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Category: Machine Learning Courses

Summer Internship/Training 101

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Summer Internship/Training 101

Hard Fact: Nowadays, all major organizations seek candidates who are technically sound, knowledgeable and creative. They don’t prefer spending time and money on employee training.  Thus, fresh college graduates face a tricky situation.

Summer internship is a quick solution for them. Besides guaranteeing a valuable experience to the fresh graduates, internship helps them secure a quick job. However, the question is what exactly is a summer internship program and how does it help bag the best job in town?

What Is a Summer Internship?

Summer internships are mostly industrial-level training programs for students who are interested in core technical industry domain. Such internships offer students hands-on learning experience while letting them gain glimpses of the real world – following a practical approach. Put simply, summer trainings enhance skills, sharpen theoretical knowledge and are a great way to pursue a flourishing career. In most cases, the candidates are hired by the companies in which they are interning.

The duration of such internships is mostly between eight to twelve weeks following the college semesters. Mostly, they start from May or June and proceeds through August. So, technically, this is the time for summer internships and at DexLab Analytics, we offer industry-relevant certification courses that break open a gamut of job opportunities. Also, such accredited certifications add value to your CV. They help build powerful CVs.

If you are a college student and from Delhi, NCR, drop by DexLab Analytics! Browse through our business analytics, risk analytics, machine learning and data science course sections. Summer internships are your key to success. Hurry now!

Deep Learning and AI using Python

Why Is It Important?

Summers are crucial. If you are college-goer, you will understand that summertime is the most opportune time to explore diverse career interests without being bogged down by homework or classroom assignments.

Day by day, summer internships are becoming popular. Not only do they expose aspiring candidates to the nuances of the big bad world but also hone their communication skills, create great resumes and make them super confident. Building confidence is extremely important. If you want to survive in this competitive industry, you have to present a confident version of you. Summer training programs are great in this respect. Plus, they add value to your resume. A good internship will help you get noticed by the prospective employers. Always, try to add references; however, ask permission from your supervisors before including their names as references in your resume.

Moreover, summer training gives you the scope to experiment and explore options. Suppose, you are pursuing Marketing Major and bagged an internship in the same, but you are not happy with it. Maybe, marketing is not your thing. No worries! Complete your internship and move on.  

On the other hand, let’s say you are very happy with your selected internship and want to do something in the respective field! Finish the internship, wait for some time and then try for recruitment in the same company where you interned or explore possibilities in the same domain.

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It’s no wonder that summer internships open a roadway of opportunities. The technical aptitude and in-demand skills learned during the training help you accomplish your desired goal in life.

For more advice or expert guide, follow DexLab Analytics.

 

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AI-Related Tech Jargons You Need To Learn Right Now

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AI-Related Tech Jargons You Need To Learn Right Now

As artificial intelligence gains momentum and becomes more intricate in nature, technological jargons may turn unfamiliar to you. Evolving technologies give birth to a smorgasbord of new terminologies. In this article, we have tried to compile a few of such important terms that are related to AI. Learn, assimilate and flaunt them in your next meeting.

Artificial Neuron Networks – Not just an algorithm, Artificial Neuron Networks is a framework containing different machine learning algorithms that work together and analyzes complex data inputs.

Backpropagation – It refers to a process in artificial neural networks used to discipline deep neural networks. It is widely used to calculate a gradient that is required in calculating weights found across the network.

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Bayesian Programming – Revolving around the Bayes’ Theorem, Bayesian Programming declares the probability of something happening in the future based on past conditions relating to the event.

Analogical Reasoning – Generally, the term analogical indicates non-digital data but when in terms of AI, Analogical Reasoning is the method of drawing conclusions studying the past outcomes. It’s quite similar to stock markets.

Data Mining – It refers to the process of identifying patterns from fairly large data sets with the help statistics, machine learning and database systems in combination.

Decision Tree Learning – Using a decision tree, you can move seamlessly from observing an item to drawing conclusions about the item’s target value. The decision tree is represented as a predictive model, the observation as the branches and the conclusion as the leaves.  

