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Application of Harmonic Mean using R and Python

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Application of Harmonic Mean using R and Python

Harmonic mean, for a set of observations is the number of observations divided by the sum of the reciprocals of the values and it cannot be defined if some of the values are zero.

This blog is in continuation with STATISTICAL APPLICATION IN R & PYTHON: CHAPTER 1 – MEASURE OF CENTRAL TENDENCY. However, here we will discover Harmonic mean and its application using Python and R.

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Application:

A milk company sold milk at the rates of 10,16.5,5,13.07,15.23,14.56,12.5,12,30,32, 15.5, 16 rupees per liter in twelve different months (January-December), If an equal amount of money is spent on milk by a family in the ten months. Calculate the average price in rupees per month.

Table for the problem:

Month

Rates (Rupees/Liter)

January

10

February

16.5

March

5

April

13.07

May

15.23

June

14.56

July

12.5

August

12

September

30

October

32

November

15.5

December

16

Calculate Harmonic Mean in R:-

So, the average rate of the milk in rupees/liter is 12.95349 = 13 Rs/liter (Approx)

We get this answer from the Harmonic Mean, calculated in R.

Calculate Harmonic Mean in Python:-

First, make a data frame of the available data in Python.

Now, calculate the Harmonic mean from the following data frame.

So, the average rate of the milk in rupees/liter is 12.953491609077956 = 13 Rs/Liter (Approx)

We get this answer from Harmonic mean, calculated in Python.

Summing it Up:

In this data, we have a few large values which are putting an effect on the average value, if we calculate the average in Arithmetic mean, but in Harmonic mean, we get a perfect average from the data, and also for calculating the average rate.

Use of Harmonic mean is very limited. Harmonic mean gives the largest value to the smallest item and smallest value to the largest item.

Where there are a few extremely large or small values, Harmonic mean is preferable to Arithmetic mean as an average.

The Harmonic mean is mainly useful in averages involving time, rate & price.

Deep Learning and AI using Python

Note – If you want to learn the calculation of Geometric Mean, you can check our post on CALCULATING GEOMETRIC MEAN USING R AND PYTHON.

Dexlab Analytics is a peerless institute for Python Certification Training in Delhi. Therefore, for tailor-made courses in Python, Deep Learning, Machine Learning, Neural Networks, reach us ASAP!

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The Rising Popularity of Python in Data Science

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The Rising Popularity of Python in Data Science

Python is the preferred programming language for data scientists. They need an easy-to-use language that has decent library availability and great community participation. Projects that have inactive communities are usually less likely to maintain or update their platforms, which is not the case with Python.

What exactly makes Python so ideal for data science? We have examined why Python is so prevalent in the booming data science industry — and how you can use it for in your big data and machine learning projects.

Deep Learning and AI using Python

Why Python is Dominating?

Python has long been known as a simple programming language to pick up, from a syntax point of view, anyway. Python also has an active community with a vast selection of libraries and resources. The result? You have a programming platform that makes sense of how to use emerging technologies like machine learning and data science.

Professionals working with data science applications don’t want to be bogged down with complicated programming requirements. They want to use programming languages like Python and Ruby to perform tasks in a hassle-free way.

Ruby is excellent for performing tasks such as data cleaning and data wrangling, along with other data pre-processing tasks. However, it doesn’t feature as many machine learning libraries as Python. This gives Python the edge when it comes to data science and machine learning.

Python also enables developers to roll out programs and get prototypes running, making the development process much faster. Once a project is on its way to becoming an analytical tool or application, it can be ported to more sophisticated languages such as Java or C, if necessary.

Newer data scientists gravitate toward Python because of its ease of use, which makes it accessible.

Why Python is Ideal for Data Science?

Data science involves extrapolating useful information from massive stores of statistics, registers, and data. These data are usually unsorted and difficult to correlate with any meaningful accuracy. Machine learning can make connections between disparate datasets but requires serious computational sophistry and power.

