Market researchers usually have a hard time preparing and conducting surveys to gather valuable data on customers as well as competitors and sorting that data to offer actionable Intel to marketing heads to enable them to devise marketing strategies accordingly.
So, basically their job centers around data, data, and more data, it is solely reason enough for them to consider ditching their jaded and often erroneous data collecting system and step into the domain of AI by pursuing customer market analysis courses.
How Market Researchers View AI
Despite recognizing and appreciating the manifold advantages of AI, some uneasiness lingers in most industries regarding its applications and full integration.
However, market researchers were thankfully, quick to recognize the pros the AI package has to offer. The implementation of AI would take most of the hassle way from their job. Surveys conducted over the past couple of years show that almost 80% of market researchers are in favor of AI and they believe that it will be the harbinger of new opportunities.
How AI Can Address the Woes of Market Researchers?
Any conversation with a seasoned market researcher would reveal the issues that continuously bug them. A huge amount of time and money that go into the process along with the enormity of data they have to process, can lead to frustration.
With rampant consumerism becoming a reality of modern society, the market researchers would need to deal with an even bigger amount of data.
Some agencies have already started migrating to AI solutions to address these concerns and they are seeing the difference already. Machine Learning Using Python is becoming a trend which they are veering towards.
Although companies are still holding back from fully integrating AI into their operations, in near future the scenario might change considering the benefits AI can offer
Shortening data processing time: Usually, it takes months to complete projects, but, with AI-powered tools, the duration of the projects can be shortened. Data processing and producing insight based upon data findings can be almost instantaneous.
It would enable the marketing heads to tackle issues faster than ever before, thus gaining an edge over the competition. Report generation would take less time and the team can be employed to handle some other productive tasks.
Higher efficiency in data handling: Any organization has to deal with a huge amount of data, as every year new data gets added to their database. However, when it comes to market research, mostly experience data is put to use while operational data which is mainly old data is shifted to the backburner. AI integration can solve this issue by combining both data sets, and combing through this massive data to produce insights and find new patterns in customer behavior which was earlier missed by your team.
Improve survey quality:
Implementation of AI can take the errors out of the present survey system and make it more efficient. Using ML and NLP, survey questions can be shaped in a conversational style which would immediately put the respondents in a comfort zone thereby improving the chance of eliciting an accurate response. AI-powered tools can also put together a list of respondents who are apt for the survey and eliminating random ones out of the list.
Customize marketing strategy: As AI gets integrated into your market research process, it starts analyzing customer behavior by going through the bounce rate, as well as login data. As a result, it points out those customer, marketing teams need to work on. They get a chance to customize a marketing strategy for them.
There is no denying the fact that AI can transform the way market researchers work. Integration and adoption of AI would certainly take time but, eventually, the merger would happen. Since AI is a highly specialized domain, the market research teams need to upgrade themselves. Enrolling in a premier artificial intelligence certification in delhi ncr, would help them be prepared for a smarter future.
The digitization of modern classrooms is an indicator of the fact that educators have decided to adopt tech to make learning a more rewarding experience for their students. In fact, some educators are making an effort to upgrade themselves with advanced courses like deep learning for computer vision with python.
But, not all children in a classroom can blend in with their classmates with ease, they have difficulty understanding teacher’s instructions or, even following what is written in the textbooks.
Yes, we are talking about children with disabilities, who cannot be taught in a typical classroom setting and who need special attention and today we are going to learn whether the transformative power of AI can make a difference in the lives of these differently-abled children.
How do we classify children who need special education
Children who have been detected with some form of physical, cognitive or learning disabilities such as ASD- Autism spectrum disorder, Dyslexia, hearing, or visual impairment, require a learning environment that is specifically designed for them.
What are the challenges they face in a traditional setting?
Usually, these children have problems coping with the traditional learning environment. They find it difficult to follow instructions, lessons, even their textbooks. They need personal attention from the teacher, and are unable to handle the pressure of competition.
What’s more, these children usually are subjected to bullying from other students, which can further discourage them. they need special tools, and lessons that are designed for them. These children also have a short attention span so, the reading material might not interest them.