Behavior Informatics (BI) – It is of extreme importance as it helps obtain behavior intelligence and insights.

Case-based Reasoning (CBR) – Generally speaking, it defines the process of solving newer challenges based on solutions that worked for similar past issues.

Feature Extraction – In machine learning, image processing and pattern recognition plays a dominant role. Feature Extraction begins from a preliminary set of measured data and ends up building derived values that intend to be non-redundant and informative – leading to improved subsequent learning and even better human interpretations.

Forward Chaining – Also known as forward reasoning, Forward Chaining is one of two main methods of reasoning while leveraging an inference engine. It is a widely popular implementation strategy best suited for business and production rule systems. Backward Chaining is the exact opposite of Forwarding Chaining.

Genetic Algorithm (GA) – Inspired by the method of natural selection, Genetic Algorithm (GA) is mainly used to devise advanced solutions to optimization and search challenges. It works by depending on bio-inspired operators like crossover, mutation and selection.

Pattern Recognition – Largely dependent on machine learning and artificial intelligence, Pattern Recognition also involves applications, such as Knowledge Discovery in Databases (KDD) and Data Mining.

Reinforcement Learning (RL) – Next to Supervised Learning and Unsupervised Learning, Reinforcement Learning is another machine learning paradigms. It’s reckoned as a subset of ML that deals with how software experts should take actions in circumstances so as to maximize notions of cumulative reward.

Looking for artificial intelligence certification in Delhi NCR? DexLab Analytics is a premier big data training institute that offers in-demand skill training courses to interested candidates. For more information, drop by our official website.

The article first appeared on— www.analyticsindiamag.com/25-ai-terminologies-jargons-you-must-assimilate-to-sound-like-a-pro

 

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A Beginner’s Guide to Learning Data Science Fundamentals

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A Beginner’s Guide to Learning Data Science Fundamentals

I’m a data scientist by profession with an actuarial background.

I graduated with a degree in Criminology; it was during university that I fell in love with the power of statistics. A typical problem would involve estimating the likelihood of a house getting burgled on a street, if there has already been a burglary on that street. For the layman, this is part of predictive policing techniques used to tackle crime. More technically, “It involves a Non-Markovian counting process called the “Hawkes Process” which models for “self-exciting” events (like crimes, future stock price movements, or even popularity of political leaders, etc.)

Being able to predict the likelihood of future events (like crimes in this case) was the main thing which drew me to Statistics. On a philosophical level, it’s really a quest for “truth of things” unfettered by the inherent cognitive biases humans are born with (there are 25 I know of).

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Arguably, Actuaries are the original Data Scientists, turning data in actionable insights since the 18th Century when Alexander Webster with Robert Wallace built a predictive model to calculate the average life expectancy of soldiers going to war using death records. And so, “Insurance” was born to provide cover to the widows and children of the deceased soldiers.

Of course, Alan Turing’s contribution cannot be ignored, which eventually afforded us with the computational power needed to carry out statistical testing on entire populations – thereby Machine Learning was born. To be fair, the history of Data Science is an entire blog of its own. More on that will come later.

The aim of this series of blogs is to initiate anyone daunted by the task of acquiring the very basics of Statistics and Mathematics used in Machine Learning. There are tonnes of online resources which will only list out the topics but will rarely explain why you need to learn them and to what extent. This series will attempt to address this problem adopting a “first principle” approach. Its best to refer back to this article a second time after gaining the very basics of each Topic discussed below:

We will be discussing:

  • Central Limit Theorem
  • Bayes Theorem
  • Probability Theory
  • Point Estimation – MLE’s
  • Confidence Intervals
  • P-values and Significance Test.

This list is by no means exhaustive of the statistical and mathematical concepts you will need in your career as a data scientist. Nevertheless, it provides a solid grounding going into more advanced topics.

Without further due, here goes:

Central Limit Theorem

Central Limit Theorem (CLT) is perhaps one of the most important results in all of Statistics. Essentially, it allows making large sample inference about the Population Mean (μ), as well as making large sample inference about population proportion (p).

