Firstly, Machine learning as well as artificial intelligence-based projects are obviously things that point out what the future holds. We want better personalization, smarter recommendations, as well as improved search functionality. Our apps should be able to see, hear, and respond – that’s what artificial intelligence (AI) has brought to the world, enhancing the user experience as well as creating value across many industries.

Particularly, Now you likely to face two major questions: How can I bring these experiences to life? and What programming language is used for AI and ML? You should consider using Python for AI and machine learning.
 

What makes Python the best programming language for both machine learning as well as AI applications?

Particularly, When the question is about AI, its projects differ from traditional software projects. Firstly, the differences lie in the technology stack that is being used, the skills that are required for an AI-based project, as well as the ability or the necessity of deep research. In order to implement your AI aspirations, you should consider making use of a programming language that is stable, flexible, as well as has all the tools available. Python offers all of this to the developers and programmers, which is why we see lots of Python AI projects today thus making it apt for the best fit.

From development to deployment as well as maintenance, Python helps developers and programmers be productive and confident about the software that they’re building and making. Benefits that make Python the best fit for machine learning and AI-based projects are simply abundant. These benefits include simplicity and consistency, access to great libraries and frameworks for AI and machine learning (ML), flexibility, platform independence, and a wide community. These are going to add to the overall popularity of the language.

Simple and consistent:

in Conclusion, Python offers concise and easily understandable and readable code. While there are complex algorithms and versatile workflows that stand behind machine learning and AI, Python’s simplicity and easy to understand code allows developers and programmers to write reliable systems. Developers are able to get together and put all their effort into solving an ML problem instead of focusing on the technical nuances of the language.

Additionally, Python is appealing to many programmers and developers as it’s easy to learn. Python code is understandable by humans just as plain English, which makes it easier to build models for machine learning.

Many programmers say that Python is going to be much more intuitive than other programming languages. Others point out the many frameworks, libraries, as well as extensions that simplify the implementation of different Python-based functionalities. It’s generally accepted and acknowledged that Python is suitable for collaborative implementation. When the need stand to use multiple developers, python support comes first. Since Python is a general-purpose language that can be used for almost anything, it can do a set of complex machine learning tasks and enable developers and programmers to build prototypes quickly. This in turn allows you to test your product for machine learning purposes.
 

An extensive selection of libraries and frameworks:

Implementing AI and ML algorithms can be sometimes or at times be tricky and require a lot of time. It’s vital to have a well-structured as well as a compatible and well-tested environment to enable developers to come up with the best coding solutions.

In order to reduce development time, programmers turn to a number of Python frameworks and libraries. A software library is nothing but a pre-written code that developers and programmers use to solve common programming tasks. Python, with its rich technology stack, has an extensive set of libraries that is used for artificial intelligence and machine learning. Here are some of them:

• • Keras, TensorFlow, and Scikit-learn for machine learning
  • NumPy for high-performance scientific computing as well as for data analysis
  • SciPy  used for advanced computing
  • Pandas for general-purpose data analysis
  • Seaborn for data visualization

Scikit-learn features have been including various classification, regression, and clustering algorithms, these include support vector machines, random forests, gradient boosting, k-means, and also DBSCAN. These features are designed to work with the Python numerical and scientific libraries NumPy and SciPy.

With these solutions, you can develop and create your product faster. Your development team won’t have to reinvent the wheel. You can make use an existing library to implement necessary features.

Also Read: How AI and ML have Revamped Mobile App Development Industry

What is Python good for?- Python for Artificial intelligence and Machine Learning

Here’s a table of сommon AI use cases and technologies that are best suited for them. We recommend using these:

Platform independence:

Platform independence has always referred to a programming language or framework that allows developers and programmers to implement things on one machine and use them on another machine without any (or with only minimal) changes. One key to Python’s popularity is that it is a platform-independent language. Python provides and enables supports for many platforms including Linux, Windows, and macOS.

