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Why is Data Literacy Important to Stay Relevant in Today’s Workspace?

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Why Is Data Literacy Important To Stay Relevant In Today's Workspace?

Today’s workspace has turned volatile in trying to adjust to the new normal. Along with struggling to stay indoors while living a virtual life, adopting new manners of social distancing, people are also having to deal with issues like job loss, pay cut, or, worse, lack of vacancies. Different sectors are getting hit, except for those driven by cutting edge technology like Data Science, Artificial intelligence. The need to transition into a digital world is greater than ever. As per the World Economic Forum, there would be a greater push towards “digitization” as well as “automation”. This signifies the need for professionals with a background in Data Science, Artificial Intelligence in the future that is going to be entirely data-reliant.

So, what are you going to do? Sit back and wait till the storm passes over or are you going to utilize this downtime to upskill yourself with a Data Science course?  With the PM stressing on how the “skill, re-skill and upskill” being the need of the hour,  you can hardly afford to lose more time. Since Data Science is one of the comparatively steadier fields, that is growing despite all odds, it is time to acquire data literacy to stay relevant in a workspace that is increasingly becoming data-driven. From healthcare to manufacturing, different sectors are busy decoding the data in hand to go digital in a pandemic ridden world, and employers are looking for people who are willing to push the envelope harder to remain relevant.

What is data literacy?

Before progressing, you must understand what data literacy even means. Data literacy basically refers to having an in-depth knowledge of data that helps the employees work with data to derive actionable information from it and channelizing that to make informed decisions. However, data literacy has a wider meaning and it is not limited to the data team comprising data scientists, no, it takes all the employees in its ambit, so, that the data flow throughout the organization is seamless. Without there being employees who know their way around data, an organization can never realize its dream of initiating a data-driven culture. Having a background in Data science using Python training is the key to achieving data literacy.

The demand for data scientists and data analysts is soaring up

Despite the ominous presence of the pandemic, the demand for Data Science professionals is there and in August, the demand for Data Analysts and Data Scientists soared. As per a recent study, in India, a Data Science professional can expect no less than ₹9.5 lakh per annum. With prestigious institutes like Infosys, IBM India, Cognizant Technology Solutions, Accenture hiring, it is now absolutely mandatory to undergo Data Science training to grab the job opportunities.

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Getting Data Science certification can help you close the gap

The skill gap is there, but, that does not mean it could not be taken care of. On the contrary, it is absolutely possible and imperative that you take the necessary step of upskilling yourself to be ready for the Data Science field. Having a working knowledge of data is not enough, you must be familiar with the latest Data Science tools, must possess the knowledge to work with different models, must be familiar with data extraction, data manipulation. All of these skills and more, you would need to master before you go seeking a well-paying job.

Self-study might seem like a tempting idea, but, it is not a practical solution, if you want to be industry-ready then you must know what the industry is expecting from a Data Science professional, and only a faculty comprising industry experts can give you that knowledge while guiding you through a well designed Python for data science training course.

An institute such as DexLab Analytics understands the need of the hour and has a great team of industry professionals and experts to help aspiring Data Scientists and Data Analysts fulfill their dream. Along with offering state-of-the-art Data Science certification courses, they also provide courses like Machine Learning Using Python.

No matter which way you look, upskilling is the need of the hour as the world is busy embracing the power of Data Science. Stop procrastinating and get ready for the future.


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What Is The Role Of Big Data In The Pharmaceutical Industry?

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What Is The Role Of Big Data In The Pharmaceutical Industry?

Big data is currently trending in almost all sectors as now the awareness of the hidden potential of data is on the rise. The pharmaceutical industry is a warehouse of valuable data that is constantly piling up for years and which if processed could unlock information that holds the key to the next level of innovation and help the industry save a significant amount of money in the process as well. Be it making the clinical trial process more efficient or, ensuring the safety of the patients, big data holds the clue to every issue bothering the industry. The industry has a big need for professionals who have Data science using Python training, because only they can handle the massive amount of data and channelize the information to steer the industry in the right direction.

We are here taking a look at different ways data is influencing the pharmaceutical industry.