How AI can be of help?
The introduction of AI in special education has opened up new avenues for both parents and teachers. Data Science training has enabled professionals to analyze student data to identify problem areas. There have been several studies on this and the findings are promising. With the application of AI-based methods, it is possible to help these children exercise more freedom as it allows them to learn at their own pace.
The Application of AI in special education is aimed to serve a dual purpose.
1) AI-based tools are used to detect disability
2) AI-based learning tools and methods are being adopted to aid learning
Now let’s take a brief look at how this dual application of AI is taking place and with what effect
1) AI-based tools are used to detect disability
Sometimes identifying a problem can lead you to the right solution. Usually, children who show apathy to learning or, are very slow to pick up lessons, treated as dumb. They are bullied and teachers keep complaining that they do not make an effort. But, what they fail to realize is that the child might have a learning disability.
But, now we have AI-enabled testing systems in place to detect children with learning disabilities.
In the year 2008, a model was introduced to diagnose autism with the help of artificial neural networks technique. Even before this way back in 2003, a fuzzy cognitive map approach was taken to diagnose SLI or, speech-language impairment. Other systems involving artificial neural network techniques were developed to detect issues like dysgraphia, dyslexia.
More data based research work on this is required. This would definitely create a demand for professionals who have undergone specialized training on neural network course python.
2) AI-based learning tools and methods are being adopted to aid learning
Children with special needs must have access to special learning tools that would make learning easier. There has been some considerable progress in this field.
Children with Autism have problems with both verbal and nonverbal communication. Developing social skills can be a challenge for them. To address this issue QTrobot was designed. This humanoid social robot is programmed to teach autistic children social skills. Other examples would be NAO robot, virtual assistant Siri which help children with ASD acquire more social skills.
ActiveMath employs Artificial Intelligence techniques to ensure children can have more freedom in selecting a convenient learning environment.
Spontaneous participation of the learner is essential for any learning process to be effective. If a child is having trouble concentrating then, he won’t learn anything. Smart Tutoring model was developed to evaluate the concentration level as well as the mood of the child, to develop a learning session that suits that particular child.
Widex’s Evoke is an intelligent hearing aid powered with AI technology which can help hearing-impaired children attend classes without any difficulty.
AI has the potential to transform the special education sector with advanced learning aids, specifically designed for differently-abled students. The field is expanding and it needs more research and collaboration among educators, app developers, engineers to come up with smarter solutions.
AI is a dynamic field that is constantly evolving thanks to the continuous stream of research work being conducted. The field is being reshaped by emerging trends. In order to keep pace with this fast-moving technology, especially if you are pursuing Data Science training you should learn about the latest trends that are going to dominate this field.
Digital Data Forgetting
It is a curious trend to watch out for as instead of learning data, unlearning would take precedence. In machine learning data is fed to the system based on which it makes predictive analysis. However, thanks to the growing channels and activities the amount of data generated is increasing, and a significant portion of which might not even be required and which only contribute to creating noise.
Although it is possible to store the data utilizing cloud-based systems, the price an organization will have to bear for unnecessary data, does not justify the decision. Furthermore, it might also raise privacy risks in the future. The efficient handling of this data lineage issue requires systems that will forget unnecessary data so that it can proceed with what is important.
NLP
Now that chatbots are being put to use to provide better customer support, the significance of NLP or, natural language processing is only going to increase. NLP is all about analyzing and processing speech patterns. There is now a shift towards developing language models around the concepts of pre-training and fine-tuning and further research work is being conducted to make these systems even more efficient, however, the focus on transfer learning might lessen considering the financial and operational complications involved in the process.
Reinforcement Learning
This is another trend to look out for, reinforcement learning is where a model or system learning involves a preset goal and is met with reward or, punishment depending upon the outcome. This particular trend might push AI to a whole new level. In RL, the learning activity is somewhat random and the system has to rely on the experience it has gained and continues to learn by repeating what it has learned, and as it starts recognizing rewards it continues working towards it until the learning takes a logical turn. Research works are being conducted to make this process more sophisticated.