So what does this really means?

Consider (X1, X2, X3……..Xn) samples, where n is a large number say, 100. Each sample will have its own respective sample Mean (x̅). This will give us “n” number of sample means. Central Limit Theorem now states:

                                                                                                &

Try to visualise the distribution “of the average of lots of averages”… Essentially, if we have a large number of averages that have been taken from a corresponding large number of samples; then Central Limit theorem allows us to find the distribution of those averages. The beauty of it is that we don’t have to know the parent distribution of the averages. They all tend to Normal… eventually!

Similarly if we were to add up independent and identically distributed (iid) samples, then their corresponding distribution will also tend to a Normal.

Very often in your work as a data scientist a lot of the unknown distributions will tend to Normal, now you can visualise how and more importantly why!

Stay tuned to DexLab Analytics for more articles discussing the topics listed above in depth. To deep dive into data science, I strongly recommend this Big Data Hadoop institute in Delhi NCR. DexLab offers big data courses developed by industry experts, helping you master in-demand skills and carve a successful career as a data scientist.

About the Author: Nish Lau Bakshi is a professional data scientist with an actuarial background and a passion to use the power of statistics to tackle various pressing, daily life problems.

 

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Deep Learning to Boost Ghost Hunting and Paleontology Efforts

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Deep Learning to Boost Ghost Hunting and Paleontology Efforts

Deep leaning technology is taking the world by storm. It is leaving no territory untouched, not even the world of dead! Yes, this robust technology has now started hunting ghosts – for real. Of late, Nature Communication even published a paper highlighting that a ghost population has even contributed to today’s genomes.

With the help of a demographic model structured on deep learning in an Approximate Bayesian Computation framework, it is now possible to delve into the evolutionary history of the Eurasian population in sync with the present-day genetic evidence. Since it is believed that all modern humans have originated Out of Africa, the evolutionary history of the Eurasian population has been identified by introgressions from currently extinct hominins. What’s more, talking about the unknown population, the researchers believe they either trace their roots to Neanderthal-Denisova clade or simply forked early from the Denisova lineage.

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If you want to take a look at the original paper, click here — www.nature.com/articles/s41467-018-08089-7

In addition, the study reflects how the fabulous technology of AI can be leveraged in paleontology. Whether it’s about discovering unpredictable ghosts or unraveling the fading footprints of the whole evolutionary journey, deep learning and AI are taking the bull (paleontology, in this respect) by its horns. According to the paper, researchers studied deep about the evolutionary process of Eurasian population, including past introgression events in OOA (Out of Africa) populations suiting the contemporary genetic evidence and they have produced several simulated evolutionary data, like the total size of ancestral human populations, the exact number of populations, the appropriate time when they branched out from one another, the rate at which they intermixed and so on. Besides, a wide number of simulated genomes for current-day humans have been launched.

The latest and very efficient deep learning method highlights the crucial importance of genomes – they can easily let you know which evolutionary models are most likely to reveal respective genetic patterns. Moreover, if you study closely, you will find that the culture of the entire industry has changed over the past few years. Advanced computers and technology modifications have achieved ‘things’ that were simply impossible with pen and paper a few years back. Perhaps, what’s more interesting is that our perspective of seeing data has changed completely. The potent advances in AI and machine learning have demystified the ways in which algorithms work leading to more concrete shreds of evidence and end-results, which were previously not possible with the age-old traditional methods.

The blog first appeared on— www.analyticsindiamag.com/deep-learning-uncovers-ghosts-in-modern-dna

Are you interested in artificial intelligence certification in Delhi NCR? DexLab Analytics is your go-to institute, which is specialized in imparting in-demand skill training courses. Be it artificial intelligence course, data science certification or Python Spark training, DexLab Analytics excels in all – for more information, contact us today.

 

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AI Jobs: What the Future Holds?

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AI Jobs: What the Future Holds?