What’s more, developers and programmers usually use services such as Google or Amazon for their computing needs. However, you can often find companies and data scientists who use their own machines that has powerful Graphics Processing Units (GPUs) in order to train their ML models. And the fact that Python has always been platform independent, this makes training a lot cheaper and easier.
 

Great community and popularity:

In the Developer Survey 2018 by Stack Overflow, Python was among the top 10 most popular programming languages, which ultimately means that you can find a development company with the necessary skill set to build your AI-based project.

Recommended Read: Difference Between Artificial Intelligence And Machine Learning

Conclusion

So you see these are the major reasons why Python is the most preferred language as far as AI and ML are concerned. Pythons increasing popularity is key to using it for different ML and AI applications. Hope you like this blog on Why Use Python for Artificial intelligence and Machine Learning? If you are looking to Hire Python Developer then do mail us on enquiry@nimapinfotech.com

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Artificial Intelligence has now made a tremendous impact on society. It was once a notion that was confined to only Sci-Fi Movies and research papers. Now has slowly but steadily made its entrance into our society.

Today there have been numerous applications that run on Artificial Intelligence. These applications are being used to make our lives easier and more comfortable. From Apple’s Siri to Google’s Deep Mind. Consider Siri for our example, Siri uses NLP or Natural Language Processing in order to interpret voice commands and being able to respond accordingly.

Google’s Deep Mind, on the other hand, uses Deep Learning. Deep Mind is able to make connections as well as reach meanings without relying on the pretext of predefined behavioral algorithms, instead, it learns from raw experiences using data as a medium of its inputs. In fact, by implementing findings from Deep Minds Google was able to improve the efficiency of its own data centres thus reducing the energy required for adequate cooling by 40%.

In the business sector, Artificial Intelligence is enabling businesses for working smarter and more efficient and faster thus enabling it to do more using significantly fewer data. As the technology, as well as the society, continues to advance, more companies, as well as organizations, are looking for much more powerful and sophisticated solutions that is going to improve and streamline the operations procedure.

Thus it is important for us to appreciate that artificial intelligence is nothing but an umbrella under which numerous technologies can reside. Deep learning, Machine learning, computer vision, robotics, artificial general intelligence, cognitive computing, natural language processing as well as knowledge and reasoning are just some of the main branches that have streamlined from Artificial Intelligence.

The current state of Artificial Intelligence

However, many of the applications of AI that we are able to see today are considered to be weak AI. This is because we have yet to realize their true potential. Weak AI, that is also known as Narrow AI is a non-sentient AI that is able to provide focus on one task alone. The applications of AI that are made available need to be though or in other words directed to be able to provide insights to a user’s needs.

Strong AI, on the other hand, refers to AI applications that are able to formulate human decisions without using any human input, apply intelligence to multiple problems and functions and be able to behave much more like a human. We are quite some way from strong AI

Yet despite current AI solutions not being true AI, the benefits, as well as the capabilities that they provide to us, are extraordinary, and many industries have already started incorporating some or the other form of AI in their day to day processes and tasks.

Current Industries are applying AI to their day to day operations
In some industries, AI is capable of providing automation for business intelligence and analytics processes and is able to provide a holistic view and end-to-end solution. In others, computer vision is being used to map as well as navigate terrain, and contribute to the development of smart driving cars that are able to learn to drive as humans do. Here are few of the examples of how AI is being used in different industries:

Application Of Artificial Intelligence

Banking and Finance:

Many banks use various types of applications of AI in order to detect fraudulent transactions or activities. The AI software is given a very large sample of data that includes fraudulent and non-fraudulent purchases and is trained in order to determine whether a transaction is valid based on the data. Over time the software is able to become increasingly adept at being able to spot fraudulent transactions based on what it had learned previously.

Retail-Online Customer Support:

Many websites are now being able to offer some or the other form of chat functionality where you are able to talk to customer service representatives or sales representatives. In many instances, it is just some form of automated AI script that begins these conversations. These AI chatbots are capable of understanding the natural language that is human conversations and can readily assist customers in finding out what they need to know as well as extracting information from the website, or directing them to the appropriate page or persons for further support.

