Why there is more scope for Data scientist?

Job Opportunities in Big Data. 

You can never get away from statistics in your life. Banks, private companies, government agencies, administrations and many other places need a lot of information and statistics. Of course it is a kind of data. Data needs to be stored and saved. So i. In T., the work on Big Data is in full swing. The process of improving business, making decisions and backing up information on competitors, storing statistics and other practical information, all fall into Big Data.

Income. T. Bigdata is a great option from a career point of view after engineering. This is because the demand for analytics professionals, data scientists is increasing in many multinational and Indian companies. Many tools have to be used while working in data analytics. For this you need to have his skills. You can work in data analytics using many tools like Table Public, OpenRefine, RapidMinor, Google Fusion Tables. Hadoop is an open source distributed Big Data framework that handles data processing, complex data management and its storage for large data applications running in clustered systems. It mainly involves information processing.

BigData's work also includes robotics technology, such as machine learning and Amazon webservices. Concepts such as Artificial Intelligence, Machine Learning have no alternative but big data for information management. And as you know, artificial intelligence technology is gaining momentum today, which is why the demand for BigData is growing.

So this is an open way to build a career in BigData. For this, it is necessary to acquire the skills of the above mentioned tools. Courses are also available at private institutes in cities like Pune, Chennai, Hyderabad, Bangalore. As soon as you become a BigData expert, you get a job as a BigData Analytics Consultant, Analytics Associate, Business Intelligence Analytics Consultant, while experienced people get a job as a BigData Analytics Architect, Metrics or Analytics Specialist. Companies are also offering good packages for this.

Growing job opportunities in 'Artificial intelligence'

 We must change ourselves with the world-changing day by day. The old curriculum should be put aside and new technology should be adopted. As you know, a lot of research is being done in the field of science and technology today. Artificial intelligence is also an area. ‘Artificial intelligence’ means artificial intelligence. Of course, artificial intelligence is the ability to make a computer, a mobile, or even a machine capable of planning and solving problems without any human intervention. The concept of artificial intelligence is more common in science fiction, which is why we see it in many movies today. Today, it has become an important part of the computer curriculum.

Artificial intelligence is another great way to build a career in computers and mechanics. Artificial intelligence has now become a distinct mainstream in the computer, science, and engineering disciplines. This technology is emphasizing to think like an intelligent human being using machine learning, process automation concepts. It is expected that careers in Artificial Intelligence will be at the center of new IT development in areas such as automation. This has created a kind of competition in Silicon Valley today and IT giants like Google, Facebook, LinkedIn, Microsoft, and many others are investing heavily in 'Careers in Artificial Intelligence. There is no doubt that this technology will flourish in the future.

After software engineering, there are many options to pursue a career in IT, including Artificial Intelligence. We have many options like Artificial Intelligence, Machine Learning, Process Automation, Cognitive Robotics. This includes working as a Robotics Automation Engineer, Algorithm Specialist, Research Scientist. Private companies, insurance companies, public institutions, education, arts, healthcare, government agencies, and the military e. The work that has to be done in all these services has to be done automatically by computer programming. Tasks like data entry, data manipulation have to be automated by robots. This is saving companies time, money, and manpower. That is why companies are giving priority to artificial intelligence. Robots are now doing jobs like data entry and that is why jobs like data operator, data entry analyst are in danger. So learning such old technology will not be of any use and it is also being eliminated from the curriculum, that is why we should always turn to new technology.

To pursue a career in Artificial Intelligence, you need to have a minimum engineering degree and a basic knowledge of a computer programming language, and you need to be firm about the logic, philosophical, and cognitive foundations for research. On this basis, there are interviews between multinational companies, it is mandatory to pass this interview. If you have experience in this field you get a good package. A few days ago t. C. Companies such as S., Cognizant have announced that they will pay double the salary of people with experience in Artificial Intelligence. It is clear that in the next few years, artificial intelligence will have a fundamental impact on the global labor market.