Technological revolutions have always been challenging, especially how they influence and impact working landscapes. They either bring on an unforeseen crisis or prove a boon; however, fortunately, the latter has always been the case, starting from the innovation of steam engines to Turing machine to computers and now machine learning and artificial intelligence.

The crux of the matter lies in persistence, perseverance and patience, needed to make these high-end technologies work in the desired way and transform the resources into meaningful insights tapping the unrealized opportunities. Talking of which, we are here to discuss the growth and expansion of AI-related job scopes in the workplace, which is expected to generate around 58 million new jobs in the next couple of years. Are you ready?

Data Analysts

Internet of Things, Machine Learning, Data Analytics and Image Analysis are the IT technologies of 2019. An exponential increase in the use of these technologies is to be expected. Humongous volumes of data are going to be leveraged in the next few years, but for that, superior handling and management skill is a pre-requisite. Only expert consultants adept at hoarding, interpreting and examining data in a meaningful manner can strategically fulfill business goals and enhance productivity.

Interested in Machine Learning course in India? Reach us at DexLab Analytics.

IT Trainers

With automation and machine learning becoming mainstream, there is going to be a significant rise in the number of IT Trainer jobs. Businesses have to appoint these professionals for the purpose of two-way training, including human intelligence as well as machines. On one side, they will have to train AI devices to grasp a better understanding of human minds, while, on the other hand, the objective will be training employees so as to utilize the power of AI effectively subject to their job responsibilities and subject profiles. Likewise, there is going to be a gleaming need for machine learning developers and AI researchers who are equipped to instill human-like intelligence and intuition into the machines – making them more efficient, more powerful.

Man-Machine Coordinators

Agreed or not, the interaction between automated bots and human brainpower will lead to immense chaos – if not managed properly. Organizations have great hope in this man-machine partnership, and to ensure they work in sync with each other, business will seek experts, who can devise incredible roadmaps to tap newbie opportunities. The objective of this job profile is to design and manage an interaction system through which machines and humans can mutually collaborate and communicate their abilities and intentions.

Data Science Machine Learning Certification

Security Analysts

Security is crucial. The moment the world switched from offline to online, a whole lot of new set of crimes and frauds came into notice. To protect and safeguard confidential information and high-profile business identities, companies are appointing skilled professionals who are well-trained in tracking, protecting and recovering AI systems and devices from malicious cyber intrusions and attacks. Thus, skill and expertise in information security, networking and guaranteeing privacy is well-appreciated.

No wonder, a good number of jobs are going to dissolve with AI, but also, an ocean of new job opportunities will flow in with time. You just have to hone your skills and for that, we have artificial intelligence certification in Delhi NCR. In situations like this, these kinds of in-demand skill-training courses are your best bet.

 

The blog has been sourced from ―  www.financialexpress.com/industry/technology/artificial-intelligence-are-you-ready-for-ocean-of-new-jobs-as-many-old-ones-will-vanish/1483437

 


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More than Statistics, Machine Learning Needs Semantics: Explained

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More than Statistics, Machine Learning Needs Semantics: Explained

Of late, machines have achieved somewhat human-like intelligence and accuracy. The deep learning revolution has ushered us into a new era of machine learning tools and systems that perfectly identifies the patterns and predicts future outcomes better than human domain experts. Yet, there exists a critical distinction between man and machines. The difference lies in the way we reason – we, humans like to reason through advanced semantic abstractions, while machines blindly depend on statistics.

The learning process of human beings is intense and in-depth. We prefer to connect the patterns we identify to high order semantic abstractions and our adequate knowledge base helps us evaluate the reason behind such patterns and determine the ones that are most likely to represent our actionable insights.

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On the other hand, machines blindly look for powerful signals in a pool of data. Lacking any background knowledge or real-life experiences, deep learning algorithms fail to distinguish between relevant and specious indicators. In fact, they purely encode the challenges according to statistics, instead of applying semantics.