You may also like to know more: Difference Between Artificial Intelligence And Machine Learning

Security:

As cyberattacks are increasing in frequency, more sophisticated tools are used to beach cyber defense mechanisms.  Human operators are no longer being enough. Top organizations across the world are investing heavily into cybersecurity.  This is to be able to ensure that their confidential data is protected. Real-time threat detection, mitigation, and prevention is ideally what is needed for businesses. And AI can deliver these results.

Using machine learning algorithms and feeding those algorithms with great quantities of data. IT and security experts can be able to teach the AI solution to be able to monitor behavior, and detect anomalies. This is to adapt and respond to threats and issue alerts. AI has risen and quickly become a key component in business security infrastructure. It is able to provide multilayer security and strategy that is robust and sophisticated. AI is going to be used heavily to help man defend itself from cyber attacks as well as predict attacks before they occur. It is no doubt that AI is proving to be beneficial to man.

Preparation for integrating AI solutions across departments

Organizations that are able to respond rapidly to opportunities in AI will have an advantage. This advantage is in the landscape of the future. But because AI is evolving rapidly.  The challenge is to ensure that the business has the necessary strategies and plans to support AI capabilities. These capabilities are available to the masses, and the right AI technical infrastructure is produced to support it. For many businesses, it is not a question but it is rather a target when to adopt AI. On that basis monitoring and development of AI technology and planning far in advance is essential to adopt AI successfully.

The optimum strategy is to be able to observe, learn as well as experiment with the current implementation of AI. Investing heavily in AI technology can turn out to be ineffective. This can be damaging to the business’s adoption and utilization of future based AI solutions. Instead of trying to determine how your business can benefit from AI solutions. How you can build it to drive efficiency into core business processes. Start with the outcome that you want to achieve in order to modernize your environment. Remember AI would not necessarily replace human operators anytime soon. But it will enable and empower organizations to do much, much more. WIth the integration of AI solutions, it is going to help mankind to simplify tasks and jobs.

Recommended reading: PHP vs Python

Conclusion:

Artificial Intelligence is the next big thing and is going to take the world by storm. Organizations can leverage the use of AI for bettering its processes and to increase productivity. AI is not going to replace humans in jobs but is going to help us increase our efficiency level. We hope that you liked this article and have enjoyed reading it. If you like to undergo an artificial intelligence course in mumbai then do contact us at enquiry@nimapinfotech.com. You can also hire python developers for the implementation of artificial intelligence work in your projects. Artificial intelligence is developed and is continuing to be developed to augment and amplify our lives. This goes without saying that AI is helping human beings. This is to carry and process work resulting in efficient and simplified processes that helps humans. AI is really helping human being to really get on with their lives.

So this was Application Of Artificial Intelligence hope you like.

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Overview

Firstly, Artificial Intelligence and Machine Learning are commonly used technologies. Companies use them interchangeably. Particularly, They are not the same thing but the perception that they look similar can often lead to confusion among these terms.  Secondly, Both these terms come up very frequently when the topic is Big data, analytics and when the broader waves of technological change that is sweeping the world.

AI and ML have led to the birth of a plethora of different kinds of applications as well as technologies. These technologies have long helped mankind to implement and use their decision-making ability and creative thinking in its applications. Man has never been helped in such situations. New inventions and capabilities have identified and distinguished due to AI and ML. Many instances are there where a real-time problem is solved due to the implementation of AI and ML. These technologies go hand in hand to solve and provide solutions to the most intricate problems that occur to man.

Let us get to the basics of Artificial Intelligence(AI) and Machine Learning(ML):

In conclusion, Artificial Intelligence is the broader concept of machines that are capable enough of carrying out tasks in a way that we consider as “Smart”.

Particularly, Machine learning is the current application of AI that is based around the idea that we should provide machines access to data and letting them learn for themselves.

