Is Python programming used in artificial intelligence?

 Over the years, choosing the best programming languages between a variety of languages is one of the current debates between programmers. However, your preferred language is undoubtedly the one which you are most comfortable to access and enables you to get the job done efficiently. And one such programming language is Python.

Python comes up with a considerable number of inbuilt libraries that influenced the fact that Python is quickly spreading in the area of AI. The extensive range of frameworks and libraries for Artificial Intelligence and Machine Learning, making it the most preferred language for artificial intelligence development companies. Some of the popular libraries are TensorFlow, Pylearn2, Scikit learns, and the list keeps going.

Secondly, Python is known for its robust, easy implementation and large community. Even programmers with basic knowledge can access this language and do wonders.

AI and ML are seeping into nearly every aspect of our lives, helping us in ways that augment our abilities and make us better at what we do. For instance, voice assistants such as Alexa, Google Assistant, Siri, and Cortana are transforming the way we work, study, and entertain ourselves.

AL and ML solutions are intelligent, i.e. they are different from traditional software applications. The focus here is more on in-depth research, data collection, and predictive analysis. Consequently, the language to be used here should be stable, flexible, and have a variety of tools.

One language that holds a special place among all is Python. Python programming language is among developers’ favourite languages for AI and ML development because of its simplicity, shorter development time, consistent syntax, platform independence and access to a huge set of libraries. (Reasons are discussed in detail in the later section)

When you code with Python programming language, you get a well-structured & tested environment where you can quickly turn your ideas into action. Also, it takes less coding and time to solve complex computational problems.

Moreover, as you get closer to understanding and working with Python app development, you will find it easy to handle massive amounts of data (i.e. data analysis and parsing required for AL and ML work well with Python, and its libraries).

AI and ML have a profound effect in the world we live in, with new solutions coming up day after day. Enterprises have realized that there is no better time than now to invest in these technologies. With its amazing set of libraries, simple & clean coding format, fast prototyping, outstanding flexibility, and huge community support, Python makes the development process of AI and ML-based projects a lot easier, fast, and budget-friendly.

If you are looking to learn python with hand-on training then I would recommend you to take a look in Technogeeks AI with the Python program by working IT professionals.

Are there any good alternatives for Selenium (testing framework)?

Selenium is a framework to conduct software testing. It is used mostly to test web applications. With selenium there is no need to write testing scripts, the software comes with easy navigation tools that can write test cases without the need for any script. Selenium can also provide a domain-specific language to write test cases in any of the popular programming languages such as C#, Java, Scala, Ruby, etc.

It can be very useful even for load testing as it allows users to re-use existing functional tests and run them with virtual concurrent users. Selenium is a very powerful open-source testing tool mainly used for automated functional testing via interacting with browser level objects.

Here are the great alternatives to Selenium available in the market: -

1. Robot Framework:

Robot Framework is an open-source automation system that executes the keyword-driven methodology for acceptance test-driven development (ATDD) and acceptance testing. This tool gives structures to various test automation demands. Its testing technique can be additionally increased out by leveraging special test libraries utilizing Java and Python. A famous external library - Selenium WebDriver - is utilized in the Robot Framework.

Test engineers can use Robot Framework as an automation system for web testing as well as for iOS and Android test automation. Robot Framework tool is also not at all difficult to learn for testers who know about keyword-driven testing.

2. Cucumber:

Cucumber removes any barrier among non-technical and technical project personnel. Fundamentally, that is the crucial element of its mystery sauce. Actually, Cucumber can go about as a Selenium alternative or perform in pairs with Selenium both. Its human-readable test cases encourage cross-team coordination and eliminate isolated software QA.

In the Cucumber framework, functional requirements, acceptance tests, and documentation converge into a solitary automatically refreshed source for testers and partners. Additionally, living documentations implement best practices for necessities management.