Efficient clinical-trial procedure

Clinical trial holds so much importance as the effectiveness of a drug or, a procedure on a select group of patients is tested. The process involves many stages of testing and it could be time-consuming and not to mention the high level of risk factors involved in the process. The trials often go through delays that result in money loss and there is risk involved too as side effects of a specific drug or a component can be life-threatening. However, big data can help in so many ways here, to begin with, it could help filtering patients by analyzing several factors like genetics and select the ones who are eligible for the trials. Furthermore, the patients who are participating in clinical trials could also be monitored in real-time. Even the possible side effects could also be predicted and in turn, would save lives.

Successful sales and marketing efforts

The pharmaceutical industry can see a great difference in marketing efforts if only they use data-driven insight. Analyzing the data the companies could identify the locations and physicians ideal for the promotion of their new drug. They can also identify the needs of the patients and could target their sales representative teams towards that location. This would take the guesswork out of the process and increase the chance of getting a higher ROI. The data can also help them predict market trends as well as understand customer behavior. Another factor to consider here is monitoring the market response to a particular drug and also its performance, as this would help fine-tune marketing strategies.

Collaborative efforts

With the help of data, there could be better collaboration among the different segments that directly impact the industry. The companies could suggest different drugs that could be patient-specific and the physicians could use real-time patient data to decide whether the suggestions should be implemented in the treatment plan. There could be internal and external collaborations as well to improve the overall industry functioning. Be it reaching out to researchers or, CROs, establishing a strong link can help the industry move further.

Predictive analysis

A new drug might be effective in handling a particular health issue and could revolutionize the treatment procedure but, the presence of certain compounds might prove to be fatal for certain patients and drug toxicity if not detected at an early stage could endanger a particular patient. So, using predictive analysis a patient data could be analyzed to determine the genetic factors, disease history, as well as lifestyle. The smart algorithms thereby help identify the risk factors and makes it possible to take a personalized approach regarding medication that could prove to be more effective rather than some random medication.

Big data can increase the efficiency of the pharmaceutical industry in more ways than one, but compared to other industries somehow this industry still hasn’t been able to utilize the full potential of big data, due to factors like privacy and, monetary issues. The lack of trained professionals could also prove to be a big obstacle. Sending their select professionals for Data Science training, could prove to be a big boon for them in the future.


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The link between AI, ML and Data Science

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The link between AI, ML and Data Science

The fields of Artificial Intelligence, Machine Learning and Data Science cover a vast area of study and they should not be confused with each other. They are distinct branches of computational sciences and technologies.

Artificial Intelligence

Artificial intelligence is an area of computer science wherein the computer systems are built such that they can perform tasks with the same agility as that done through human intelligence. These tasks range from speech recognition to image recognition and decision making systems among others.

This intelligence in computer systems is developed by human beings using technologies like Natural Processing Language (NLP) or computer vision among others. Data forms an important part of AI systems. Big Data, vast stashes of data generated for computer systems to analyze and study to find patterns in is imperative to Artificial Intelligence. 

Machine learning

Machine learning is a subset of artificial intelligence. Machine learning is used to predict future courses of action based on historical data. It is the computer system’s ability to learn from its environment and improve on its findings.

For instance, if you have marked an email as spam once, the computer system will automatically learn to mark as spam all future emails from that particular address. To construct these algorithms developers need large amounts of data. The larger the data sets, the better the predictions. A subset of Machine Learning is Deep Learning, modeled after the neural networks of the human brain.

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Data Science:

Data science is a field wherein data scientists derive valuable and actionable insights from large volumes of data. The science is based on tools developed with the knowledge of various subjects like mathematics, computer programming, statistical modeling and machine learning.

The insights derived by data scientists help companies and business organizations grow their business. Data science involves analysis of data and modelling of data among other techniques like data extraction, data exploration, data preparation and data visualization. As data volumes grow more and more vast, the scope of data science is also growing each passing day, data that needs to be analyzed to grow business.