Inception

Firstly, Artificial Intelligence has been around for quite a long time, the Greek myths have contains many stories of mechanical men designed to mimic human behavior. European computers that were invented in the olden days. These were used as logical machines by reproducing the capabilities such as basic arithmetic and memory. Engineers saw their Job fundamentally as attempting to create mechanical brains.

As the technologies evolved, our understanding of how our minds work has progressed, our concept of what constitutes AI has changed. Instead of increasing complex calculations, the main work in the field of AI constitutes mimicking human behavior.  Also mimicking decision making capability and carrying out tasks in human ways.

Direct Comparison- Artificial Intelligence vs Machine Learning

Artificial Intelligence is nothing but imparting human intelligence and behavior to a machine that allows them to act in a smart way. Many games and modern robots interact and function using Artificial intelligence. There are two types of AI.  These are Applied AI and General AI. Applied AI is far more common. This AI is designed to smartly and intelligently trade stocks and shares or imparting the capability to manoeuvre an autonomous vehicle is something that would fall in this category

Read More: Technical Approach To Artificial Intelligence For Beginners

Generalized AI is something that imparts machines the capability to handle any task in common, but this is where some of the most exciting advancements are happening today. This is the area that has to lead to the development of Machine Learning. This is referred to as a subset of AI. It is really more accurate to think of it as the current state-of-the-art.

Let us dive deep into what are the subtle differences between AI and ML:

Artificial Intelligence vs Machine Learning

Know More: Artificial Intelligence on smartphones – why it’s important

Conclusion:

So you see that there is a vast difference between Artificial Intelligence and Machine Learning. We hope that you have found this article useful and have gained some knowledge about AI vs ML topic. If you’re looking to take up artificial intelligence and machine learning course in Mumbai then do contact us. We have highly experienced professionals to guide you in your AI and ML venture. Partnering with us will provide you the advantage to be the best in your industry and solve real-time user problems and provide efficient and effective solutions.

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Artificial Intelligence has given numerous advantages in this computer age. In this article, we will see what is artificial intelligence and what drives it. How it will impact the digital revolution ahead.

What is Artificial Intelligence?

Artificial Intelligence is the ability given to any machine to perform cognitive functions such as perceiving, learning, solving problems as well as reasoning. These machines are said to have AI built into them.

Artificial Intelligence exists when a machine is said to have the cognitive ability. The proposed benchmark for a machine to have AI is the human level of reasoning, speech as well as vision.

Introduction to Artificial Intelligence Levels:

There are three basic levels for Artificial Intelligence:

1) Narrow AI: Artificial intelligence is said to be narrow when the machine is able to perform a particular task better than a human being. The current research of Artificial Intelligence is here now.

2) General AI: Artificial Intelligence reaches the general state when the machine is able to perform an intellectual task with the same accuracy level as a human would.

3) Strong AI: Artificial Intelligence is said to be strong when a machine can beat humans in many tasks.

Nowadays Artificial Intelligence is being used in almost all industries and has been giving a technological advantage and edge to all the companies integrating AI at a scale. According to McKinsey, AI has the avid potential to create 600 billion dollars in value for the retail sector, bring 50% more incremental value in banking as compared with other analytical techniques. In the transportation and logistics sector, the potential revenue jump is 89% more.

Subsequently, if an organization uses AI for its marketing activities, it can very well automate many of the mundane as well as repetitive tasks, allowing sales representatives to focus on tasks like relationship building, lead nurturing, and many more such tasks. An organization named Gong provides a conversation intelligence service. Each time a sales representative makes a phone call the machine records transcribes and analyses the chat. The VP can use AI analytics and recommendations in order to formulate a winning strategy.

In short, AI provides a cutting edge technology to deal with complex data which can be impossible to handle by a human being. AI automates redundant jobs and allows a worker to focus on the high level, value-added tasks. When an organization implements AI on a scale, it leads to cost reduction and revenue increase.

