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Tag: Artificial Intelligence

What Are The Application of Machine Learning in Medicine?

Posted on by Imarticus Learning

Medicine and healthcare are leading industries with altruistic goals. Smart applications of the Machine Learning are today playing a role in a variety of areas like billing, insurance claims, record-keeping, patient-care, staffing solutions and many more. Just over the last decade technology has changed much. So have the volumes of data and its complexities, the various smart devices being used, and the algorithms specific to medical care.

ML is the system of algorithms developed for specific tasks that use deep-learning and artificial intelligence to simulate the way in which the human brain works to execute and aid us in complex tasks. Hence Machine Learning Course is an evolving and very important field. The ability of the ML algorithms to accurately predict, self-learn and assist us with forecasts is truly amazing and way beyond what the human brain is capable of. The field of ML is lucrative too!

Let us look at some of the best applications of ML in the medical and allied fields.
1. Diagnosis on a global scale: ML algorithms and applications score in disease diagnosis, providing simple diagnosis and online treatment even in rural areas, identifying patterns in the disease progression that is hard to recognize, and creating a global repository of research to help better the healthcare segment. Some of these are cancer detection, treating underlying genetic conditions hard-to-diagnose like Parkinson’s, diabetes, etc, providing therapeutic aid, and predicting conditions like stress, depression, etc.

2. CAT scans and MRI aided Diagnosis: Deep Learning and ML have been responsible for the development of advanced imaging devices like the Computer-Aided-Tomography scans, Magnetic-Resonance-Imaging, and the non-invasive Doppler or ultrasound scans. These developments embody machine learning training, handling of large data volumes and the ability of AI and can easily be used to forecast accurately the development of tumours and aneurysms.

3. Manufacturing and discovery of new drugs: Research, discovery, and use of the latest drugs are very important today as we discover more diseases and strains that are traditional-drug-resistant. ML allows the learning in an unsupervised fashion and helps develop new drugs with optimum dosages which can even be personalized for cancer patients.

4. Modification behavior-therapy: Prevention being better than cure several startup firms have come out with gesture-control based therapy apps, symptomatic analysis, early cancer warning, and detection apps, sleep trackers and such behavior-sensing apps that can help even with predicting the susceptibility to genetic diseases. For ex: Angelina Jolie underwent mastectomy based on such a forecast of cancer.

5. App-based medical personalization: Currently medical predictions use biosensors, historical data, genetic information and symptoms to diagnose ailments which tend to have rather fixed treatment methods. The time is not far where ML can offer holistic personalized treatment options which can help faster recoveries by identifying the underlying cause, diets, optimum medicine dosages, and recovery paths in a moment. Even the very process of seeking doctor’s advice appears to be going online to reach out further into rural areas and the personalization of healthcare regimens.

6. Outbreak Prediction: ML, deep-learning and AI have succeeded in not only assisting in healthcare but can also accurately and through unsupervised learning predict and forecast epidemic outbreaks which can then be nipped in the bud.

7. Smart Record Keeping and insurance claims:
The better the maintenance of records the easier cashless hospitalization and treatment becomes. ML is taking very large strides with smart apps to enable smart health-records in real-time, better document classification and intelligent paperless claim settlements.

8. R and D and medical trials: ML has and bears high potential in these areas of medical care where innovation and data play a huge role.

9. Data Crowdsourcing: The smart apps powered through intelligent ML allows doctors globally to crowdsource their data resources making it easy to give a real-time diagnosis, treatment, and better healthcare facilities. Even operations and complex deliveries can now be conducted easily and safely.
10. Radiotherapy and medical imaging:
Radiology, laser treatments, pathology, gastroenterology, cosmetology and several disciplines of medicine are being innovatively modernized with smart algorithmic-based ML to make model-making of individual cases much easier to treat successfully.

In conclusion, ML is growing by the moment and now is the right time to get on board this transformation. If you want to learn Machine Learning and reap career benefits in the medical field then you need to have Machine Learning Training.