The training is available in India in private institutes in Pune, Hyderabad, Bangalore, Chennai, Noida for the students who have just graduated from the college. It has many tools like Ui-Path, Blue-Prism, Automation Anywhere to work. We can become proficient in bypassing this online exam. Some of these exams are charged while some online training is free. And then you get a job in a multinational company with a good package. Today the demand for Artificial Intelligence skills is increasing. Although a career in Artificial Intelligence is a fast and challenging field, it is creating a visible path in our daily lives which is why the demand for employees who are aware of this technology is increasing.

क्रांती तंत्रज्ञानाची - अपंगाना आधार कृत्रिम बुद्धिमत्तेचा !

अपंगाना आधार कृत्रिम बुद्धिमत्तेचा !

नव्या तंत्रज्ञानाचे फायदे व तोटे :

भविष्यात तंत्रज्ञानामुळे कोणते बदल होतील हे तंतोतंत सांगणे आज तसे कठीणच आहे. परंतु, आजवर जितके संशोधन झाले आहे, हे भविष्यतील संशोधनाचा विचार करता अगदी किरकोळ म्हणावे लागेल. आजचे संशोधन आधुनिक तंत्रज्ञानाच्या काळाला ज्या प्रकारे घेऊन जात आहे ते भविष्यात होणाऱ्या आमूलाग्र बदलांबद्दल विचार करायला भाग पाडत आहे.   पुढे वेगवेगळे आणि भरपूर असे तंत्रज्ञान समोर येत राहणार आहे किंबहुना त्याचे दुष्परिणामही असतील! परंतु त्यांचे फायदे विलक्षण असणार आहेत. त्यातला एक फायदा म्हणजे अपंग लोकांना आधार असेल आर्टिफिशिअल इंटीलिजन्सचा अर्थात कृत्रिम बुद्धिमत्तेचा!

कशी होईल मदत :

आर्टिफिशिअल इंटीलिजन्स आज स्वयंचलित मोटारगाडीपासून ते श्रवणयंत्रांपर्यंत अपंग व्यक्तींसाठी दारे उघडत आहे आणि येत्या काही वर्षांत, आर्टिफिशिअल इंटीलिजन्स अनेक नवीन प्रयत्नांसह आणि विस्तारित प्रवेशासह अनेक क्षेत्रात त्रुटींना दूर करत सुपरचार्ज करण्यास नवीन सुरवात करेल. २०११ च्या वर्ल्ड हेल्थ ऑर्गनायझेशनच्या अहवालानुसार, जगभरात एक अब्जहुन अधिक अपंग लोक आहेत. 

कृत्रिम बुद्धिमत्ता आणि अपंगांच्या बाबतीत, प्रत्येक बदल अधिक समावेशक असेल आणि हा बदल म्हणजे वैद्यकीय तंत्रज्ञानामध्ये मोठी प्रगती करेल आणि लोकांनी जी कल्पनाही केली नसेल अशा प्रकारे स्वातंत्र्य मिळविण्यात अधिक लोकांना मदत करेल. अलेक्झांडर ग्रॅहम बेलची आई बहिरे होती आणि दूरध्वनीचा त्यांचा शोध हा त्याच्या कर्णबधिर समुदायाबरोबरच झाला होता.

बाकीच्या तांत्रिक सुलभतेबरोबरच अपंग लोक हेही कृत्रिम बुद्धिमत्तेचे लक्ष आहे.  मायक्रोसॉफ्टची मुख्य प्रवेशिका अधिकारी जेनी-ली-फ्लोरी म्हणते, की "आम्ही अपंगासाठी खूप काही नाविन्यपूर्ण बदल घेऊन येत आहोत, तुम्हाला अपंग व्यक्ती कोणत्याही दृष्टिटकोनातून अपंग वाटणार नाहीत, या अनुषंगाने मायक्रोसॉफ्टचे काम चालू आहे".  जेनी-ली-फ्लोरी जी स्वतः बहिरी आहे . 

