is a large number of different data points, and you want to highlight similarities in the data set.

Apart from the above mentioned Data Visualization techniques, there are many other such techniques that can be used to represent the data. You can check these here.

Data Visualization tools

As a huge amount of data is generated all the time, it becomes important to present such valuable data in a pictorial form that anyone can easily understand. So, to make this happen, certain Data Visualization techniques are available that can are capable enough to achieve this.

Now let us look into some of the Data Visualization tools:

Tableau: Tableau is one of the widely used Data Visualization tools. Many startups, as well as tech giants, rely on this tool to create several
interactive visualizations to enhance their business. This tool provides a user-friendly interface and a rich set of library of interactive visualizations that make it the most preferred Data Visualization tool.
1. Plotly: Plotly is also a very popular Data Visualization tool. The major benefit of using this tool is that it allows the users to create charts using programming languages such as R and Python.
Power BI: Power BI with its unique features has become one of the most powerful Data Visualization tools that can handle a huge amount of data. This platform allows users to create customized reports from different sources. This tool is fully compatible with Azure and Microsoft services and it can also directly connect to existing apps and drive analytics to custom systems.
Google Charts: Google Charts is a Data Visualization that runs on HTML5 and SVG. The charts created using this tool are interactive and some of them are also zoomable.
Infogram: Infogram is a Data Visualization tool that allows users to create and share digital charts, infographics, and maps. Coding skills are not required to use this tool.

Apart from the above mentioned Data Visualization tools, there are still many others available, that can be used to create useful visualization that add great value for companies. You can check here for these.

Conclusion: Data Visualization technique plays an important role in efficiently presenting complex data so that it can be understood easily.

We hope that this guide is successful in providing some valuable information about the Data Visualization technique to our readers.

Looking at the number of job opportunities provided by the Data Visualization techniques, many IT professionals are willing to learn this wonderful technology. So, such professionals can take advantage of these online courses(simpliv website) on this particular technology that proves to be very beneficial to them.
If you think anything more information can be added here then please send them in the comment section.

What should I learn first: Data Science or Machine Learning?

satyanageshan — Fri, 22 Nov 2019 04:58:56 +0000

What should I learn first: Data Science or Machine Learning? This seems to be a common doubt in the minds of many people that want to make a career in any of the latest technologies that are becoming an integral part of the tech world, and with it, our lives. This is natural, considering that these two are among many highly and frequently quoted buzzwords in the technology circles today.

Data Science and Machine Learning are distinct in their own ways in terms of their content and their methodologies, but when it comes to their application and their derivation, they are not similar, but share a lot in common. As explained so convincingly in this paper, they both proceed on the basis of data analysis. Why data analysis, you may ask. This is the most fundamental element of both Data Science and Machine Learning. This is because our digital world emits just so much data that it is impossible to employ traditional databases or manual methods to assess them.

The next question that arises is this: why should companies employ all the possible means at their disposal to analyze this data? This is necessary because these gigantic amounts of data will in themselves come to nothing if they are not meaningfully analyzed. It is only when this is done that they can make perceptive inferences, interpretations and analyses that will help them arrive at crucial business decisions based on intelligence rather than through guesswork. This is where the disciplines of Machine Learning and Data Science are used.

Machine Learning is a means by which scientists get machines to perform a given task through observation and experience without having to implicitly program or supervise it. Machine Learning is a method by which errors can be prevented or rectified by building on statistical and mathematical models and bringing predictions closer to the reality. In order to do this, it assesses the suitability of data to a purpose, formulates a specific objective, implements a set of systems and processes, and finally, communicates its findings with the stakeholders.

Data Science makes Machine Learning work for businesses
What is interesting here is that these abilities do not connect with business objectives even remotely. So, there has to be a match between its capabilities of performing tasks more quickly and relatively accurately than through other methods for businesses to derive sense. This is exactly what Data Science enables Machine Learning to perform.

This points to a clear convergence and overlap between these two areas and their interdisciplinary nature. Machine Learning, data visualization, statistics, Artificial Intelligence, mathematics and related ones are used to help Data Science carry out its work.

Let us use this understanding to get back to the original question of this blog: what should I learn first, Data Science or Machine Learning? The answer is YOU! Yes, there is no set method or flow or order by which one should precede or succeed the other. It is entirely up to your aptitude and liking. Learning one before the other or after makes no major impact on your prospects, unless your company wants you to.

We have a large collection of courses on Data Science and Machine Learning that will help you crack the complexities of these two technologies with ease! Do visit these links and see how they can improve your prospects in a big way. Please also let us know what you think of this blog.

Top 8 Technology Trends for 2020

satyanageshan — Thu, 21 Nov 2019 10:12:00 +0000

Python

A programming language used in software development, infrastructure management and data analysis.

React (web)

A JavaScript library for building user interfaces.

Angular

A JavaScript-based open-source front-end web framework.

Machine learning

The scientific study of algorithms and statistical models.

Docker

An open-source platform used to create software packages called containers.

Django

A Python-based free and open-source web framework

CompTIA

A professional tech organization that has four IT certification series ranging from entry-level to expert.

Amazon AWS

A certification that validates cloud expertise.

Deep learning

A class of machine learning based on artificial neural networks.

React Native (mobile)

An open-source mobile application framework created by Facebook to develop apps for Android, iOS, Web and Universal Windows Platform.