At Imarticus Learning, the mentored specialization in skill-oriented courses like ML is par excellence and comes with certification, skill-based training, personality development, and assured placements. Hurry!

4 Industries Where Artificial Intelligence is Making a Huge Impact

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Artificial Intelligence has been the next best thing to revolutionize the world we live in. Once a distant dream, AI is now a reality owing to higher and far more powerful processing powers and advances in the field of machine learning. The applications of AI are bountiful and range over many areas. Here are some of the areas where AI is working wonders.

Healthcare

AI surpasses human capabilities when it comes to processing massive amounts of data efficiently and accurately in a matter of seconds or minutes. This can become indispensable for the medical sector. There are AI-powered apps such as Ada and Babylon where users can enter their symptoms, and the apps use data analytics to offer the users a medical consultation. AI can also generate customized treatment paths for patients depending on their medical histories, genetics, and symptoms. Since AI is based on machine learning algorithms, the more data you feed, the more accurate the results will be. In a field where the question is often about saving someone’s life, using systems that are quick, efficient and free of human error will go a long way.

Security

Another critical area where AI is increasingly deployed is security. The amount of data being stored in the cloud has given rise to some severe cyber security concerns. AI, through a combination of data analytics and machine learning, can offer protection from hackers by automating the intricate process of detecting and preventing breaches. This can be done with the speed and accuracy that lie beyond human ability. Since it uses machine learning that mimics humans’ experiential learning, AI-powered security systems are getting progressively sophisticated and powerful as they analyze more data. This also makes it more difficult for hackers to steal or corrupt data. However, technology has its limitations, and it is possible for AI to lose against a hacker so a combination of humans and AI can proficiently combat the increasing security threats.

Education

Another field where AI has an increasing influence is in the area of education. One of the primary uses of AI is in grading – a very time-consuming job that often might have errors. Deploying AI-powered machines for grading objective questions like multiple choice questions can save a lot of time. This can also be used across a wide range of students from school to graduate students. AI can also be used to analyze large amounts of data and develop personalized lessons for students based on previous learning patterns. Each of us learn at different paces and need different techniques and AI can do precisely that. It can also focus on places and subjects we lack in and thus revolutionize education as a whole. AI cannot replace teachers as a whole but can help them better the experience for students.

Human Resource

One of the most fundamental jobs of HR is that of recruitment. An HR department might have to go through loads and loads of applications which can be very time-consuming and stressful. Additionally, as psychology has time and again points out, humans are often subject to biases. AI can swiftly find the best candidates for a position based on processing all the data on the candidates’ CVs. AI is also devoid of the human element of bias. AI can also find possible risk areas of performance through data analytics of the employees. Moreover, it also can use available data and machine learning to offer decisions that would be best suited for the company.

AI has the potential to empower us and be a game-changer just like wheels and electricity were all those years ago. It’s just a matter of how we use it.

Related Article: The Promise of AI: Application in Education and Health Care Sector

How The Machine Learning Will Improve Education In The Future?

Posted on by Imarticus Learning

 

Education has definitely moved away from the teacher facing a classroom of students all using the same textbook. Today the learning experience is internet and ML dependent for data, technology, and digital resources, No wonder the education system is deeply invested in machine learning.

Let us explore how a machine learning course of AI is going to bring its benefits to the education experience of the future. The class sizes keep increasing with compulsory education and teachers are often facing many challenges in giving attention and help to the large numbers of students. A big challenge like this has been simplified by incorporating computer programs with ML algorithms that allow each student to follow his own pace and learning curve.

The newer methods of experiential learning at educational institutions use advanced techniques of AI, machine learning and deep learning in instructing and teaching like chatbots and learning bots. A differentiated machine learning course and AI style of learning deal with the most effective style to help the student learn.

Adaptive based learning curates the learning exercises matching them to the student’s needs and knowledge gaps. Competency-based AI tests aid the students to gauge their learning levels and progress from thereon. Using all these three types of learning, ML and AI can together test how well the students adapt their learning to applications and thus promote the progress of students based on individual interests.

What is machine learning? 