Automated Machine Learning
If you are aware of Google AutoML, then you already have an inkling of what AutoML is. It basically focuses on the end-to-end process and automates it. It applies a number of techniques including RL, to reach a higher level of accuracy. It works on raw data and processes it to suggest a solution that is most appropriate. It basically is a lifesaver for those who are not familiar with ML. However, there are programs available that enable professionals pursue Machine Learning Using Python who are looking to gain expertise in this field.
Internet of Things
IOT devices are a rage and they are able to collect a huge amount of user data that needs to be processed to gather valuable information. However, there could be certain challenges involved in the data collection process which lead to error. The application of ML in this particular field can not only lend more efficiency to the way IOT operates but it can also process a large amount of data to offer actionable insight. The information filtered this way could help develop efficient models for businesses and various other sectors. The merger of IOT and ML is definitely a trend that is definitely going to be revolutionary.
AI technology is getting more sophisticated with emerging trends. The manifold application of AI is opening up new career avenues. Enrolling in a premier artificial intelligence training institute in Gurgaon, would be a good career move for anybody looking forward to having a career in this domain.
With more and more sectors turning to AI to find real-time solutions, it is no wonder that AI would gain momentum in the field of credit risk management as well. AI adds efficiency to the process by offering an insight into the portfolios of potential borrowers, which was not available to financial firms up until now. This trend is pushing corporate houses to sign up for credit risk analytics training.
Let’s have a look at credit risk management and figure out how AI can play a key role in building the perfect model.
What is credit risk management
Banks and financial firms lend money to individuals as well as businesses, now credit risk refers to the uncertainty arising due to that borrower, delaying or failing to pay the amount borrowed along with interest resulting in the bank losing money. Remember that infamous recession of 2008?
Credit risk management is about mitigating the risk factor in the process. It involves identifying, analyzing, and measuring the risk factors to keep the risk at a minimal level, or, eliminating the risk if possible.
How AI features in credit risk management
In order to eliminate risk, the bank needs to identify the risk factors, which means going through data, mainly regarding the borrower’s financial activities, portfolio to decide whether that particular individual or, a commercial firm would be able to pay the money back before lending can happen.
But, the process needs to be as much error-free as possible, because while analyzing the portfolios any mistake could lead to failure to recognize a potential defaulter, or, might result in rejecting an applicant who could have been a valuable customer in future. Credit Risk Modelling Courses are being developed to train professionals to deal with this highly specialized task.
AI and especially its subset Machine Learning come into this picture, due to the massive amount of structured and unstructured data involved in the process. The traditional methodology applied by financial institutes is not error-free. processing a huge amount of raw data and identifying patterns is a job that is better handled by AI.
The benefits AI bring to the table
Despite financial firms implementing all sorts of solutions available to them, achieving efficiency in credit risk management has remained a challenge for them due to not having access to smart risk assessment tools, fault in the data management procedure. This is primarily the reason why AI is now being incorporated in the process to achieve better results. With Artificial Neural Networks, Random Forest in place, sorting through loan applications and portfolios to process valuable data and finding patterns becomes easier. Undergoing credit risk modelling certification is almost mandatory for any individual looking forward to having a career in this field. Here are the benefits AI has to offer
Data quality: When traditional models are employed they fail to deal with the issue of data quality which for any financial institute could be a big problem. But with Machine learning detecting any oddity in the data entry is easy. Another fact is that detecting complex patterns from diverse data sets to analyze risk factors is essential which traditional models are not equipped to perform.
Segmentation is better: While analyzing customer portfolios, AI could help in introducing smart segmentation solutions to gain a deep insight into their profiles, thus ensuring efficient risk recognition.
Automated process: Usually organizations have to put together a team for dealing with data handling and report generation tasks, but AI can automate the whole process and minimize the chances of human error while allowing the organizations to set people free to deal with other vital work. Automation would also lead to faster loan processing.
Intuitive analysis guarantees accuracy: Usually, traditional models are somewhat rigid, due to functioning as per set guidelines. However, with AI the analysis gets intuitive and as it continues to wade through new data sets, it continues to learn and come up with more accurate predictions.