3. Test Project:

Test Project is the first free tool to make Selenium testing effortless. With a cloud-based interface built on top of Selenium, you can easily start testing in a matter of no time. Developers will find Test Project's SDK familiar and compatible with existing Selenium code, while non-technical testers will love Test Project's codeless recorder. Think twice before spending precious time building and maintaining a Selenium framework – Test Project is available now completely for free.

Selenium is probably the most popular test automation tool in the market at present. Though, there is no official support available since it is an open-source tool, some of the brightest minds are behind the success of this tool which makes the selenium community strong and growing. Selenium is not just a single tool, it is a complete package. It is a suite of tools consisting of quite a few components, each one of them playing an explicit role in the development of web applications. If you are looking to learn Automation Testing then I would recommend you to enrol in Technogeeks training program by working IT professionals.

Will Artificial Intelligence Take Care of Us or Destroy Us?

From SIRI to self-driving cars, artificial intelligence (AI) is progressing rapidly. While science fiction often portrays AI as robots with human-like characteristics, AI can encompass anything from Google’s search algorithms to IBM’s Watson to autonomous weapons.

Artificial intelligence today is properly known as narrow AI (or weak AI), in that it is designed to perform a narrow task (e.g. only facial recognition or only internet searches or only driving a car). However, the long-term goal of many researchers is to create general AI (AGI or strong AI). While narrow AI may outperform humans at whatever its specific task is, like playing chess or solving equations, AGI would outperform humans at nearly every cognitive task.

Today, the most popular A.I. techniques are machine learning and its younger cousin, deep learning. Unlike computer programs that rigidly follow rules written by humans, both machine learning and deep learning algorithms can look at a dataset, learn from it, and make new predictions. Deep learning in particular can make impressive predictions by discovering data patterns that people might miss.

People say these machines will be benevolent. They will be perfect. They will truly be these beautiful, benevolent, creatures. I don't think that is accurate and I don't think that they are going to be terminating, baby-eating machines either. These machines are going to reflect our species and our evolutionary process. Everything we are will end up in these artificially intelligent machines no matter what we do.

It's such a complex and such new technology in a lot of ways. I think we are afraid of ourselves. That is what it is. We are afraid of ourselves and our own unconscious minds. When we are building something that reflects us, it's the one thing we're all afraid to face. We're afraid to face ourselves. Building machines that mirror our consciousness is a very frightening proposition because we have seen how evil people can be.

What we are really talking about here is an artificially intelligent machine that is going to be able to surpass our intelligence level. At a certain point, you can call it a singularity. There will be a certain point where these machines have super-human intelligence. The best-case scenario is that they will be caretakers and they will be our teachers. They will teach us to be a better species. That is what I am hoping for as a techno-optimist.

If you are interested in learning Artificial Intelligence and want to make a career in this field then I would recommend you to enrol in Technogeeks AI program by working IT professionals.

How Does AI Learn Things?

Unlike humans, AI has computational power that allows it to process copious amounts of data. As AI’s tasks continue to become more advanced, there’s also an increase in computational power necessary-which can become expensive. In order to avoid this need, AI was made to be a specific purpose learner. If AI gains the ability to relate data that is similar, it should be able to more efficiently process the data and understand it with a lower amount of power.

AI makes you look better in selfies, prevents blindness, and can even turn water into tastier beer. Tech giants and governments say we’re living in a golden age of AI. Roll out the self-driving cars!

AI is already used in multiple disciplines and industries. These devices are capable of reading, writing, speaking, seeing, and understanding. They are able to recognize emotions, play games, and even debate. Due to these capabilities, AI is often leveraged as a virtual assistant that can help plan meetings; as a tool to simplify the job hunting and hiring processes; and much more.

However, in order for this technology to be able to advance, it needs to be able to learn.

AI systems rely on artificial neural networks (ANNs), which try to simulate the way the brain works in order to learn.

ANNs can be trained to recognise patterns in information - including speech, text data, or visual images - and are the basis for a large number of the developments in AI over recent years.