Data Science, Machine Learning and Artificial Intelligence

Data Science, Artificial Intelligence and Machine Learning are all related in that they all rely on data. To process data for Machine Learning and Artificial Intelligence, you need a data scientist to cull out relevant information and process it before feeding it to predictive models used for Machine Learning. Machine Learning is the subset of Artificial Intelligence – which relies on computers understanding data, learning from it and making decisions based on their findings of patterns (virtually impossible for the human eye to detect manually) in data sets. Machine Learning is the link between Data Science and Artificial Intelligence. Artificial Intelligence uses Machine Learning to help Data Science get solutions to specific problems.

The three technological fields are thus, closely linked to each other. For more on this, do not forget to check-out the artificial intelligence certification in Delhi NCR from DexLab Analytics.


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The Four Important Machine Learning Algorithms in Use

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The Four Important Machine Learning Algorithms in Use

Machine Learning, a subset of Artificial Intelligence, has revolutionized the business environment the world over. It has brought actionable insights to business operations and helped increase profits acting as a reliable tool of business operations. In fact, its role in the business environment has become almost indispensable, so much so that machine learning algorithms are needed to maintain competitiveness in the market. Here is a list of machine learning algorithms crucial to businesses.

Supervised Machine Learning Algorithms

Supervised Learning involves those algorithms which involve direct supervision of the operation. In this case, the developer labels sample data corpus and sets strict boundaries upon which the algorithm operates, says a report.

Here human experts act as the tutor or teacher feeding the computer system with input and output data so the computer can learn the patterns.

“Supervised learning algorithms try to model relationships and dependencies between the target prediction output and the input features such that we can predict the output values for new data based on those relationships which it learned from the previous data sets,” says another report.

The most widely used supervised algorithms are Linear Regression; Logistical Regression; Random Forest; Gradient Boosted Trees; Support Vector Machines (SVM); Neural Networks; Decision Trees; Naive Bayes; Nearest Neighbor. Supervised algorithms are used in price prediction and trend forecasting in sales, retail commerce, and stock trading.

Unsupervised Machine Learning Algorithms

Unsupervised Learning is the algorithm which does not involve direct control of the developer or teacher. Unlike in supervised machine learning where the results are known, in the case of unsupervised machine learning algorithms, the desired results are unknown and not yet defined. Another big difference between the two is that supervised learning uses labelled data exclusively, while unsupervised learning feeds on unlabeled data.

The unsupervised machine learning algorithm is used for exploring the structure of the information; extracting valuable insights; detecting patterns; implementing this into its operation to increase efficiency.

Digital marketing and ad tech are the two fields where Unsupervised Learning is used to effectively. Also, this algorithm is often applied to explore customer information and mould the service accordingly.

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Semi-supervised Machine Learning Algorithms

Semi-supervised learning algorithms represent features of both supervised and unsupervised algorithms. In essence, the semi-supervised model combines some aspects of both into a unique aspect of itself. Semi-supervised machine learning algorithm uses a limited set of labelled sample data to train itself. The limitation results in a partially trained model that later gets the task to label the unlabeled data. Due to the limitations of the sample data set, the results are considered pseudo-labelled data, says a report. Lastly, labelled and pseudo-labelled data sets are combined with each other to create a distinct algorithm that combines descriptive and predictive aspects of supervised and unsupervised learning.

Semi-supervised learning uses the classification process to identify data assets and clustering process to group it into distinct parts.

Legal and Healthcare industries, among others, manage web content classification, image and speech analysis with the help of semi-supervised learning.

Reinforcement Machine Learning Algorithms

Reinforcement learning represents what is commonly understood as machine learning artificial intelligence.

In essence, reinforcement learning is all about developing a self-sustained system that, throughout contiguous sequences of trials and errors, improves itself based on the combination of labelled data and interactions with the incoming data. The method aims at using observations gathered from the interaction with the environment to take actions that would maximize the reward or minimize the risk.

Most common reinforcement learning algorithms include: Q-Learning; Temporal Difference (TD); Monte-Carlo Tree Search (MCTS); Asynchronous Actor-Critic Agents (A3C).

Modern NPCs and other video games use this type of machine learning model a lot. Reinforcement Learning provides flexibility to the AI reactions to the player’s action thus providing viable challenges. Self-driving cars also rely on reinforced learning algorithms.