Credit risk management will continue to be a key area for financial firms in the future as well, given the present circumstances, the risk factor would only grow. So, it is time for this sector to recognize and embrace the potential of AI in mitigating the risk.
Machine learning is a subset of Artificial Intelligence, or, AI which draws from its past experiences to predict future action and act on it. The growing demand for Machine Learning course in Gurgaon, is a clear pointer to the growth the field is experiencing.
If you have been on Youtube frequently then you would certainly have noticed, how it recognizes the choices you made during your last visit and it suggests results based on those past interactions.
The world of machine learning is way past its nascent stage and has found several avenues where its application has become manifold over the years. From predictive analysis to pattern recognition systems, Machine learning is being put to use for finding an array of solutions.
AWS has been a pioneer in the field as it embraced the technology almost 20 years back, recognizing its potential growth across all business verticals.
At a recently held online tech conference, vice president of Amazon AI shared his concerns and ideas regarding the journey of ML while pointing out the hurdles still in the way and which need to be addressed. Here are the key takeaways from the discussion
Growing need for Machine learning
Amazon was quick to realize a crucial fact in the very beginning that consumer experience is a crucial aspect of business which needs to get better with the application of ML.
Despite the impressive trajectory of machine learning and its growing application across different fields there are still issues which pose serious challenge. There are certain issues which if tackled properly would pave the way for a smarter future for all.
Get your data together
Businesses intent on building a machine learning strategy need to understand that they are missing a vital component of the model which is the data itself. Setting out business objectives is not enough; machine learning model is basically built upon data. You need to feed the model data, accumulated over a period of time which it could analyze and to predict future action.
Clarity regarding machine learning application
It is understood that you need to apply machine learning in order to find solutions, to do that you need to identify that particular area of your business where you need the solution. Once you have done that, you need clarity regarding data backup, applicability and impact on business. Swami Sivasubramaniam, vice president of Amazon AI at Amazon Web Services referred to these aspects as “three dimensions”.
Another point he stressed was regarding a collaboration between domain experts and machine learning teams.
Dearth of skill
Although there has been a quantum growth in the application of machine learning, there is a significant lack of trained personnel for handling machine learning models. Undergoing a Machine Learning course in Gurgaon, could bridge the skill gap.
Since, this sector is poised to grow, people willing to make a career should consider undergoing training.
In fact, organizations looking to implement machine learning model, should send their employees for corporate training programs offered at a premier MIS Training Institute in Delhi NCR.
Avoid undifferentiated heavy lifting
Most companies tend to shift their focus from the job at hand and according to Sivasubramaniam, starts dealing with issues like “server hosting, bandwidth management, contract negotiation…”, when they should only be concerned with making the model work for their business model and should look for cloud-based solutions for handling the rest of the issues.
Addressing these issues would only pave the way towards a brighter future where Machine learning would become an integral part of every business model.
In the pages of sci-fi, we find a world that is completely driven by AI, but, we no longer have to wish for a dystopian world to become a reality to experience AI firsthand. Our world as we know it is gradually being reshaped by the powerful presence of AI. From your Smartphone to virtual assistants, AI is taking slow but firm strides which is evident from the high demand for courses like artificial intelligence certification in delhi ncr.
So, when everything is being impacted by AI, why should your business lag behind? You would be surprised to learn the incredible changes AI could bring to the table.
Be it managing administrative tasks or, ensuring data security, AI could streamline operations and add efficiency to every task that needs to be performed to ensure zero error. Here is how you can make the difference
Implement AI to smoothen your HR practices: Managing your employees and recruiting new ones are essential tasks for any organization. Your HR department can be more efficient with their tasks at hand with the help of machine learning. Dealing with scores of applications while recruiting new faces can be a mundane task. But, now processing applications and sorting out the ones that match the criterion gets easier and so does the entire hiring process. Throw in chatbots in the interview process to ensure that you select the right candidates.