Conventional AI uses input to 'teach' an algorithm about a particular subject by feeding it massive amounts of information.  

Practical applications include Google's language translation services, Facebook's facial recognition software and Snapchat's image altering live filters.

The process of inputting this data can be extremely time consuming and is limited to one type of knowledge.

A new breed of ANNs called Adversarial Neural Networks pits the wits of two AI bots against each other, which allows them to learn from each other.

This approach is designed to speed up the process of learning, as well as refining the output created by AI systems.

If you want to learn artificial intelligence then you can enrol for one of the best resources i.e. Artificial Intelligence Course Technogeeks. This training is led by trainers who are AI experts. Besides, at the end of the training, they also awarded me an AI course completion certificate.

Which is Better for Job Opportunities, AWS or Azure?

 Amazon Web Services (AWS) is a cloud service platform from Amazon, which offers services in various domains such as compute, Object storage, content delivery and other functionality which helps the organizations to scale and grow their business.

Hence, AWS offering services are categorized into 3 types such as Infrastructure as a service (IaaS), software as a service (SaaS) and platform as a service (PaaS). Today it becomes the best cloud platform among currently available cloud platforms.

Azure is a cloud service platform by Microsoft, which offers services in various domains such as compute, storage, database, networking, developer tools and other functionality which help businesses to scale and grow.

Azure services are also broadly categorized as the platform as a service (PaaS), software as a service (SaaS) and Infrastructure as a service (IaaS). This can be used by developers and software employees to create, deploy and manage services and applications through the cloud and emerges as one of the biggest commercial cloud service providers.

There are several cloud computing technologies in the market. You will come across an overwhelming number of cloud service providers, hybrid solutions, products, services, features and more. There are two public cloud behemoths that have captured a majority of the market - Microsoft Azure and Amazon AWS. So, if you are planning to grow your career in public cloud computing, you need to choose between these two providers. Making a choice between these two providers is not an easy job! Both of these companies are leaders in their own rights, so none has an unprecedented edge over the other. It is important to understand both these technologies into detail, take into consideration multiple factors before making a decision. Both Amazon AWS and Microsoft Azure offer similar capabilities revolving around storage, security, and networking.

Amazon AWS is an already established leader in cloud computing, while Azure is catching up at a rapid pace. In terms of popularity, both AWS and Azure are competing on the same level. One core difference is that AWS operates more on an Infrastructure-as-a-Service (IaaS) level while Azure operates more on Platform-as-a-Service (PaaS) level.

It is not surprising when people say that the future is in cloud computing. Taking cloud computing at this stage is a great time to get prepared for this evolving industry. Businesses today are moving away from the traditional methods and approaches, so the role and demand of a cloud computing expert are going to rise in coming times. You can build a long and successful career and embrace the new opportunities arising in cloud computing. Both these cloud computing giants have their set of merits, you need to understand your skills and interests before choosing any one of them. It’s the best time for you to move your career in cloud computing and I would suggest you take a look at Technogeeks Cloud Computing programs by working IT professionals.

For More Information:

Call Now +91 8600998107

Address:- 3rd Floor, Plot No 7, Common Wealth Society, Opposite Aundh, Pune, Maharashtra- 411007

Email: contact@technogeekscs.co.in

How do I Prepare for Azure Certification?

There are specific examinations that candidates can take to get the certifications directly from Microsoft. These exams have moderate to a very high level of difficulty and require students to prepare thoroughly. Many of the certifications are based on specific roles and tasks related to Azure tools and cloud services. Some of the certifications include Azure Fundamentals, Microsoft Certified: Azure Administrator Associate (AZ:104), Microsoft Certified Azure Developer Associate (AZ:204), Microsoft Azure Architect Technologies (AZ:30), Microsoft Azure Architect Design (AZ-301) and more. Different exams check specific expertise related to cloud management and security for granting the certifications.