For more on Machine Learning courses in Delhi, check out the DexLab Analytics course structure today.


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8 Applications of AI and Machine Learning in our Daily Lives

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8 Applications of AI and Machine Learning in our Daily Lives

Artificial intelligence (AI) and machine learning are today thought to be one of the biggest innovations since the microchip. With the advancement of the science of neural networks, scientists are making extraordinary breakthroughs in machine learning through what is termed as deep learning. These sciences are making life easier and more streamlined for us in more ways than one. Here are a few examples.

1. Smart Gaming

Artificial Intelligence and Machine Learning are used in smart gaming techniques, especially in games that primarily require the use of mental abilities like chess. Google DeepMind’s AlphaGo learnt to play chess, and defeat champions like Lee Sedol (in 2016) by not only studying the moves of masters but by learning how to play the game by practising against itself innumerable times.

2. Automated Transportation

When we fly in an airplane, we experience automated transportation in the sense that a human pilot is only flying the plane for a couple of minutes during take-off and landing. The rest of the flight is maneuvered by a Flight Management System, a synchronization of GPS, motion sensors and computer systems that track flight position. Google Maps has already revolutionized local transport by studying coordinates from smart phones to determine how fast or slow a vehicle is moving and therefore how much traffic there is on a given road at any point of time.

3. Dangerous Jobs

AI technology powered robots are taking over dangerous jobs like bomb disposal and welding. In bomb disposal, today, robots need to be controlled by humans. But scientists believe there will soon come a time when these tasks would be completed by robots themselves. This technology has already saved hundreds of lives. In the field of welding, a hazardous job which entails working in high levels of noise and heat in a toxic environment, robots are helping weld with greater accuracy.

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4. Environmental Protection

Machine Learning and artificial intelligence run on big data, large caches of data and mind boggling statistics generated by computer systems. When put to use in the field of environmental protection, these technologies could be used to extract actionable solutions to untenable problems like environmental degradation. For instance, “IBM’s Green Horizon Project studies and analyzes environmental data from thousands of sensors and sources to produce accurate, evolving weather and pollution forecasts.”

5. Robots as Friends

A company in Japan has invented what it calls a robot companion named Pepper who can understand and feel emotions and empathy. Introduced in 2014, Pepper went on sale in 2015 and all the 1000 units were sold off immediately. “The robot was programmed to read human emotions, develop its own, and help its human friends stay happy,” a report says. Robots could also assist the aged in becoming independent and take care of themselves, says a computer scientist at Washington State University.

6. Health Care

Hospitals across the world are mulling over the adoption of AI and ML to treat patients so there are reduced instances of hospital related accidents and spread of diseases like sepsis. AI’s predictive models are helping in the fight against genetic diseases and heart ailments. Also, Deep Learning models which “quickly provide real-time insights and…are helping healthcare professionals diagnose patients faster and more accurately, develop innovative new drugs and treatments, reduce medical and diagnostic errors, predict adverse reactions, and lower the costs of healthcare for providers and patients.”

7. Digital Media

Machine learning has revolutionized the entertainment industry and technology has already found buyers in streaming services such as Netflix, Amazon Prime, Spotify, and Google Play. “ML algorithms are…making use of the almost endless stream of data about consumers’ viewing habits, helping streaming services offer more useful recommendations.”

These technologies will assist with the production of media too. NLP (Natural Language Processing) algorithms help write and compose trending news stories, thus cutting on production time. Moreover, a new MIT-developed AI model named Shelley “helps users write horror stories through deep learning algorithms and a bank of user-generated fiction.”

8. Home Security and Smart Stores

AI-integrated cameras and alarm systems are taking the home security world by storm. The cutting-edge systems “use facial recognition software and machine learning to build a catalog of your home’s frequent visitors, allowing these systems to detect uninvited guests in an instant.” Brick and Mortar stores are likely to adopt facial recognition for payments by shoppers. Biometric capabilities are largely being adopted to enhance the shopping experience.

Key Takeaway

AI is no longer the domain of fiction. It’s our new reality and is it no surprise then that it is revolutionizing our lives. Deep learning training institutes and Machine Learning courses in India along with Artificial Intelligence courses in Delhi abound because India too is attempting to make the most of the AI revolution.