History related to Artificial Intelligence

Artificial Intelligence has become a buzzword today, this term is not new. In 1956, experts from different backgrounds decided to organize a summer research project on AI. Four bright minds lead the project John McCarthy (Dartmouth College), Marvin Minsky (Harvard University), Nathaniel Rochester (IBM), and Claude Shannon (Bell Telephone Laboratories).

The primary purpose for this research project was to tackle and use “every aspect of learning or any other feature of intelligence that can in principle be so precisely described that a computer machine can be made to simulate it.”

The proposals of the summit included

1. Automated computers
2. How a computer can be programmed to use a language?
3. Neuron nets
4. Self-Improvement

It led to the birth of the idea that intelligent computers can be created. A new era began, that was full of hope- Artificial Intelligence.

Know More: Artificial Intelligence on smartphones – why it’s important

Types of Artificial Intelligence

Artificial Intelligence can be divided into 3 sub fields.

• Artificial Intelligence
• Machine Learning
• Deep Learning

Machine Learning

Machine Learning is nothing but an art of study of algorithms that is able to learn from experiences and examples.

Machine Learning is based completely on the idea that there are some patterns that exist in the data that were identified and used for future predictions.

The difference shown in hard-coding rules is that the machine learns on its own to find such rules.

Deep Learning

Deep Learning is a subfield of Machine Learning. Deep learning does not mean that the machine learns from more in-depth knowledge. It means that the machine uses different layers to learn from the data. The depth of the model is represented by the number of layers in order to learn from the data. For example, Google LeNet model for image recognition counts 22 layers.

In deep learning, the learning phase is conducted by using a neural network. A neural network is nothing but an architecture where the layers are stacked on top of each other.

AI vs Machine Learning

Most of our smartphones, even daily use devices or even the Internet uses Artificial Intelligence.

Very often, AI and Machine Learning can be used interchangeably by big organizations that want to announce their latest innovations. However, Machine Learning and AI are different in many deemed ways

AI is the science of training and developing machines to perform human tasks. The term was invented in the 1950s when the scientists began exploring how computers could solve problems on their own.

Artificial Intelligence is nothing but a computer that is given human-like properties. Take our brain, for example, it works tirelessly and seamlessly to calculate the world around us. Artificial Intelligence is the concept that a computer can mimic human behaviour. It is said that AI is the largest science that is able to mimic human aptitudes.

Machine Learning is a different subset of AI that trains a machine on how to learn. A machine learning model looks for patterns in the data and tries to conclude. In a nutshell, the machine does not need to be explicitly programmed by the people. The computer is given some examples and the computer learns what to do from those samples.

Read More: AI and Android App Development

AI has broad applications-

•     Artificial intelligence is used to reduce or avoid the repetitive task. For instance, AI can repeat a task continuously, and tirelessly. In fact, AI never rests, and it is indifferent to the task to carry out
•     Artificial intelligence improves an existing product. Before the age of machine learning, core products were building upon hard-code rule. Firms have introduced artificial intelligence in order to enhance the functionality of the product instead of starting from scratch to design new products. You can think of a Facebook image. A few years ago, you needed to tag your friends manually in the photo. Nowadays, with the help of AI, Facebook gives you a friend’s recommendation.

Conclusion:

AI will continue to dominate the world helping humans to solve the day to day challenging tasks with ease. AI is developed and is continuing to be developed for the sole purpose of augmenting our lives and amplifying our productivity and tasks multiple folds.

We hope you understood the sole purpose of Artificial intelligence and why it is being used to help human lives achieve their goals. If you want to hire Artificial intelligence Developer you can contact Nimap Infotech by sending an email at enquiry@nimapinfotech.com

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A developer is an individual that builds and creates software applications. He or she writes, debugs, and executes the source code of a software application. A developer is also the key individual behind all software applications. Also, they are the creative minds behind the computer program. Although the developer’s primary job is writing code, a developer also may gather requirements for software, design or overall software architecture, software documentation and other related software development processes. Hence, the developer has a better future due to these qualities and these are as follows:

8 technologies developers should explore now

1) Internet of things security

After ten million connected devices were taken over last year, even causal observes could see that unprotected Internet of things devices create distributing security problems. Demand is high for engineers with the Internet of things security skills, especially those who understand the powerlessness of the hardware and software used by net-connected devices. The attack vectors on the Internet of things are largely similar to those of any other distributed network, like computers or cellphones, so the same security knowledge is relevant and critical.