Lessen errors during manufacturing: During the manufacturing process, oftentimes one or, two faulty products end up spoiling the image of the brand and most importantly you end up spending time, money, and personnel for return and refund procedure. To err is human but, with AI coming into play, any kind of glitch could easily be detected and prevented at an early stage. This would ensure that only quality products are ending up in the showrooms.
Customize marketing strategies: It is so hard to guess what your customers want and even more difficult to chalk out a marketing plan around that guesswork. However, advanced analytics could help you keep a track of consumer behavior because it is purely based on data. So, now that you have eliminated the guesswork, you can come up with a marketing strategy that actually works to entice customers.
Provide customer service round the clock: While running your business you must have noticed how difficult and essential it is to stay in touch with constant queries. People who are buying from you might have umpteen number of queries and you need a big team to handle that. However, despite your best efforts, some queries go unanswered, this might create a negative impact on the customers. So, why not take the help of chatbots who can handle all the primary queries round the clock and provide customer satisfaction? Critical queries could be handed over to a support team with specialized knowledge.
You can do more with AI-powered tools and systems to make your business grow. The world is moving with AI towards a brighter future and, it is time for you to welcome and embrace its power.
Here is taking an in-depth look at how sampling distribution works along with a discussion on various types of the sampling distribution. This is a continuation of the discussion on Classical Inferential Statistics that focused on the theory of sampling, breaking it down to building blocks of classical sampling theory along with various kinds of sampling. You can read part 1 of the article here
The sampling distribution is a probability distribution of statistics obtained from a large number of samples drawn from a specific population. And the types of sampling distribution are- (i) Gamma Distribution, (ii) Beta Distribution, (iii) Chi-Square Distribution, (iv) Exponential Distribution, (v) T-Distribution & (vi) F-Distribution.
The key components for describing all these distributions are – (i) Probability Density Function – Which is the function whose integral is to be calculated to find probabilities associated with a continuous random variable and their shape(graph) for the same, (ii) Moment Generating Function – which helps to find the moment of those distributions, and (iii) Degrees of Freedom – It refers to the number of independent sample points and compute a static minus the number of parameters explained from the sample.
For Gamma and Beta distribution, we will discuss gamma and beta function and relation between them etc.
2. Probability Density Function, Moment Generating Function, Sampling Distribution, Degrees of Freedom
PROBABILITY DENSITY FUNCTION
Probability density function (PDF), in statistics, is a function whose integral is calculated to find probabilities associated with a continuous random variable (see continuity; probability theory). Its graph is a curve above the horizontal axis that defines a total area, between itself and the axis, of 1. The percentage of this area included between any two values coincides with the probability that the outcome of an observation described by the probability density function falls between those values. Every random variable is associated with a probability density function (e.g., a variable with a normal distribution is described by a bell curve). If X be continuous Random variable taking any continuous real values then f(x) is a probability density function if:-
Moment Generating Function
The moment generating function (m.g.f.) of a random variable X (about origin) having the probability function f(x) is given by:
The integration of summation being extended to the entire range of x, t being the real parameter and it is being assumed that the right-hand side of the equation is absolutely convergent for some positive number h such that –h<t<h.
SAMPLING DISTRIBUTION
It may be defined as the probability law which the statistic follows if repeated random samples of a fixed size are drawn from a specified population. A number of samples, each of size n, are taken from the same population and if for each sample the values of the statistic are calculated, a series of values of the statistic will be obtained. If the number of samples is large, these may be arranged into a frequency table. The frequency distribution of the statistic that would be obtained if the number of samples, each of the same size (say n), were infinite is called the Sampling distribution of the statistic
DEGREES OF FREEDOM
The term degrees of freedom (df) refers to the number of independent sample points used to compute a statistic minus the number of parameters estimated from the sample points: For example, consider the sample estimate of the population variance (s2)
Where is the score for observation i in the sample, X ̅ is the sample estimate of the population mean, n is the number of observation in the sample. The formula is based on n independent sample points and one estimated population parameter (x ̅). Therefore, the number of degrees of freedom is n minus one. For this example
df=n-1
3. Gamma Function, Beta Function, Relation between Gamma &Beta Function
GAMMA FUNCTION
The Gamma function is defined for x>0 in integral form by the improper integral known as Euler’s integral of the second kind.