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How AI and Machine Learning are Helping Fight Coronavirus

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How AI and Machine Learning are Helping Fight Coronavirus

A Toronto based AI-startup detected the outbreak of coronavirus, a large family of viruses which infect the respiratory tract of human beings and animals, hours after the first few cases were diagnosed in Wuhan in December 2019.

More than 100,000 people the world over have been infected by the novel coronavirus since then and more than 4000 people have died, most in China.

The start-up team confirmed their findings and informed their clients about an “unusual pneumonia” in a market place in Wuhan a week before Chinese authorities and international health bodies made formal announcements about the virus and the epidemic. The key to the company’s ability to detect and warn of a possible outbreak of an epidemic is AI and big data.

NLP and Machine Learning

The company uses natural language processing or NLP and machine learning to, says a report, “cull data from hundreds of thousands of sources, including statements from official public health organizations, digital media, global airline ticketing data, livestock health reports and population demographics. It’s able to rapidly process tons of information every 15 minutes, 24 hours a day.”

This information becomes the basis of reports compiled by computer programmers and physicians. Also, they do not just detect the outbreak of a disease but also track its spread and the consequences.

In the case of COVID-19, the company besides sending out an alert, correctly identified the cities that were highly connected to Wuhan using data on global airline ticketing “to help anticipate where the infected might be travelling.”

GDP

“Already, the COVID-19 coronavirus is likely to cut global GDP growth by $1.1 trillion this year, in addition to having already wiped around $5 trillion off the value of global stock markets,” a report says.

The vast amount of X-rays and scans people across the world are undergoing in this outbreak of coronavirus has strained medical resources and systems across the world. That is why AI and machine learning models are being trained to read accurately vast amounts of data tirelessly, and efficiently.

Thermal Scanners

China has already deployed AI-powered thermal scanners at railway stations in major cities to read and record, from a distance through infrared, body temperatures of persons passing to detect a fever. This technology has to a large extant reduced stress on institutions across the country.

But it must be noted that AI is set to become a huge firewall against infectious diseases and pandemics not only by powering diagnostic techniques but by identifying potential vaccines and lines of treatment against the next coronavirus and COVID-19 itself within days.

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Robots

Also, AI and big data are helping revolutionize the medical management system in China. With the outbreak of the pandemic, China hospitals are using robots to reduce the stresses piled on medical staff. Ambulances in the city of Hangzhou are assisted by AI in navigation to help them reach patients and people suspecting an infection faster.

“Robots have even been dispatched to a public plaza in Guangzhou in order to warn passersby who aren’t wearing face-masks…China is also allegedly using drones to ensure residents are staying at home and reducing the risk of the coronavirus spreading further.”

In India, though the virus has been detected in some states, it has not spread as alarmingly as it has in other countries. It is now more than ever important to concentrate on building more robust and competent Artificial Intelligence courses in Delhi and Machine Learning courses in India.


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Why Python is Preferred in AI and Machine Learning?

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Why Python is Preferred in AI and Machine Learning?

Python has become one of the leading coding languages across the globe and for more reasons than one. In this article, we evaluate why Python is beneficial in the use of Machine Learning and Artificial Intelligence applications.

Artificial intelligence and Machine Learning are profoundly shaping the world we live in, with new applications mushrooming by the day. Competent designers are choosing Python as their go-to programming language for designing AI and ML programs.

Artificial Intelligence enables music platforms like Spotify to prescribe melodies to users and streaming platforms like Netflix to understand what shows viewers would like to watch based on their tastes and preferences. The science is widely being used to power organizations with worker efficiency and self-administration. 

Machine-driven intelligence ventures are different from traditional programming languages in that they have innovation stack and the ability to accommodate an AI-based experiment. Python has these features and more. It is a steady programming language, it is adaptable and has accessible instruments.

Here are some features of Python that enable AI engineers to build gainful products.

  • An exemplary library environment 

“An extraordinary selection of libraries is one of the primary reasons Python is the most mainstream programming language utilized for AI”, a report says. Python libraries are very extensive in nature and enable designers to perform useful activities without the need to code them from scratch.