Python fills this need by being a general-purpose programming language. It allows you to create CSV output for easy data reading in a spreadsheet. Alternatively, more complicated file outputs that can be ingested by machine learning clusters for computation.

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Consider the Following Example:

Weather forecasts rely on past readings from a century’s worth of weather records. Machine learning can help make more accurate predictive models based on past weather events. Python can do this because it is lightweight and efficient at executing code, but it is also multi-functional. Also, Python can support object-orientated and functional styles, meaning it can find an application anywhere.

There are now over 70,000 libraries in the Python Package Index, and that number continues to grow. As previously mentioned, Python offers many libraries geared toward data science. A simple Google search reveals plenty of Top 10 Python libraries for data science lists. Arguably, the most popular data analysis library is an open-source library called pandas. It is a high-performance set of applications that make data analysis in Python a much simpler task.

No matter what data scientists are looking to do with Python, be it predictive causal analytics or prescriptive analytics, Python has the toolset to perform a variety of powerful functions. It’s no wonder why data scientists embrace Python.

If you are interested in Python Certification Training in Delhi, drop by DexLab Analytics. With a team of expert consultants, we provide state-of-the-art Machine Learning Using Python training courses for aspiring candidates. Check out our course itinerary for more information.

 

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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.

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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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Now Machine Learning Can Predict Premature Death, Says Research

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Now Machine Learning Can Predict Premature Death, Says Research

Machine Learning yet again added another feather in its cap; a team of researchers tried and tested a suave machine learning system that can now predict early death. Yes, premature death can now be estimated, courtesy a robust technology and an outstanding panel of researchers from the University of Nottingham! At first, it may sound weird and something straight out of a science fiction novel, but fret not – machine learning has proved itself in improving the status of preventive healthcare and now it’s ready to venture into new unexplored medical territories.

Prediction at Its Best

Published in PLOS ONE in one of their special editions of Machine Learning in Health and Biomedicine, the study delves into how myriad AI and ML tools can be leveraged across diverse healthcare fields. The technology of ML is already reaping benefits in cancer detection, thanks to its sophisticated quantitative power. These new age algorithms are well-equipped to predict death risks of chronic diseases way ahead of time from a widely distributed middle-aged population.

To draw clear conclusions, the team collected data of more than half a million people falling within the age group of 40 and 69 from the UK Biobank. The data collection is from the period 2006-2010, followed up till 2016. With this data in tow, the experts analyze biometric, demographic, lifestyle and clinical factors in each individual subject. Robust machine learning models are used in the process.

Adding in, the team observed dietary consumption of vegetables, fruits and meat per day of each subject. Later, the team from Nottingham University proceeded to predict the mortality of these individuals.

“We mapped the resulting predictions to mortality data from the cohort, using Office of National Statistics death records, the UK cancer registry and ‘hospital episodes’ statistics,” says Dr. Stephen Weng, assistant professor of Epidemiology and Data Science.  “We found machine-learned algorithms were significantly more accurate in predicting death than the standard prediction models developed by a human expert.”

Accuracy and Outcome

The researchers involved in this ambitious project are excited to the bones. They are eager about the outcomes. They are in fact looking forward to a time where medical professionals would be able to distinguish potential health hazards in patients with on-point accuracy and evaluate the following steps that would lead the way towards prevention. “We believe that by clearly reporting these methods in a transparent way, this could help with scientific verification and future development of this exciting field for health care”, shares Dr. Stephen Weng.

As closing thoughts, the research is expected to build the foundation of enhanced medicine capabilities and deliver customized healthcare facilities tailoring risk management for each individual patient. The Nottingham research draws inspiration from a similar study where machine learning techniques were used to predict cardiovascular diseases.

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In case, you are interested in Machine Learning Using Python training course, DexLab Analytics is the place to be. With a volley of in-demand skill training courses, including Python certification training and AI training, we are one of the best in town. For details, check out our official website RN.

 
The blog has been sourced from —
interestingengineering.com/machine-learning-algorithms-are-now-able-to-predict-premature-death
 


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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

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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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