This is why diverse data training is high on significance. It makes sure the machines witness an array of counterexamples so that the specious patterns get automatically cancelled out. Also, segmenting images into objects and practicing recognition at the object level is the order of the day. But of course, current deep learning systems are too easy to fool and exceedingly brittle, despite being powerful and highly efficient. They are always on a lookout for correlations in data instead of finding meaning.

Are you interested in deep learning? Delhi is home to a good number of decent deep learning training institutes. Just find a suitable and start learning!

How to Fix?

The best way is to design powerful machine learning systems that can tersely describe the patterns they examine so that a human domain expert can later review them and cast their approval for each pattern. This kind of approach would enhance the efficiency of pattern recognition of the machines. The substantial knowledge of humans coupled with the power of machines is a game changer.

Conversely, one of the key reasons that made machine learning so fetching as compared to human intelligence is its quaint ability to identify a range of weird patterns that would look spurious to human beings but which are actually genuine signals worth considering. This holds true especially in theory-driven domains, such as population-scale human behavior where observational data is very less or mostly unavailable. In situations like this, having humans analyze the patterns put together by machines would be of no use.

End Notes

As closing thoughts, we would like to share that machine learning initiated a renaissance in which deep learning technologies have tapped into unconventional tasks like computer vision and leveraged superhuman precision in an increasing number of fields. And surely we are happy about this.

However, on a wider scale, we have to accept the brittleness of the technology in question. The main problem of today’s machine learning algorithms is that they merely learn the statistical patterns within data without putting brains into them. Once, deep learning solutions start stressing on semantics rather than statistics and incorporate external background knowledge to boost decision making – we can finally chop off the failures of the present generation AI.

Artificial Intelligence is the new kid on the block. Get enrolled in an artificial intelligence course in Delhi and kickstart a career of dreams! For help, reach us at DexLab Analytics.

 

The blog has been sourced from ― www.forbes.com/sites/kalevleetaru/2019/01/15/why-machine-learning-needs-semantics-not-just-statistics/#789ffe277b5c

 

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5 Great Takeaways from Machine Learning Conference 2019

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5 Great Takeaways from Machine Learning Conference 2019

Machine Learning Developer Summit, one of the leading Machine Learning conferences of India, happening on the 30th and 31st of January 2019 in Bangalore, aims to assemble machine leaning and data science experts and enthusiasts from all over India. Organized by Analytics India Magazine, this high-level meeting will be the hotspot for conversing about the latest developments in machine learning. Attendees can gather immense knowledge from ML experts and innovators from top tech enterprises, and network with individuals belonging to data sciences. Actually, there are tons of rewards for those attending MLDS 2019. Below are some of the best takeaways:

  1. Creation of Useful Data Lake on AWS

In a talk by reputable Raghuraman Balachandran, Solutions Architect for Amazon Web Services, participants will learn how to design clean, dependable data lakes on AWS cloud. He shall also share his experienced outlook on tackling some common challenges of designing an effective data lake. Mr Balachandran will explain the process to store raw data – unstructured, semi-structured or completely structured – and processed data for different analytical uses.

Data lakes are the most used architectures in data-based companies. This talk will allow attendees to develop a thorough understanding of the concept, which is sure to boost their skill set for getting hired.

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  1. Improve Inference Phase for Deep Learning Models

Deep learning models require considerable system resources, including high-end CPUs and GPUs for best possible training. Even after exclusive access to such resources, there may be several challenges in the target deployment phase that were absent in the training environment.

Sunil Kumar Vuppala, Principal Scientist at Philips Research, will discuss methods to boost the performance of DL models during their inference phase. Further, he shall talk about using Intel’s inference engine to improve quality of DL models run in Tensorflow/Caffe/Keras via CPUs.

  1. Being more employable amid the explosive growth in AI and its demand

The demand for AI skills will skyrocket in future – so is the prediction of many analysts considering the extremely disruptive nature of AI. However, growth in AI skills isn’t occurring at the expected rate. Amitabh Mishra, who is the CTO at Emcure Pharmaceuticals, addresses the gap in demand and development of AI skills, and shall share his expert thoughts on the topic. Furthermore, he will expand on the requirements in AI field and provide preparation tips for AI professionals.