पुढे ती सांगते "दृष्टी, श्रवणक्षमता, मानसिक आरोग्य, शिक्षण, आकलन किंवा एखाद्याच्या गतिशीलतेशी संबंधित अपंगत्व कायमस्वरुपी, तात्पुरते किंवा परिस्थितीजन्य देखील असू शकते. तंत्रज्ञानाच्या प्रत्येकासाठी कार्य करते, हे सुनिश्चित करण्यासाठी अनेक स्तरांवर क्षमता असलेल्या नवीन उत्पादनांची रचना - सर्वसमावेशक डिझाइन करणे, अशी संकल्पना मायक्रोसॉफ्टकडे आहे."  मग हे उत्पादक वापरकर्ते मुके असतील, बहिरे असतील किंबहुना अंधही असतील. जेव्हा अपंग व्यक्ती अशी उपकरणे डोक्याला लावेन तेव्हा, तो जे विचार करेल ते प्रत्येक्षात आणण्यासाठी उपकरण वापरकर्त्याकडून मंजुरीही घेईन. उदा. एखाद्या हात नसलेल्या अपंग व्यक्तीला कॉम्पुटर बंद करायचा असल्यास त्याने केवळ विचार केला तरी कॉम्पुटर बंद होईल किंवा  अंध व्यक्तीच्या मेंदूला कॅमराकडून  उपकरणद्वारे दृश्य पाठवले जाईल. बहिऱ्या व्यक्तीच्या मेंदूला उपकरणद्वारे आवाजाचे सिग्नल्स पाठवले जातील.

"मेंदूला सिग्नल्स पोहोचणारे मज्जातंतू (न्यूरॉन्स ) जरी काम करत नसतील तरी डोक्याला ऍनोड-कॅथोड अशा इलेकट्रोडची उपकरणे लावून ही संकल्पना प्रत्येक्षात उतरवली जात आहे. मनुष्यच्या ज्ञानेंद्रियांकडून मेंदूकडे पाठवणारे सिग्नल्स कृत्रिमपद्धतीने पाठवणे शक्य आहे."

 

वॉशिंग्टन युनिव्हर्सिटीचे भारतीय  न्यूरोइंजिनेसर राजेश राव यांनी ब्रेन-कॉम्प्यूटर इंटरफेसचे काम केले आहे, जे यंत्र किंवा संगणकास कार्ये करण्यास सक्षम करण्यासाठी उपकरणे शोधू शकतील आणि मेंदूच्या सिग्नल्स देवाण-घेवा करण्यावर नजर ठेवू शकतील. व्हिडिओ गेम्स खेळण्यापासून अपंगाचे कृत्रिम हात नियंत्रित करू शकतील. मनुष्याचा मेंदू आणि इलेकट्रोनिक उपकरणे यांच्यात संपर्क होण्याच्या आणि प्रत्येक्षात आणलेल्या या संकल्पनेला  "मिस्ट्री ऑफ द ब्रेन" नाव दिले आहे.  काही दिवसापूर्वीच एका पत्रकारितेतील एका चिनी कंपनीने न्यूज एंकर म्हणून एक रोबोटही वापरला होता.  

राष्ट्रय आपत्ती आणि तंत्रज्ञानाची भमिका : 

आर्टिफिशिअल इंटीलिजन्स  अपंगत्व दूर करेल असे वाटत असले, तरी प्रत्येक राष्ट्रासमोर नैसर्गिक अपंगत्व दूर करण्याचे आवाहन असणारच आहे. जास्त तीव्रतेचे सिग्नल्स असल्यास त्याने कॅन्सरसारखे आजार उद्भवतात.  आजवर पाहिलेल्या तंत्रज्ञानाच्या दोन्ही बाजूंचा विचार करता याचा खर्च आणि समोर आलेले असे दुष्परिणाम याचेही आवाहन संशोधकांपुढे असणार आहे. आधुनिक तंत्रज्ञांचा वाढत जोर पाहता अशा  सर्व समस्यांवर मात करण्याचे संशॊधन होऊन अपंगांना आर्टिफिशिअल इंटीलिजन्सचा आधार असेल, हा विश्वास दृढ होत आहे.  

गणेश आटकळे

This article is published in Dainik Pudhari Newspaper in Sept 2019

What is Deep Learning ?

 What Is Deep Learning?

It's Nothing but AI and some kind of machine learning.

Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network. NLP has a major role in DL. (NLP - Natural language processing)

How Deep Learning Works?