The definition of ML- machine learning is that it gives the AI the ability to self-learn from data, mimicking the human brain and is based on statistical techniques. The algorithm used need not be supervised or explicitly programmed. Almost all ML applications in education work very closely with concepts that are interconnected with artificial learning, deep learning of data, neural networks based on complex self-learning algorithms and the very basic concepts of a horde of machine learning course based applications helping machines do repetitive and intuitive tasks most times more accurately and better than humans themselves.

The benefits of machine learning in education:

Here are some ways in which ML makes a difference in the educational experience of educators and students.

Aid the educators: Data mining is the basis of ML and how well it performs. Forming a single repository of the students in one database, ML can effectively study each student’s behavior versus his peers. Thus ML can help cluster similar students and pace them better throughout the learning experience with the right resources and learning materials.

Gives insight to a student’s performance: One of the huge pluses of ML is the ability to give insights and make predictions based on data of a student’s performance. The ML technology can identify gaps and weaknesses to help students stay ahead of the curve.

Capacity to test students: ML can offer both offline and online tests and guidance that helps students to revise, relearn and evaluate performances. Both educators and students can benefit from their foresight and insights. The AI and ML-based tests and multiple choice answers also test the practical application of knowledge and not just rote learning.

Fair gradation of students: ML removes any bias in grading and scoring. The objective style tests and assignment answers can now be automatically assessed with tools like Grammarly or Turn It In. Both online and offline resources, MOOCs and such can be integrated into the learning process.

Experiential and customized learning: Personalizing the experience and offering near-instantaneous feedback is a huge advantage of ML. Both students and teachers can now benefit from knowing how to fill the knowledge gaps.

Content and feedback are instantaneous: ML is excellent at organizing content, task lists, learning resources, colleges, schools information and much more, to personalize the studying for each student. This helps students grade themselves and progress up the ladder with the suggested courses.

Through identifying weaknesses, machine learning can organize content more effectively. For example, as students learn one skill, they move on to the next skill continually building upon knowledge.

Drop-out rate reduction and retention: Corrective action can be applied rapidly if knowledge gaps persist and are identified by ML. This prevents higher drop-out rates while improving retention levels.

Availability based tutoring: This means ML will facilitate the student’s needs with an available expert tutor for effective learning and tutoring.

Conclusions:

Yes, technology and ML especially will transform the educational experience with more and more algorithms being developed by the minute. If you want to learn all about how to make a career in this field then do a machine learning course at the reputed Imarticus Learning Institute. Now is the right time to jump onto the bandwagon. Why wait?

What Is The Role of Machine Learning In Financial Fraud Prevention?

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What is the role of machine learning in financial fraud prevention?

The instances of fraud rose between 2015 and 2018, there was also an increase in the total value and volume of fraud, according to KPMG. Needless to say, financial fraud is alive and well, despite increasingly stringent measures to reduce numbers and tighten security.

Out of all fraud instances, banks reported that cyber-attacks were the most imminent risks, no doubt due to the burgeoning use of technology within the sector without adequate security. The solution is a tad ironic- to fight technology with more technology, like fighting fire with fire. Financial fraud prevention is best countered by machine learning, a subset of Artificial Intelligence.

Machine learning may be an alien concept to many, but it plays an intrinsic role in our daily functioning– even more so as times become for futuristic. Detection of email spam, product recommendations on your favorite video network or even image recognition algorithms on cellphones are examples of machine learning in everyday use.

For the banking industry, machine learning comes off much like a savior, especially in light of the many disadvantages of traditional fraud detection.

Traditional methods were heavily based on rules, which meant they could be inefficient, erroneous or hard to scale. This formed a weak buttress against sophisticated hackers who are well-versed in enhanced fraudulent methods and was as good as having no firewall after a point.

Against all of this, machine learning has an important role to play, as any Machine Learning course would tell you. Here are the benefits of using machine learning for financial fraud prevention:

Scalable, cost-effective algorithms

The benefit of machine learning algorithms is that more data means more precision. This is a definite step-up from traditional methods where bigger datasets could lead to incorrect data or even crashes. In machine learning, the algorithm learns with more data as it picks out outliers, nuances and different patterns to provide more accurate results in a more efficient manner.