2) Artificial Intelligence

Artificial Intelligence is the simulation of human intelligence processes by machines, especially computer systems artificial Intelligence can be categorized as weak and strong, that is designed and trained for a particular task. Because hardware, software and staffing costs for Artificial Intelligence be expensive.

3) Machine Learning

A Form of Artificial Intelligence, machine learning can take a massive amount of data to very quickly find patterns- like facial recognition- and solve the problem, without being explicitly programmed to do so. Also, machine learning focuses on the development of a computer program that can access data and use it to learn for themselves. The primary aim is to allow the computer to learn automatically without human intervention or assistance and adjust accordingly. Machine learning is more pervasive than we think: there are numerous real-world applications- self-driving cars, speech and image recognition, text classification, web search, smart robots, etc.

You may also want to know: 4 Ways Hiring Android Developers Offshore Increase Productivity

4) Data Science

Mining large amounts of structured and unstructured data to identify patterns can help an organization. Requirements can include experiments with machine learning and Artificial Intelligence to take large amounts of data and shape it in a form that can be used to make business decisions.” Skilled data scientists are in short supply, period ” says Speeding. ”Especially, I see areas where technology can be designed ‘ assist ‘ decisions, such as cognitive bots and guided analytics, to be high value-added opportunity areas.

5) Blockchain

Blockchain is a distributed ledger, which simply means that a ledger is spread across the network among all peers in the network, and each peer holds a copy of the complete ledger. Blockchain works with Blocks, whereas spreadsheets work with ” rows” and ” columns”. A blockchain is a database most famously implemented as one of several parts required for the Bitcoin jigsaw puzzle to the work. Anyone who has been frequenting the Zipmex Indonesia website should be well aware of how important Blockchain is to cryptocurrencies like Bitcoin. Blockchain can help to track the amount of bitcoin every individual owns. This could give those that buy bitcoin from websites similar to bitcoin.com.au some more security. This is done through the use of a ledger that tracks all bitcoin transactions. Knowing how much bitcoin you have is useful, especially because of the added benefit of being able to purchase items from websites such as Microsoft and Amazon with your bitcoins. You might want to take a look at this website to see where else your online currency could be spent.

6) Mesh app and service architecture

The demand for Apps that seamlessly stay connected as we move through our home, commute, and work are increasingly in demand. The purpose of the mesh app network or app is that it will be high availability – everything connected to everything. We have seen this being used for examples with the Tile tracker devices, which has created a community of tracking devices, which has created a community of tracking devices, and bitcoins being a distributed ledger.

7) Digital Twins

Prepare to Fail- Software models tied to physical and virtual sensors can help predict product or service failures so that the organization is able to plan. Internet of things and machine learning systems allow the organization to ensure that is an asset isn’t randomly failing, and if they do fail, then the organization can optimize.

Read More: Which language is better for creating websites, HTML or HTML5?(HTML VS HTML5)

8) Autonomous vehicles, Robots, and appliances

New opportunities are seen developing as Artificial Intelligence and machine learning to smarten up a home device, industrial equipment, cars, and drones. Research firm Gartner estimates that by 2020, the auto maker will send 61 million data connected cars off production lines.

Conclusion

I’II will be considering these trends and more than I look into the future of programming. If you are seeing any to Hire mobile app developer. Then do contact us or drop a message. We have the most experienced backend team ready to develop and deploy your website.

If you’re looking to get yourself trained for a full-fledged iOS app development course, then contact us We have expert trainers to help you with your mobile app development journey. You want to learn iOS app development or android app development it’s your choice. Looking for mobile app development training, well your search ends here. We have world-class trainers available to guide you for the Android course in Mumbai. We have the best iOS training company faculty ready to guide you in your endeavors. Learn from the best experts in the industry. Contact us now.