  1. Walmart AI mission and how to implement AI in low-infrastructure situations

In the talk by Senior Director of Walmart Lab, Prakhar Mehrotra, audiences get a view of Walmart’s progress in India. Walmart Lab is a subsidiary of the global chain Walmart, which focuses on improving customer experience and designing tech that can be used with Merchants to enhance the company’s range. Mr Mehrotra will give details about Wallmart’s AI journey, focusing on the advancements made so far.

  1. ML’s important role in data cleansing

A good ML model comes from a clean data lake. Generally, a significant amount of time and resources invested in building a robust ML model goes on data cleansing activities. Somu Vadali, Chief of Future Group’s CnD Labs Data and Products section, will talk about how ML can be used to clean data more efficiently. He will speak at length about well-structured processes that allow organizations to shift from raw data to features in a speedy and reliable manner. Businesses may find his talk helpful to reduce their time-to-market for new models and increase efficiency of model development.

Machine learning is the biggest trend of IT and data science industry. In fact, day by day it is gaining more prominence in the tech industry, and is likely to become a necessary skill to get bigger in all fields of employment. So, maneuver your career towards excellence by enrolling for machine learning courses in India. Machine learning course in Gurgaon by DexLab Analytics is tailor-made for your specific needs. Both beginners and professionals find these courses apt for their growth.

 

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Being a Statistician Matters More, Here’s Why

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Being a Statistician Matters More, Here’s Why

Right data for the right analytics is the crux of the matter. Every data analyst looks for the right data set to bring value to his analytics journey. The best way to understand which data to pick is fact-finding and that is possible through data visualization, basic statistics and other techniques related to statistics and machine learning – and this is exactly where the role of statisticians comes into play. The skill and expertise of statisticians are of higher importance.

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Below, we have mentioned the 3R’s that boosts the performance of statisticians:

Recognize – Data classification is performed using inferential statistics, descriptive and diverse other sampling techniques.

Ratify – It’s very important to approve your thought process and steer clear from acting on assumptions. To be a fine statistician, you should always indulge in consultations with business stakeholders and draw insights from them. Incorrect data decisions take its toll.

Reinforce – Remember, whenever you assess your data, there will be plenty of things to learn; at each level, you might discover a new approach to an existing problem. The key is to reinforce: consider learning something new and reinforcing it back to the data processing lifecycle sometime later. This kind of approach ensures transparency, fluency and builds a sustainable end-result.

Now, we will talk about the best statistical techniques that need to be applied for better data acknowledgment. This is to say the key to becoming a data analyst is through excelling the nuances of statistics and that is only possible when you possess the skills and expertise – and for that, we are here with some quick measures:

Distribution provides a quick classification view of values within a respective data set and helps us determine an outlier.

Central tendency is used to identify the correlation of each observation against a proposed central value. Mean, Median and Mode are top 3 means of finding that central value.

Dispersion is mostly measured through standard deviation because it offers the best scaled-down view of all the deviations, thus highly recommended.

Understanding and evaluating the data spread is the only way to determine the correlation and draw a conclusion out of the data. You would find different aspects to it when distributed into three equal sections, namely Quartile 1, Quartile 2 and Quartile 3, respectively. The difference between Q1 and Q3 is termed as the interquartile range.

While drawing a conclusion, we would like to say the nature of data holds crucial significance. It decides the course of your outcome. That’s why we suggest you gather and play with your data as long as you like for its going to influence the entire process of decision-making.

On that note, we hope the article has helped you understand the thumb-rule of becoming a good statistician and how you can improve your way of data selection. After all, data selection is the first stepping stone behind designing all machine learning models and solutions.

Saying that, if you are interested in learning machine learning course in Gurgaon, please check out DexLab Analytics. It is a premier data analyst training institute in the heart of Delhi offering state-of-the-art courses.