It’s cost-effective, too– rule-based methods required a lot more in terms of costs to scale, whereas machine learning setups need a few tweaks to get back up and running after an update in datasets. An additional benefit is that machine learning algorithms can repeat menial, frustrating tasks 24/7 without a hitch– a feat highly impossible if a human employee were involved.

Real-time processing

In traditional methods of fraud detection and prevention, the focus was more on long-term processing, which often led to delays in resolving fraud reports and transactions. That naturally led to a lot of unhappy customers, but Machine Learning can successfully put an end to that saga.

The results from machine learning algorithms are real-time, which means instant updates and immediate results. Not only does this lead to quicker fraud resolutions, but it also helps banks identify loopholes in their system and fix them immediately.

Reduce time and increase accuracy

The main draws of machine learning are its ability to reduce the time and effort taken to detect fraud and prevent it. There are claims that a fully-ready machine learning algorithm can detect up to a whopping 95% of fraud, that too accurately. According to another Capgemini report, fraud detection software that uses machine learning can reduce the time taken to investigate by 75%, all the while improving the accuracy of fraud detection by 90%. Needless to say, machine learning purports a lot of benefits when used in the banking sector for fraud detection.

Applicable across industries

The banking sector liaises with multiple other industries, as do their customers. However, the fraud cases they deal with are often dissimilar in intensity, method, uses, and reasoning. It only makes sense, then, that a fraud detection setup is capable of handling fraud detection and resolution across all industries.

With machine learning, algorithms can be scaled or adapted for different industries including e-commerce, medicine, hospitality, retail, and insurance. By using unique datasets for each sector, similar machine learning algorithms can be used to cater to each, instead of limiting all claims to one or a few traditional fraud prevention methods.

The financial and banking sector has a lot to benefit from the use of machine learning in fraud detection– today’s machine learning courses today cover these uses in-depth, making aspirants ready for changing technology in a traditional industry.

Artificial Intelligence: Futuristic Career Options

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Artificial Intelligence has been defined as the area of computer science that aims at creating intelligent machines that can think and behave like humans. In reality, AI software has widespread applications in today’s age of digital media. From chatbots, Voice-assistant software to self-driving cars, AI and Machine Learning (ML) has become the way to go.
A study by McKinsey in 2017 suggested that by 2030 one third of the total jobs would be related to AI. However, the pandemic situation has intensified this change as more and more companies have started to revamp their strategies to become more technology-centric in their approaches.

Artificial Intelligence, thus, can provide huge opportunities and has great scope for the future. Let us look at some of the jobs that Artificial Intelligence will create

  1. User Experience (UX): Products that incorporate AI at the core will be hard to use. It would be mostly because of the fact people are not very well accustomed to AI products. Thus, the need for a User Experience specialist will grow in the coming years. These professionals would mainly be responsible to understand how people are employing the various machines and equipment so that scientists can develop even more advanced software. Thus, a UX specialist would be required to understand the functionalities of an AI-tech that would help in filling up of more need gaps in the due course.
  2. Computer Science and Artificial Intelligence Research: Every technology needs to be upgraded to keep it relevant, in the due course. A Computer Science and AI researcher’s job would be just that. Based upon the area of their specialization, an AI researcher would be mostly responsible for upgrading AI-based data systems and unlock new potential in the existing avenues by curating AI software. There might also be a need for researchers to overlook the ethical aspects of adopting AI technologies in any industry. For instance, an ethical specialist handling the trolley problem relating to the self-driving car.
  3. Software Engineering: The traditional role of software engineers would remain highly relevant to AI-related fields. The software engineers would be responsible to design the technical functionalities of a product that would help in better utilization of machine learning schemas to perform diverse operations. Existing software engineers who want to leverage this trend change are looking for more Artificial Intelligence courses. It is predicted that the job role of a software engineer would grow 28 per cent faster than the average in the upcoming future owing to the growth of AI.
  4. Data Scientists: Data Scientists are responsible for the meaningful analysis of data. The Artificial Intelligence systems run on huge databases, from which important information needs to be extracted to support a business in its ordinary course. A data scientist would be expected to be adept in maths and statistics so that it can transform numeric information into simpler terms that would be helpful to make key inferences and decisions.
  5. Big Data Engineer: A Big Data engineer is entrusted with the task of building an entire ecosystem that would help the AI frameworks of different businesses to interact with each other. The job role of big data scientists would mostly revolve around designing, planning, and building up the big data environment. It is a highly challenging task and it is expected to mostly involve individuals with PhD in mathematics, statistics, or computer science.
  6. Natural Language Processing Experts: Most of the AI tools are used revolve around converting or rather translating virtual languages to natural language that are understandable by human beings. This has seen widespread applications in chatbots, virtual voice assistants, etc. The key responsibility of the individual experts in these fields would be to create systems through which computers can communicate with humans. Thus, these individuals must have good language and technology skills.