Many probability distributions are defined by using the gamma function, such as gamma distribution, beta distribution, chi-squared distribution, student’s t-distribution, etc. For data scientists, machine learning engineers, researchers, the Gamma function is probably one of the most widely used functions because it is employed in many distributions.
BETA FUNCTION
The Beta function is a function of two variables that is often found in probability theory and mathematical statistics.
The Beta function is a function B: R_(++)^2→R defined as follows:
There is also a Euler’s integral of the first kind.
For example, as a normalizing constant in the probability density functions of the F distribution and of the Student’s t distribution
RELATION BETWEEN GAMMA AND BETA FUNCTION
In the realm of Calculus, many complex integrals can be reduced to expressions involving the Beta Function. The Beta Function is important in calculus due to its close connection to the Gamma Function which is itself a generalization of the factorial function.
We know,
So, the product of two factorials as
Now apply the changes of variables t=xy and s=x(1-y) to this double integral. Note that t + s = x and that 0 < t < ∞ and 0 < x < ∞ and 0 < y < 1. The jacobian of this transformation is
Since x > 0 we conclude that Hence we have
Therefore,
4. Gamma Distribution
The gamma distribution is a widely used distribution. It is a right-skewed probability distribution. These distributions are useful in real life where something has a natural minimum of 0.
If X be a continuous random variable taking only positive values, then X is said to be following a gamma distribution iff its p.d.f can be expressed as:-
=0 otherwise….(1)
Probability Density Function for Gamma Distribution
For (1), to be the Probability Density Function, we must have:-
Now, f(x)>0 if x>0 & f(x)=0 if x taking any non-positive values, so, f(x)≥0 ∀x .
Hence, condition (i) is satisfied. Now,
Using (3) & (4) in (2) we get:-
Hence, f(x) statistics condition (ii).
So, equation (1) is a proper pdf.
Moment Generating Function for Gamma Distribution
Moment generating functions are general procedure of finding out moments of a probability distribution mathematically it may be expressed as- M_x (t)=E(e^xt )
This represents raw moments of the random variable X about to the origin 0.
Three important properties of m.g.f. are:- (i) where c is a constant.(ii) If ’s are independent Random variables i.e. then (iii) If X and Y are two random variables and if then X and Y are two identical distribution this is called the uniqueness property,Calculating the m.g.f. of gamma distribution:
Using (2) and (3) in (1) we get:
5. Beta Distribution
In probability theory and statistics, the beta distribution is a family of continuous probability distributions defined on the interval [0, 1] parameterized by two positive shape parameters, denoted by α and β, that appear as exponents of the random variable and control the shape of the distribution.
The beta distribution has been applied to model the behavior of random variables limited to intervals of finite length in a wide variety of disciplines.
Probability Density Function for Beta Distribution
The probability density function (PDF) of the beta distribution, for 0 ≤ x ≤ 1, and shape parameters α, β > 0, is a power function of the variable x and of its reflection (1 − x) as follows:
Where Γ(z) is the gamma function. The beta function, B, =+is a normalization constant to ensure that the total probability integrates to 1. In the above equations, x is a realization—an observed value that actually occurred—of a random process X.
This definition includes both ends x = 0 and x = 1, which is consistent with the definitions for other continuous distributions supported on a bounded interval which are special cases of the beta distribution, for example, the arcsine distribution, and consistent with several authors, like N. L. Johnson and S. Kotz. However, the inclusion of x= 0 and x= 1 does not work for α, β < 1; accordingly, several other authors, including W. Feller, choose to exclude the ends x = 0 and x = 1, (so that the two ends are not actually part of the domain of the density function) and consider instead 0 < x < 1. Several authors, including N. L. Johnson and S. Kotz, use the symbols p and q (instead of α and β) for the shape parameters of the beta distribution, reminiscent of the symbols are traditionally used for the parameters of the Bernoulli distribution, because the beta distribution approaches the Bernoulli distribution in the limit when both shape parameters α and β approach the value of zero.