Machine Learning demands incessant information preparation, and Python’s libraries allows you to access, deal with and change information. These are libraries can be used for ML and AI: Pandas, Keras, TensorFlow, Matplotlib, NLTK, Scikit-picture, PyBrain, Caffe, Stats models and in the PyPI storehouse, you can find and look at more Python libraries. 

  • Basic and predictable 

Python has on offer short and decipherable code. Python’s effortless built allows engineers to make and design robust frameworks. Designers can straightway concentrate on tackling an ML issue rather concentrating on the subtleties of the programming language. 

Moreover, Python is easy to learn and therefore being adopted by more and more designers who can easily construct models for AI. Also, many software engineers feel Python is more intuitive than other programming languages.

  • A low entry barrier 

Working in the ML and AI industry means an engineer will have to manage tons of information in a prodigious way. The low section hindrance or low entry barrier allows more information researchers to rapidly understand Python and begin using it for AI advancement without wasting time or energy learning the language.

Moreover, Python programming language is in simple English with a straightforward syntax which makes it very readable and easy to understand.

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Conclusion

Thus, we have seen how advantageous Python is as a programming language which can be used to build AI models with ease and agility. It has a broad choice of AI explicit libraries and its basic grammar and readability make the language accessible to non-developers.

It is being widely adopted by developers across institutions working in the field of AI. It is no surprise then that artificial intelligence courses in Delhi and Machine Learning institutes in Gurgaon are enrolling more and more developers who want to be trained in the science of Python.


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Skills Data Scientists Must Master in 2020

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Skills Data Scientists Must Master in 2020

Big data is all around us, be it generated by our news feed or the photos we upload on social media. Data is the new oil and therefore, today, more than ever before, there is a need to study, organize and extract knowledgeable and actionable insights from it. For this, the role of data scientists has become even more crucial to our world. In this article we discuss the various skills, both technical and non-technical a data scientist needs to master to acquire a standing in a competitive market.

Technical Skills

Python and R

Knowledge of these two is imperative for a data scientist to operate. Though organisations might want knowledge of only one of the two programming languages, it is beneficial to know both. Python is becoming more popular with most organisations. Machine Learning using Python is taking the computing world by storm.

GitHub

Git and GitHub are tools for developers and data scientists which greatly help in managing various versions of the software. “They track all changes that are made to a code base and in addition, they add ease in collaboration when multiple developers make changes to the same project at the same time.”

Preparing for Production

Historically, the data scientist was supposed to work in the domain of machine learning. But now data science projects are being more often developed for production systems. “At the same time, advanced types of models now require more and more compute and storage resources, especially when working with deep learning.”

Cloud

Cloud software rules the roost when it comes to data science and machine learning. Keeping your data on cloud vendors like AWS, Microsoft Azure or Google Cloud makes it easily accessible from remote areas and helps quickly set up a machine learning environment. This is not a mandatory skill to have but it is beneficial to be up to date with this very crucial aspect of computing.

Deep Learning

Deep learning, a branch of machine learning, tailored for specific problem domains like image recognition and NLP, is an added advantage and a big plus point to your resume. Even if the data scientist has a broad knowledge of deep learning, “experimenting with an appropriate data set will allow him to understand the steps required if the need arises in the future”. Deep learning training institutes are coming up across the globe, and more so in India.

Math and Statistics

Knowledge of various machine learning techniques, with an emphasis on mathematics and algebra, is integral to being a data scientist. A fundamental grounding in the mathematical foundation for machine learning is critical to a career in data science, especially to avoid “guessing at hyperparameter values when tuning algorithms”. Knowledge of Calculus linear algebra, statistics and probability theory is also imperative.

SQL

Structured Query Language (SQL) is the most widely used database language and a knowledge of the same helps data scientist in acquiring data, especially in cases when a data science project comes in from an enterprise relational database. “In addition, using R packages like sqldf is a great way to query data in a data frame using SQL,” says a report.

AutoML

Data Scientists should have grounding in AutoML tools to give them leverage when it comes to expanding the capabilities of a resource, which could be in short supply. This could deliver positive results for a small team working with limited resources.