Also Read: 10 Interesting Facts About Artificial Intelligence

What is Differential Deep Learning?

Posted on by Imarticus Learning

Introduction

The world today has changed drastically. Therefore, it is important to change according to the environment in which we function. One such major change has been brought about by the advent caused by Artificial Intelligence.

Deep learning is a concept which has come around from artificial intelligence. It has been adopted by a lot of companies who want to automate their processes and make things easy. Also, people have started transitioning to deep learning to cut their losses and maximise profits. Therefore, deep learning is creating a lot of jobs in the market.

So, you can take up a Deep Learning course to increase your employability.

Understanding Differential Deep Learning

Deep learning is a way of implementing mathematical methods in the day to day working of a company. Numbers change every day. In most cases, we have to do these changes manually. With deep learning, you can keep a tap on the numbers without any additional effort.

With Deep Learning, you can formulate different algorithms to make your job easy. Deep Learning provides you with the ease of making predictions about future rates and prices based on the past and current trends and also on the market situations at different points of time. It provides ease of calculating and predicting values even if the market is volatile. Deep learning increases the analytic speed and efficiency of any company.

With proper Machine Learning training, you can design different algorithms for a different set of problems with which the company deals. Application of Deep Learning brings in automation which drastically improves the performance of any system is it calculating the sales value, keeping a tap on the generated leads, making a financial analysis, etc.

Differential Learning principals can be applied to different machine learning algorithms. Also, once you are well versed with the working of differential learning, you can try your hands on any machine learning language. It is going to be easy for you to understand.

You can apply Differential learning in models like regression, Principal component analysis, etc to get the best results. Also, differential deep learning is effectively used in the field of Finance. This can be used to develop a mechanism on pricing.

You can also use this to manage and assess various risks. Differential deep learning is effectively used to produce different models for closed-form solutions. Various Risk management metrics like hedge strategies, etc can be easily generated with the help of deep learning.

Uses of Differential Deep Learning

  1. It is used for developing different machine learning models where these algorithms are built to give results on different sets of inputs.
  2. Differential deep learning is being widely used in the field of risk management. With the use of different models, you can assess the situation at different values,
  3. It makes use of Automatic Adjoint Differentiation which can calculate changes and derivative sensitives quite easily.
  4. With Differential deep learning various fast pricing analytics can be formed which can be used to compute the metrics of risk management at different sets of information
  5. It is used to extract important estimations and values even when small data sets are available. Using differential deep learning can bring you to a conclusion very easily.
  6. It replaces the frequent interference of humans which was a prerequisite earlier. Differential Deep learning creates a system which is automated and reduces the cost of labour to a great extent. This system reduces the chances of errors drastically.

Conclusion

Differential Deep learning has changed the way we use our data. It has made the application of Machine learning possible. Also, with the help of this, companies are moving towards a fully automated world.

How the Indian Government’s AI Website Will Help the Country’s Artificial Intelligence Industry?