In the following, a random variable X beta-distributed with parameters α and β will be denoted by:
Other notations for beta-distributed random variables used in the statistical literature are. X- Be(α,β)and X~β_(α,β)
Moment Generating Function for Beta Distribution
6. Chi-square Distribution & Exponential Distribution
CHI-SQUARE DISTRIBUTION
A chi-square distribution is defined as the sum of the squares of standard normal variates. Let x be a random variable which follows normal distribution with mean μ& variance then standard normal variate is defined as: –
The variate Z is said to follow a standard normal distribution with mean 0 and variance 1. Let X be a random variable containing observations,.
Then the chi-square distribution is defined as:- So we can say:-
A chi-square distribution with ‘n’ degree of freedom, where degrees of freedom refer to number of independent associations among variables.
The Probability Density Function of a Chi-Square Distribution:
The Moment Generating Function of Chi-Square Distribution:
(4) is a required m.g.f. of the chi-square distribution.
EXPONENTIAL DISTRIBUTION
The Exponential distribution is one of the widely used continuous distributions. It is often used to model the time elapsed between events.
The Probability Density Function of Exponential Distribution:
Let X be a continuous random variable assuming only real values then X is said to be following an exponential distribution iff:-
Therefore, exponential distribution is a special case of gamma
distribution with v = 1.
The Moment Generating Function of Exponential Distribution:
Let X~Exponential (λ), we can find its expected value as follows, using integration by parts:
Now let’s find Var (X), we have
7. T-Distribution & F-Distribution
T-DISTRIBUTION
Student’s t-distribution:
If x1,x2,… ,xn be ‘n’ random samples drawn from a normal population having mean & standard deviation then the statistics following student t-distribution with (n-1) degrees of freedom.
Fisher’s t-distribution:
Let X~N(0,1) & let the random variable Y~X_n^2. Both X & Y are independent random variables. Then the fisher’s t-distribution is defined as :-
Probability Density Function for t-distribution:
Where, t2 > 0 Where, v=(n-1) degrees of freedom
= 0 , otherwise
For Fisher’s t-distribution:
Where, t2 > 0
=0 otherwise
Application of t-distribution:
If x1,x2, and x3 are independent random variables. Each following a standard normal distribution. What will be the distribution of
F- DISTRUBUTION
The F-Distribution is a ration of two chi-square distributions. If X be a random variable which follows a fisher’s t-distribution.Then:-
Squaring the above expression we get:
The R.V. X2~F1, n. Then we say X2 follows F-distribution with 1,n degrees of freedom.
Probability Density Function for F-distribution:
Application of F-Distribution:
Let x1,x2,… ,xn be a random sample drawn from a normal population with mean μ & variance σ2. where both μ & σ are unknown. Obtain the MLEs of θ.
Let x1,x2,… ,xn be ‘n’ random sample drawn from a normal population with mean μ & variance σ2.
Taking logarithms on both sides; we get:-
CONCLUSION: That was a thorough analysis of different types of sampling distribution along with their distinct functions and interrelations. If the resource was useful in understanding statistical analytics, find more such informative and analytical, subject-oriented discussion regarding statistical analytics courses on DexLab Analytics blog.
The next time you are buying a smart phone or a digital camera, instead of concentrating on the lens specifications, try to find out what the manufacturer has to say about artificial intelligence. Because the nature of photography has changed for good in a world fast recalibrating to keep pace with the exigencies of advanced computing. In this article we will examine how artificial intelligence has transformed the way the world looks at photography now.
Smart Devices
Photography, the idea of sharing with others what we can see through a lens, has been a long developed art with teachers and mentors passing down acquired skills to protégés and juniors for years now. However, with the advancement of AI’s uses in the photography industry, things have changed tremendously.
Take for instance Apple’s A11 Bionic neural engine chip that powers the latest generation of iPhones. The chip is fitted with AI technology that assists in image and face recognition, AR applications and more. Google Pixel then came out with its high-tech hardware chip designed for dedicated image enhancement and image processing.