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What is NLP?

NLP stands for natural language processing. It is the field of computer science and artificial intelligence that deals with the communication between a human and a machine. NLP helps machines to interpret the human language and extract the intent, context, and meaning of the message and come up with proper response based on analysis that is similar to a human being. Over a period of training, it can be used to personalize and enhance the user experience.
 

What is chatbot?

It is a program built using artificial intelligence that responds to the user using text or sound. Chatbots are generally used in customer support, seat reservation, product ordering and making appointments. The famous example of chatbots is Alexa, Siri, Google assistant. Chatbots provide 24×7 assistance to the user. Today almost every company has a chatbot to engage with its users. Find out more about chatbots – read what is a chatbot.
 

How NLP works?

The first step in NLP is tokenization. Tokenization mean splitting sentences into words. We can just split the words where space occurs. For the example purposes, consider the following sentence “Delhi is the capital of India”. After tokenization, we get a set of words {“Delhi”, “is”, “the”, “capital”, “of”, “India”}

The next step is identifying the parts of speech of each token. For example, “Delhi” and “India” are proper nouns. Thus, the knowledge gained from this step is that Delhi and India are proper nouns so the sentence must be talking about them.

Next step is to identify the stop words. Stop word is nothing but a list of words that frequently occurs in a sentence. So NLP filters out these stop words. In the given above example “is”, “the”, “of” is primarily the stop words. Stop words can be identified by just by checking a hard-coded list of the available stop words.

The following step includes dependency parsing where the NLP determines how all the words are related to each other. Next step is NER (Named Entity Recognition) which detects and label these nouns with the real-world concepts that they represent. For example “Delhi” and “India” represents the location.

These above steps are just a short summary of how NPL extracts information from messages and uses it for further analysis.
 

You may also want to know more: Artificial Intelligence

Why NLP is required in the chatbot?

The main reason why chatbots are developed is to provide human-like assistance to the user and help him to achieve his/her goal. NLP helps the chatbot to understand the user’s message and respond appropriately. For example, if you send a message “Hi”, it is the NLP that lets the chatbot understand, that the message is a standard greeting. Thus using AI functionality the chatbot will respond with a return greeting.

Many chatbots are of little or no value without NLP. Only with NLP the chatbots understand messages and respond appropriately. The system is powerful enough to make or break a chatbot’s success as well as make it popular. With NLP chatbots can connect to queries and identify entities as well as the sentiments to generate own intelligent responses. Without NLP a chatbot cannot meaningfully understand the context of the message. The message is just a text input for a chatbot. It’s the natural language processing that helps to extract the context and meaning of the message.

NLP empowers bots with personality. It enhances the functions of chatbots for all businesses. It elevates their scripted and sequential conversations of chatbots to that of humans. Assessing, analyzing and communicating skills of NLP based chatbots makes this possible.
 

Read More: Creating Chatbot with Deep Learning

 
NLP understands and enables the needs of the customer with better clarity as well as the accuracy. It gains importance in the business world in order to provide better user experience across applications like chatbots, speech recognition, social media listening & texting, voice-enabled applications and devices. NLP with its translational and other features are the need of the hour. NLP based chatbots improve customer experience. You can fulfil the Expectations of today’s millennial business people around the globe with NLP’s business intelligence.

Want to build your own business app with cool development hacks? We at Nimap Infotech have experienced Python developers to take your business one step closer to your goals. Hire Python developers to experience the best full-stack python web development services. Contact us to offer you the most optimized solutions for web and mobile technologies.

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In simple words, Artificial Intelligence is to make machines do what the human brain does for us. Learning, reasoning & correction. Quite simple, no?

It is said that a person learns from his mistakes. Everything around us that we see, smell, and hear is kind of data that our brain takes as an input and then learns something out of it. The process keeps on going until eternity, There’s no end to a learning process. We learn, we make mistakes and then we improve our decision making.