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Collaboratively developed by the National Association of Software and Service Companies (Nasscom) and the National e-Governance Division of the Ministry of Electronics and Information Technology (MeitY), the portal is intended to be a one-stop-shop for all AI-related developments to happen in India.

The portal will also distribute documents, case studies, research reports and more relating to AI, and will also become a repository for AI-related learning job roles.

Noteworthy Resources On the Website

  • AI Courses

In order to help professionals and fresh graduates upskill, a variety of AI-related and machine learning courses are being offered through this government portal. The course providers range from IIT Bombay to companies such as IBM.

  • Editorial content

In order to collect all related information in one place, the website publishes editorial content and news as well as case studies and resources for budding AI entrepreneurs.

  • Highlighting successes

The portal also aims to highlight the country’s successes in AI in the recent past, spanning industries and use cases.

How the Website Will Help India’s AI Industry

The website is a distinctive step forward in recognizing the potential and scope of AI in India. Here are some ways in which the website can benefit the AI landscape at national and grassroots levels:

  • Democratising AI

Sanjay Dhotre, the MoS for E&IT, Communications, and HRD, said that digital innovation, including AI, has been a massive equaliser among the Indian population despite the diversity of the country. AI has already penetrated several homes; in the future, this website hopes to bring AI to virtually every part of the country and expound on its benefits in a growing economy.

  • Cementing India’s Place in the World

This public declaration of support towards Artificial Intelligence developments is a solid way of confirming to the world that India is investing in the technologies of the future. India has already supplied global leaders in technology to different parts of the world. By funding this AI website and encouraging its use, the country is making its way up the list of countries with one eye on the foreseeable future.

  • Encouraging Innovation

By making case studies, courses and resources accessible to the masses, the AI website is, in effect, encouraging each and every citizen to experiment in the field. There is no dearth of innovation and ideation in the country; this website becomes a place for resources to support the realization of AI-related dreams and the furthering of the country’s technological goals.

  • Building Networks

The website is touted as a convergence point for students, fresh graduates, AI experts, experimental businesses and new-age entrepreneurs to share and transact over revolutionary AI-driven ideas and proposals. The website also becomes a launching pad for innovation and a networking platform for those with ideas and those with the power to fund them. The departments are also working on creating investor pitch events to link ideators and funders alike.

  • Preparing the Future Generation

Alongside the website, the departments also announced the Responsible AI for Youth Program which is targeted at imparting AI education in government schools. The different phases of the programme aim to train teachers and gather groundbreaking ideas from the students themselves. Keeping in mind that India is one of the most populous countries in the world, instilling an understanding of AI and a passion for innovation at a young age is critical to the growth of the country internationally.

Conclusion

The AI website can be looked at as a show of support towards futuristic technologies that emerge from the grassroots level. By attempting to democratize AI, this government website is also creating a conducive atmosphere for technological growth on par with the rest of the world.

Why Artificial Intelligence is better than Other Technology

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Living in the 21st century, we have been able to see a lot happening in the field of technology and its advancement. Artificial Intelligence in that regard is the latest and the most developed version of classic technology. However, the term artificial intelligence still perplexes many people as they are not aware of its details. Even though people are using gadgets birthed by artificial intelligence, they may still not know about it.

What Artificial Intelligence Exactly Is?

AI is that field of machined technology that imitates humans and their functions on various levels. These machines or robots are functioned using various codes and algorithms to make them work as humanly as possible. It deals with the behavioural part of the machines which has a connection or a relation with intelligence. This branch of computer science has made it possible for the machines to think and act rationally and humanly to a great extent.

Why is AI Better than Other Technology?