Data Visualization

Data visualization is the first step to data storytelling. It helps showcase the brilliance of a data scientist by graphically depicting his or her findings from data sets. This skill is crucial to the success of a data science project. It explains the findings of a project to stakeholders in a visually attractive and non-technical manner.

Non-Technical Skills

Ability to solve business problems

It is of vital importance for a data scientist to have the ability to study business problems in an organization and translate those to actionable data-driven solutions. Knowledge of technical areas like programming and coding is not enough. A data scientist must have a solid foundation in knowledge of organizational problems and workings.

Effective business communication

A data scientist needs to have persuasive and effective communication skills so he or she can face probing stakeholders and meet challenges when it comes to communicating the results of data findings. Soft skills must be developed and inter personal skills must be honed to make you a creatively competent data scientist, something that will set you apart from your peers.

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Agility

Data scientist need to be able to work with Agile methodology in that they should be able to work based on the Scrum method. It improves teamwork and helps all members of the team remain in the loop as does the client. Collaboration with team members towards the sustainable growth of an organization is of utmost importance.

Experimentation

The importance of experimentation cannot be stressed enough in the field of data science. A data scientist must have a penchant for seeking out new data sets and practise robustly with previously unknown data sets. Consider this your pet project and practise on what you are passionate about like sports.


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Applications of Artificial Intelligence: Healthcare

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Applications of Artificial Intelligence: Healthcare

This article, the second part of a series, is on the application of artificial intelligence in the field of healthcare. The first part of the series mapped the applications of AI and deep learning in agriculture, with an emphasis on precision farming.

 AI has been taking the world by storm and its most crucial application is to the two fields mentioned above. Its application to the field of healthcare is slowly expanding, covering fields of practice such as radiology and oncology.

Stroke Prevention

In a study published in Circulation, a researcher from the British Heart Foundation revealed that his team had trained an artificial intelligence model to read MRI scans and detect compromised blood flow to and from the heart.

And an organisation called the Combio Health Care developed a clinical support system to assist doctors in detecting the risk of strokes in incoming patients.

Brain-Computer Interfaces

Neurological conditions or trauma to the nervous system can adversely affect a patient’s motor sensibilities and his or her ability to meaningfully communicate with his or her environment, including the people around.

AI powered Brain-Computer Interfaces can restore these fundamental experiences. This technology can improve lives drastically for the estimated 5,00,000 people affected by spinal injuries annually the world over and also help out patients affected by ALS, strokes or locked-in syndrome.

Radiology

Radiological imagery obtained from x-rays or CT scanners put radiologists in danger of contracting infection through tissue samples which come in through biopsies.  AI is set to assist the next generation of radiologists to completely do away with the need for tissue samples, experts predict.

A report says “(a)rtificial intelligence is helping to enable “virtual biopsies” and advance the innovative field of radiomics, which focuses on harnessing image-based algorithms to characterize the phenotypes and genetic properties of tumors.”

Cancer Treatment

One reason why AI, has made immense advancements in the field of medical oncology is the vast amount of data generated during cancer treatment.

Machine learning algorithms and their ability to study and synthesize highly complex datasets may be able to shed light on new options for targeting therapies to a patient’s unique genetic profile.

Developing countries

Most developing counties suffer from health care systems working on shoe-string budgets with a lack of critical healthcare providers and technicians. AI-powered machines can help plug the deficit of expert professionals.

For example, AI imaging tools can study chest x-rays for signs of diseases like tuberculosis, with an impressive rate of accuracy comparable to human beings. However, algorithm developers must bear in mind the fact that “(t)he course of a disease and population affected by the disease may look very different in India than in the US, for example,” the report says. So an algorithm based on a single ethnic populace might not work for another.

Conclusion

It is no surprise then that developing countries like India are even more enthusiastic about adopting deep learning courses in Delhi and machine learning and artificial intelligence sciences in the healthcare sector. Machine Learning courses in India are coming up everywhere and it is important to note that DexLab Analytics is one of the leading artificial intelligence training institute in Gurgaon. Do visit the website today.


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