The Chinese smartphone, Huawei’s P20 Pro, features four cameras. Besides achieving the highest DxO Mark score to date, the Huawei P20 Pro is packed with AI features, such as real-time image scene recognition, meaning it can discern 500 scenarios in 19 categories, such as animals, landscapes, as well as an advanced night mode, where the AI assists in processing noisy photos, making them almost perfect, says a report.
So it has become the norm for smart devices to have inbuilt AI powered hardware that help enhance the process of photography unlike traditional cameras. Manufacturers are concentrating on image capture and real-time processing because it is a market differentiator.
Processing professional photographs
Professional photography needs to be processed and cannot be used in the RAW format. But the procedure has been enhanced by AI technology lately. For instance, recently, PetaPixel released a research paper that talked about how extremely underexposed images can be retrieved through techniques wherein AI is applied to the digital images. This technology can be used in high-end security cameras as well.
Photo optimization
Photo optimisation is what AI has been able to take to the next level. A team of AI developers at Skylum is working on technology that will allow smartphone images to be expanded and printed with very high resolution and sharpness. This technology will help consumers lagging behind with older smart phones and old technology to optimise photos taken years ago. Other companies are trying to build technology that will compress RAW images up to 10 times the original heavy files without loss of data.
It might seem like AI is intruding into the art space of photography, especially for professionals who have spent years honing the art of taking and editing photographs. But for the common user, AI powered technology is a boon and this technology is being sought by the best tech companies across the world. In India, artificial intelligence certifications in Delhi NCR are springing up to cater to a growing clientele that wants to join the AI revolution.
Lack of collaboration between team members could be a frustrating experience as could be spending time maintaining your models after deploying them.
These reasons among others could mean the need for adopting data science platforms and having to choose the right platform from a host of available packages in the market.
“Various organizations keep floating data science platforms to simplify machine learning workflows. However, in the ever-changing data science landscape, only a few draw the attention of practitioners,” says a report.
Here is a list of top 7 data science platforms available for use in 2020.
Databricks
“Built by the founder of Apache Spark, Databricks provides a unified analytics platform that allows data scientists to manage end-to-end machine learning workflows.
The one-size-fits-all platform not only enables practitioners to explore, visualize and build superior machine learning models, but also allows them to scale it quickly with the help of collaboration.”
DataRobot
DataRobotassists companies to automate the workflows of machine learning through its feature-rich solutions and it constantly strives to enhance its platform by either acquiring various companies, or by developing in-house solutions.
“Apart from assisting the regular analytics workflows”, DataRobot is among the best in the AutoML arena.
Apache Spark
“Apache Spark is an open-source unified analytics engine for large-scale data processing and analyzing. It is similar to HadoopMapReduce; it works on cluster computing, but due to exceptional speed – which is believed to be 100x faster in memory and 10x faster on disk than Hadoop – it has become popular among data scientists.”
Dataiku
This is yet another reputed enterprise AI and machine learning platform that “helps businesses in minimizing data processes to expedite the development of machine learning-based solutions”.
The platform helps companies in bringing together data analysts, engineers, and scientists to achieve shared goals through collaboration. “It also provides instant visual and statistical feedback on model performance to manage models’ lifecycle effectively”.
IBM Cloud Pak for Data
“Built on Red Hat OpenShift container platform, IBM Cloud Pak for Data is a fully-integrated AI platform to meet the changing needs of enterprises. It allows data scientists to unlock insights and eliminate data silos quickly.
The platform has a high degree of enterprise readiness and delivers business value by enabling practitioners to integrate with other platforms using APIs.”
Alteryx
“Alteryx is a self-service analytics platform that can be utilized across organizations to democratize data. The platform caters to every need of analytics professionals, such as business intelligence, data analyst, data scientist, and non-experts to assist them in quickly solving business problems. It supports analytics modelling without code and advanced modelling with algorithms.”
TIBCO
TIBCO Software acts as a foundation for digital innovation for data-driven companies. “Integration among platforms has been one of the longest standing predicaments for organizations.”
“Thus, TIBCO offers a suite of products like Connect, API-Led Integration, Data Fabric, Unify, Data Science & Streaming, and more, to eliminate challenges for a streamlined data science workflow.”