That’s what most AI experts are trying to achieve. When someone says AI, Robots are the first thing that comes in our mind, But that is not what AI is all about. The concept is broader than we think. 

If you are someone like me who is interested in learning Artificial Intelligence, then I am going to talk here about how you can approach learning AI.

How can you approach to learn Artificial Intelligence?

During recent time AI and ML field have evolved rapidly. Experts are daily working on new technologies to make the process simpler for you. 

Today it is easy to find packages and libraries that you can work with while developing your own AI.

Understanding the basics

https://www.youtube.com/watch?v=cKxRvEZd3Mw&list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal 

To get into any field, it is necessary to understand what the field is about and how things are going to work. Above given link is to understand the basic concepts of machine learning and how things are actually going to work. Being a subfield of Artificial intelligence it is strongly recommended to get hands-on many of algorithms of machine learning.

Read More: Creating Chatbot with Deep Learning

Selecting a problem

As you go along with learning, You will find so many things like different algorithms, process and what not. As you explore more, things start getting more complicated. Hence it is necessary to have a predefined goal which will make it easier to select the options that are best for your problem. (It is also necessary to have prior knowledge about various different methods to solve the problem, at least introductory part.) Having a problem also engages you more into implementation and practicals rather than just reading. 

A common problem that most people face when starting is that they keep on collecting the information only while not understanding how the implementation is done for a real-world problem. 

Sometimes it is hard for us to come up with a problem to solve if you do not have prior experience with the domain. Sites like Kaggle ( https://www.kaggle.com/ ) provides a lot of problems and datasets to practice. 

Making your own solution.

Once you have a goal in your mind, you can start implementing various methods to achieve your goal. It is not necessary to come up with the perfect solution at the very first try. Hence start making a simple solution to the end while exploring various methods to approach a problem. 

However, a problem most people face in this process is that while selecting the method to solve the problem you spend much of your time with methods that you are already aware of and don’t move towards other methods. In such cases, cloud services like IBM Watson – Knowledge Discovery can help you a lot. By simply uploading your data into Watson, It can give insights about your data that you have not already thought about. 

(https://www.ibm.com/cloud/watson-discovery) 

Collecting data, cleaning data, training data and finding results from the data are the things that you can do while making your own solution.

Evaluating and Improving your solution.

Once you get to the solution, evaluate the results and check how accurate is your solution. As I mentioned earlier, It is not necessary to get the perfect solution at the very first try. Once you finish with the first phase of your solution you will start understanding difficulties that came in every step and flow of your entire project.

Try to Improve your solution.

Share your solution

In every project what you have done till the end can’t be always right. There can be so many things you might have done wrong. Hence you should always share what you have done with others and get their opinion about how you can improve our solution and what mistakes can be avoided. Kaggle is the best site to share your solution.

Extras –

As it is the most evolving technology in the market right now it is not just limited to computer technology but is also equally used in every sector like health, education, marketing, entertainment and so on. When it comes to AI there are many key terms used along with it such as Machine Learning, Cognitive Analytics, and Robotics.

Machine Learning carries the ability to learn from its experiences. ML an application of AI mostly deals with the thinking part of AI. Its analytics analyze data to provide an obvious output.

Here are some of the techniques that can be helpful for you for building and implementing Artificial Intelligence –

• (Artificial) Neural Networks
• Natural Language Processing
• Heuristics
• Vector machines
• These are the commonly used techniques in AI. 

All these techniques are boarder term which can’t be explained in one line.

If you are really interested in learning AI, Here are the following sources you can follow –

Siraj Raval : https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A 

Edureka : https://www.youtube.com/watch?v=JMUxmLyrhSk&t=16240s 

Following links are for beginners who want to get into AI. They can learn the basics from Edureka’s video. Siraj Raval is the best guy who can keep you updated with new information in AI and also keeps you motivated. Along with it, I strongly recommend you to follow machine learning and AI forums to connect with the community. 

Hope the above information about how to start with AI appears useful. And beginners find it easy indulging learning AI without facing any serious issues.