Deep learning has evolved hand-in-hand with the digital era, which has brought about an explosion of data in all forms and from every region of the world. Deep Learning gives more accuracy and efficiency in AI.  This data, known simply as big data, is drawn from sources like social media, internet search engines, e-commerce platforms, and online cinemas, among others. This enormous amount of data is readily accessible and can be shared through fintech applications like cloud computing.

However, the data, which normally is unstructured, is so vast that it could take decades for humans to comprehend it and extract relevant information, to provide more efficiency. Companies realize the incredible potential that can result from unraveling this wealth of information and are increasingly adapting to AI systems for automated support.

Deep learning learns from vast amounts of unstructured data that could normally take humans decades to understand and process.

Deep Learning Versus Machine Learning

One of the most common AI techniques used for processing big data is machine learning, a self-adaptive algorithm that gets increasingly better analysis and patterns with experience or with newly added data.

In case, If a digital payments company wanted to detect the occurrence or potential for fraud in its system, it could employ machine learning tools for this purpose. The computational algorithm built into a computer model will process all transactions happening on the digital platform, find patterns in the data set and point out any anomaly detected by the pattern.

Deep learning, a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web. While traditional programs build analysis i.e. understanding with data in a linear way, the hierarchical function of deep learning systems enables machines to process data with a nonlinear approach.

A traditional approach to detecting fraud or money laundering might rely on the amount of transaction that ensues, while a deep learning nonlinear technique would include time, geographic location, IP address, type of retailer, and any other feature that is likely to point to fraudulent activity with more accuracy. The first layer of the neural network processes a raw data input like the amount of the transaction and passes it on to the next layer as output. The second layer processes the previous layer’s information by including additional information like the user's IP address and passes on its result.

The next layer takes the second layer’s information and includes raw data like geographic location & makes the machine’s pattern even better. This continues across all levels of the neuron network. DL is a very depth level of a machine learning concept. 

We will keep posting in upcoming blogs.. stay tuned with the site.

 

What is Machine Learning ?

Now a day, Robotics and Data science growing in the world and there are several sub-branches are come up like Big Data, Machine Learning, Cognitive intelligence, etc. In this article, we are going to speak about Machine Learning.


What is Machine Learning ? 

Machine learning is the deep scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without explicit instructions, relying on patterns and inference. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model of sample data, known as "training data", to make predictions or decisions without being explicitly programmed to perform the task. Without Math ML can not be completed, where it is infeasible to develop an algorithm of specific instructions for performing the task. The study of mathematical optimization delivers methods, theory, and application domains to the field of machine learning. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. Domain knowledge is the most important part in ML to get accuracy.

How machine learn itself?

The concept here is, Machine is learning itself from the experience. Machine learning can have many tasks, and they are classified into several broad categories. In supervised learning, the algorithm builds a mathematical model from a different set of data that contains both the inputs and the desired outputs. For example, if the task were determining whether an image contained a certain object, the training data for a supervised learning algorithm would include images with and without that object (the input), and each image would have a label (the output) designating whether it contained the object.n special cases, the input may be only partially available or restricted to special feedback. Semi-supervised learning algorithms develop mathematical models from incomplete training data, where a portion of the sample input doesn't have labels.

Classification algorithms and Regression algorithms are types of supervised learning.

• Classification algorithms are used when the outputs are restricted to a limited set of values. e.g. Email Filtration

• Regression algorithms are named for their continuous outputs, meaning they may have any value within a range. Examples of a continuous value are the temperature, length, or price of an object.

Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining (Not Data warehousing) uses many machine learning methods, but with different targets; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a pre-processing step to improve learner accuracy.

Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). 

Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a pre-processing step to improve learner accuracy.

Machine learning also has intimate ties to optimization: many learning problems are formulated as minimization of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples). The difference between the two fields arises from the goal of generalization: while optimization algorithms can minimize the loss on a training set, machine learning is concerned with minimizing the loss on unseen samples.

Machine learning (ML) and statistics are closely related fields. According to Michael I. Jordan, machine learning ideas have had a long pre-history in statistics from methodological principles to theoretical tools. He also suggested the term data science as a placeholder to call the overall field. Leo Breiman distinguished two statistical modeling paradigms: data model and algorithmic model, wherein "algorithmic model" means more or less the machine learning algorithms like Random forest. Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.