Artificial Intelligence is any day better than human intelligence or works efficiently as compared to other technologies of the world. This can be made clear through the following points:

  • Mitigating Risks: AI has been working incessantly in reducing the risk factor associated with completing a task as compared to other technologies. To quote it as an example, forest fires can be handled more effectively with AI robotic drones instead of manual machines to put out the fire. AI can minimize the risks associated with human lives in many areas like radiation, electricity, hydropower and fire etc.
  • Zero Errors: Unlike other forms of technology or human intelligence for that matter, AI works so efficiently that it leaves no chance for errors or mistakes. Owing to a situation, human beings can alter the ways they work but AI does not change frequently according to their surroundings in all situations.
  • Remarkable Anticipation: The technology preceding AI is not accurate enough to make correct judgements about the events which are about to happen. However, forecasts or predictions made by AI are far more accurate. Likewise, AI sensors to forecast and measure the intensity of an earthquake beat the normal scales to measure the earthquakes. Also, AI functions with far less human involvement as compared to other technology.
  • Saves Time and Money: The classic technological advancement would just let you type a song name in the search list and it will present you a list of preferred songs. However, Siri and Alexa, a gift of artificial intelligence are voice regulated gadgets which work on your personalized instructions. There is no default set of questions and answers fed in these gadgets. They respond to any question with any suitable answer, isn’t that amazing?
  • Reduced Human Intervention: When technology functions on its own without human intervention, it can turn out to be the best. To illustrate, a doctor carrying out a surgical procedure makes use of all the modern gear and instruments but can still perform differently with different patients of the same conditions. However, this is not the case if robots carry out the surgery by themselves. This points to the fact that AI outshines human interference while making use of technology.

Conclusion
Embracing a positive approach to make use of Artificial Intelligence can always turn out to be in the best interest of every human being. Although, AI has eased the lifestyle and the way people used to work but still, exploring it outside the boundaries of positive development can cause harm to humanity at once.

If you wish to explore the field of AI and aspire to learn artificial intelligence, you must check out Imarticus learning for the same.

Top 3 Apache Spark Tutorials For Machine Learning Beginners!

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Apache Spark is a well-known name in the machine learning and developer worlds. For those who are unfamiliar, it is a data processing platform with the capacity to process massive datasets. It can do so on one computer or across a network of systems and computing tools. Apache Spark also offers an intuitive API that reduces the amount of repetitive computing and processing work that developers would otherwise have to do manually.

Today, Apache Spark is one of the key data processing and computing software in the market. It’s user-friendly and it can also be used through whatever programming language you’re most comfortable with including Python, Java and R. Spark is open-source and truly intuitive in that is can be deployed for SQL, data streaming, machine learning and processing graphs. Displaying core knowledge of Apache Spark will earn you brownie points at any job interview.

To gain a headstart even before you begin full-fledged work in Apache Spark, here are some tutorials for beginners to sign up for.

  1. Taming Big Data with Apache Spark and Python (Udemy)

This best-selling course on Udemy has fast become a go-to for those looking to dive into Apache Spark. More than 47,000 students have enrolled to learn how to:

  • Understand Spark Streaming
  • Use RDD (Resilient Distributed Datasets) to process massive datasets across computers
  • Apply Spark SQL on structured data
  • Understand the GraphX library

Big data science and analysis is a hot skill these days and will continue to be in the coming future. The course gives you access to 15 practical examples of how Apache Spark was used by industry titans to solve organisation-level problems. It uses the Python programming language. However, those who wish to learn with Scala instead can choose a similar course from the same provider.

  1. Machine Learning with Apache Spark (Learn Apache Spark)

This multi-module course is tailored towards those with budget constraints or those who are unwilling to invest too much time, preferring instead to experiment. The modules are bite-sized and priced individually to benefit those just dipping their toes. The platform’s module on “Intro to Apache Spark” is currently free for those who want to get started. Students can then progress to any other module which catches their fancy or do it all in the order prescribed. Some topics you can expect to explore are:

  • Feature sets
  • Classification
  • Caching
  • Dataframes
  • Cluster architecture
  • Computing frameworks
  1. Spark Fundamentals (cognitiveclass.ai)

This Apache Spark tutorial is led by data scientists from IBM, is four hours long and is free to register for. The advantage of this course is that it has a distinctly IBM-oriented perspective which is great for those wishing to build a career in that company. You will also be exposed to IBM’s own services, including Watson Studio, such that you’re able to use both Spark and IBM’s platform with confidence. The self-paced course can be taken at any time and can also be audited multiple times. Some prerequisites to be able to take this course are an understanding of Big Data and Apache Hadoop as well as core knowledge of Linux operating systems.