 

The blog has been sourced from — www.analyticsindiamag.com/are-you-a-better-statistician-than-a-data-analyst

 

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The Soaring Importance of Apache Spark in Machine Learning: Explained Here

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The Soaring Importance of Apache Spark in Machine Learning: Explained Here

Apache Spark has become an essential part of operations of big technology firms, like Yahoo, Facebook, Amazon and eBay. This is mainly owing to the lightning speed offered by Apache Spark – it is the speediest engine for big data activities. The reason behind this speed: Rather than a disk, it operates on memory (RAM). Hence, data processing in Spark is even faster than in Hadoop.

The main purpose of Apache Spark is offering an integrated platform for big data processes. It also offers robust APIs in Python, Java, R and Scala. Additionally, integration with Hadoop ecosystem is very convenient.

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Why Apache Spark for ML applications?

Many machine learning processes involve heavy computation. Distributing such processes through Apache Spark is the fastest, simplest and most efficient approach. For the needs of industrial applications, a powerful engine capable of processing data in real time, performing in batch mode and in-memory processing is vital. With Apache Spark, real-time streaming, graph processing, interactive processing and batch processing are possible through a speedy and simple interface. This is why Spark is so popular in ML applications.

Apache Spark Use Cases:

Below are some noteworthy applications of Apache Spark engine across different fields:

Entertainment: In the gaming industry, Apache Spark is used to discover patterns from the firehose of real-time gaming information and come up with swift responses in no time. Jobs like targeted advertising, player retention and auto-adjustment of complexity levels can be deployed to Spark engine.

E-commerce: In the ecommerce sector, providing recommendations in tandem with fresh trends and demands is crucial. This can be achieved because real-time data is relayed to streaming clustering algorithms such as k-means, the results from which are further merged with various unstructured data sources, like customer feedback. ML algorithms with the aid of Apache Spark process the immeasurable chunk of interactions happening between users and an e-com platform, which are expressed via complex graphs.

Finance: In finance, Apache Spark is very helpful in detecting fraud or intrusion and for authentication. When used with ML, it can study business expenses of individuals and frame suggestions the bank must give to expose customers to new products and avenues. Moreover, financial problems are indentified fast and accurately.  PayPal incorporates ML techniques like neural networks to spot unethical or fraud transactions.

Healthcare: Apache Spark is used to analyze medical history of patients and determine who is prone to which ailment in future. Moreover, to bring down processing time, Spark is applied in genomic data sequencing too.

Media: Several websites use Apache Spark together with MongoDB for better video recommendations to users, which is generated from their historical data.

ML and Apache Spark:

Many enterprises have been working with Apache Spark and ML algorithms for improved results. Yahoo, for example, uses Apache Spark along with ML algorithms to collect innovative topics than can enhance user interest. If only ML is used for this purpose, over 20, 000 lines of code in C or C++ will be needed, but with Apache Spark, the programming code is snipped at 150 lines! Another example is Netflix where Apache Spark is used for real-time streaming, providing better video recommendations to users. Streaming technology is dependent on event data, and Apache Spark ML facilities greatly improve the efficiency of video recommendations.

Spark has a separate library labelled MLib for machine learning, which includes algorithms for classification, collaborative filtering, clustering, dimensionality reduction, etc. Classification is basically sorting things into relevant categories. For example in mails, classification is done on the basis of inbox, draft, sent and so on. Many websites suggest products to users depending on their past purchases – this is collaborative filtering. Other applications offered by Apache Spark Mlib are sentiment analysis and customer segmentation.

Conclusion:

Apache Spark is a highly powerful API for machine learning applications. Its aim is wide-scale popularity of big data processing and making machine learning practical and approachable. Challenging tasks like processing massive volumes of data, both real-time and archived, are simplified through Apache Spark. Any kind of streaming and predictive analytics solution benefits hugely from its use.

If this article has piqued your interest in Apache Spark, take the next step right away and join Apache Spark training in Delhi. DexLab Analytics offers one the best Apache Spark certification in Gurgaon – experienced industry professionals train you dedicatedly, so you master this leading technology and make remarkable progress in your line of work.

 

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