Every certification is meant to qualify you for different roles and has different prerequisites and associated exams. Though the preparation technique is similar for most certifications, some certifications require particular attention to certain topics and a modification in preparation techniques.

You can also look at the Microsoft Azure Documentation for getting the base concepts. Especially valuable for beginners, their page contains key insights in fundamental concepts like Agility, Elasticity, Fault Tolerance, High Availability, Scalability and other core features in any cloud network.

It also contains Azure-specific details like computation, security and storage in Azure. Furthermore, it provides a thorough grounding into creating an account in Azure, networking options, application storage, using PowerShell to automate Azure tasks and using the CLI for utilizing Azure services.

After brushing up on the exam fundamentals and acquiring the basics from Microsoft’s own documentation, it is beneficial to join some instructor-led course to obtain further expertise in the topics and for availing the mock-exams such courses usually provide.

Any standard course would provide a thorough grounding in the topics covered in the exam’s syllabus and would also prepare you better by hosting lab sessions where you can hone your practical skills. And I would recommend you to enrol in Technogeeks Microsoft Azure certification training program by working IT professionals.

Another key advantage of instructor-led training is the facility to set up on-demand classroom training. Here, you can arrange classroom training on your convenience such as a live class in person, attending live class remotely, asking questions to the instructor in person and many more.

The Microsoft Azure certification path can be an enriching journey and enable you to pursue a career in the cutting-edge world of the public cloud. With proper preparation and support and with the right instructors, clearing the exams should not be a challenge at all.

For More Information:

Address:- 3rd Floor, Plot No 7, Common Wealth Society, Opposite Aundh, Pune, Maharashtra- 411007

Email: contact@technogeekscs.co.in

Website:- https://technogeekscs.com/

Call Now +91 8600998107

Why is Python considered a good language for AI and Machine Learning?

Python has enjoyed a steady rise to fame over recent years and is now jostling for the position of one of the most popular programming languages in the world. Favoured for applications ranging from web development to scripting and process automation, Python is quickly becoming the top choice among developers for artificial intelligence (AI), machine learning, and deep learning projects.

The high-level, robust programming language focuses on rapid application development. The core functionality has helped Python become one of the fastest-growing programming languages. Here are top reasons why developers and companies working on emerging technologies including AI, and ML tend to choose Python.

Simple code structure:

Python focuses on reducing the number of lines of code that you write to execute a function. The language simplifies the code and makes it readable. The combination of Python and AI will help in reducing the size of code that developers write. The language, if used with AI and ML algorithms, will make it easier to reduce the quantity of code.

Vast Community:

Python is well-known for its extremely vast community. Since its release, the community is contributing largely and which is the reason a Python user never feels abandoned with sudden changes. The constant upgrade by the developer community support makes Python one of the most suitable languages for machine learning applications. Large organisations such as Google, Amazon, Facebook, etc. are using this language.

Frameworks & Libraries:

One thing the developers like most about Python is the abundance of open source libraries and frameworks. The language has a great number of machine learning libraries and some of the prominent libraries are such as TensorFlow, Pytorch, Matplotlib, SciKit Learn, etc. Python has a collection as well as code stack of various open-source repositories in almost every domain such as Django for integrating web applications, pandas for machine learning, SciPy for scientific computing, Librosa for audio, OpenCV for images, NumPy for text, etc. Other commonly used libraries for artificial intelligence and machine learning are such as pyDatalog (Logic Programming engine in Python), PyML (the bilateral framework is written in Python that focuses on SVMs and other kernels methods), EasyAI (Simple Python engine for two-players games with AI), PyBrain (simple and effective algorithm for ML tasks), AIMA (Python implementation of algorithms), simple, etc.

If you are interested in learning AI with Python then I would recommend you to enrol in Tehcnogeeks Artificial Intelligence with Python the program under a working IT professional with hands-on projects.