The five modules that constitute the course cover, among other topics, the following:

  • The fundamentals of Apache Spark
  • Developing application architecture
  • RDD
  • Watson Studio
  • Initializing Spark through various programming languages
  • Using Spark libraries
  • Monitoring Spark with metrics

Conclusion

Apache Spark is leveraged by multi-national million-dollar corporations as well as small businesses and fresh startups. This is a testament to how user-friendly and flexible the framework is.

If you wish to enrol in a Machine Learning Course instead of short and snappy tutorials, many of them also offer an introduction to Apache Spark. Either way, adding Apache Spark to your resume is a definite step up!

What is Alpha Beta Pruning in Artificial Intelligence?

Posted on by Imarticus Learning

What is Artificial Intelligence?

Most of us are aware of the edge cutting technology i.e. Artificial Intelligence (AI). It is used to create machines that have their decision-making capability. They can learn from their work environment and can behave autonomously. in the initial stages, it is man-made but once it has learned and evolved, it can enhance itself.

For example, the University of California, Irvine developed an AI machine that could solve Rubik’s cube. The machine learns and trains itself through algorithms and now it can solve complicated Rubik’s cube in a fraction of a second. In this article, let us learn about Alpha Beta Pruning in AI.

What is Alpha Beta Pruning?

Before you learn about Alpha-Beta Pruning, one needs to know about the minimax algorithm. Minimax algorithm backtracks a scenario/game and finds the best move which will enhance the decision making or in terms of gaming, will maximize the chances of winning. It assumes that there is an opponent who is also trying to win, it tries to reduce the winning chances of the opponent and optimizing its steps to win.

Alpha Beta Pruning is an optimization technique that decreases the number of steps in the minimax algorithm. It helps in reducing the number of steps in searching/traversing. For example, if we are applying a minimax algorithm in a chess game, then Alpha Beta Pruning helps in finding those steps which will not result in winning, and then those steps need not be traversed.

The minimax algorithm prepares a search tree after backtracking, there are many nodes in this search tree. The redundant/useless nodes are eradicated with the help of Alpha-Beta Pruning. It helps in decreasing complexity and saves time. There are two main components in the minimax algorithm, first one is maximizer which tries to get the highest score and the minimizer does the opposite. Let us know about the two parameters ‘Alpha’ & ‘Beta’:

Alpha Parameter (α):

The best choice/decision found in the whole path of maximizer is called Alpha. Its initial value is (-∞). One can also say that the highest value along the path of maximizer is Alpha.

Beta Parameter (β):

The best choice/decision found in the path of minimizer is called Beta. It is the lowest value of all the values encountered in the path of the minimizer. The initial value of the Beta parameter is supposed to be (+∞).

 Note: Before Pruning one needs to check whether (α>=β). This is a necessary condition to run the Alpha Beta Pruning algorithm.

Why Alpha Beta Pruning is important?

There is no change in the result if we compare the outputs minimax algorithm and Alpha-Beta Pruning. Pruning helps in decreasing the number of steps thus making the algorithm faster and less complex.

Key points and terminologies in Alpha-Beta Pruning

  1. The child node is provided with the values of α & β.  While backtracking, the values of lower-order nodes are passed to the upper nodes in the search tree except for the child node.
  2. In some cases, the Alpha Beta Pruning algorithm fails to reduce the number of nodes. In such cases, more time is wasted because of α & β parameters and the number of steps comes out to be the same as the minimax algorithm.
  3. This scenario is called Worst Ordering. Ideal Ordering occurs when a lot of pruning happens and a lot of steps are decreased (especially on the left side of the tree).

Conclusion

AI is a budding technology and is expected to grow more. If you want to learn more about AI, then you can search for courses available online. One such good course is the PG program in Analytics & Artificial Intelligence in Imarticus Learning’s list of offered programs. This article was all about the Alpha Beta Pruning algorithm in AI. I hope it helps!