In this particular blog we will discuss about few of the basic functions of MQL (MongoDB Query Language) and we will also see how to use them? We will be using MongoDB Compass shell (MongoSH Beta) which is available in the latest version of MongoDB Compass.
Connect your Atlas cluster to your MongoDB Compass to get started. Latest version of MongoDB Compass will have this shell, so if you don’t find this shell then please install the latest version for this to work.
Now lets start with the functions.
find() :- You need this function for data extraction in the shell.
In the shell we need to first write the “use database name” code to access the database then use .find() to extract data which has name “Wetpaint”
For the above query we get the following result:-
The above result brings us to another function .pretty() .
2. pretty() :- this function helps us see the result more clearly.
Try it yourself to compare the results.
3. count() :- Now lets see how many entries we have by the company name “Wetpaint”.
So we have only one document.
4. Comparison operators :-
“$eq” : Equal to
“$neq”: Not equal to
“$gt”: Greater than
“$gte”: Greater than equal to
“$lt”: Less than
“$lte”: Less than equal to
Lets see how this works.
5. findOne() :- To get a single document from a collection we use this function.
6. insert() :- This is used to insert documents in a collection.
Now lets check if we have been able to insert this document or not.
Notice that a unique id has been added to the document by default. The given id has to be unique or else there will be an error. To provide a user defined id use “_id”.
So, with that we come to the end of the discussion on the MongoDB. Hopefully it helped you understand the topic, for more information you can also watch the video tutorial attached down this blog. The blog is designed and prepared by Niharika Rai, Analytics Consultant, DexLab AnalyticsDexLab Analytics offers machine learning courses in Gurgaon. To keep on learning more, follow DexLab Analytics blog.
This is another blog added to the series of time series forecasting. In this particular blog I will be discussing about the basic concepts of ARIMA model.
So what is ARIMA?
ARIMA also known as Autoregressive Integrated Moving Average is a time series forecasting model that helps us predict the future values on the basis of the past values. This model predicts the future values on the basis of the data’s own lags and its lagged errors.
When a data does not reflect any seasonal changes and plus it does not have a pattern of random white noise or residual then an ARIMA model can be used for forecasting.
There are three parameters attributed to an ARIMA model p, q and d :-
p :- corresponds to the autoregressive part
q:- corresponds to the moving average part.
d:- corresponds to number of differencing required to make the data stationary.
In our previous blog we have already discussed in detail what is p and q but what we haven’t discussed is what is d and what is the meaning of differencing (a term missing in ARMA model).
Since AR is a linear regression model and works best when the independent variables are not correlated, differencing can be used to make the model stationary which is subtracting the previous value from the current value so that the prediction of any further values can be stabilized . In case the model is already stationary the value of d=0. Therefore “differencing is the minimum number of deductions required to make the model stationary”. The order of d depends on exactly when your model becomes stationary i.e. in case the autocorrelation is positive over 10 lags then we can do further differencing otherwise in case autocorrelation is very negative at the first lag then we have an over-differenced series.
The formula for the ARIMA model would be:-
To check if ARIMA model is suited for our dataset i.e. to check the stationary of the data we will apply Dickey Fuller test and depending on the results we will using differencing.
In my next blog I will be discussing about how to perform time series forecasting using ARIMA model manually and what is Dickey Fuller test and how to apply that, so just keep on following us for more.
So, with that we come to the end of the discussion on the ARIMA Model. Hopefully it helped you understand the topic, for more information you can also watch the video tutorial attached down this blog. The blog is designed and prepared by Niharika Rai, Analytics Consultant, DexLab AnalyticsDexLab Analytics offers machine learning courses in Gurgaon. To keep on learning more, follow DexLab Analytics blog.
ARMA(p,q) model in time series forecasting is a combination of Autoregressive Process also known as AR Process and Moving Average (MA) Process where p corresponds to the autoregressive part and q corresponds to the moving average part.
Autoregressive Process (AR) :- When the value of Yt in a time series data is regressed over its own past value then it is called an autoregressive process where p is the order of lag into consideration.
Where,
Yt = observation which we need to find out.
α1= parameter of an autoregressive model
Yt-1= observation in the previous period
ut= error term
The equation above follows the first order of autoregressive process or AR(1) and the value of p is 1. Hence the value of Yt in the period ‘t’ depends upon its previous year value and a random term.
Moving Average (MA) Process :- When the value of Yt of order q in a time series data depends on the weighted sum of current and the q recent errors i.e. a linear combination of error terms then it is called a moving average process which can be written as :-
yt = observation which we need to find out
α= constant term
βut-q= error over the period q .
ARMA (Autoregressive Moving Average) Process :-
The above equation shows that value of Y in time period ‘t’ can be derived by taking into consideration the order of lag p which in the above case is 1 i.e. previous year’s observation and the weighted average of the error term over a period of time q which in case of the above equation is 1.
How to decide the value of p and q?
Two of the most important methods to obtain the best possible values of p and q are ACF and PACF plots.
ACF (Auto-correlation function) :- This function calculates the auto-correlation of the complete data on the basis of lagged values which when plotted helps us choose the value of q that is to be considered to find the value of Yt. In simple words how many years residual can help us predict the value of Yt can obtained with the help of ACF, if the value of correlation is above a certain point then that amount of lagged values can be used to predict Yt.
Using the stock price of tesla between the years 2012 and 2017 we can use the .acf() method in python to obtain the value of p.
.DataReader() method is used to extract the data from web.
The above graph shows that beyond the lag 350 the correlation moved towards 0 and then negative.
PACF (Partial auto-correlation function) :- Pacf helps find the direct effect of the past lag by removing the residual effect of the lags in between. Pacf helps in obtaining the value of AR where as acf helps in obtaining the value of MA i.e. q. Both the methods together can be use find the optimum value of p and q in a time series data set.
Lets check out how to apply pacf in python.
As you can see in the above graph after the second lag the line moved within the confidence band therefore the value of p will be 2.
So, with that we come to the end of the discussion on the ARMA Model. Hopefully it helped you understand the topic, for more information you can also watch the video tutorial attached down this blog. The blog is designed and prepared by Niharika Rai, Analytics Consultant, DexLab AnalyticsDexLab Analytics offers machine learning courses in Gurgaon. To keep on learning more, follow DexLab Analytics blog.
Artificial Intelligence, or, its more popular acronym AI is no longer a term to be read about in a sci-fi book, it is a reality that is reshaping the world by introducing us to virtual assistants, helping us be more secure by enabling us with futuristic measures. The evolution of AI has been pretty consistent and as we are busy navigating through a pandemic-ridden path towards the future, adapting to the “new normal”, and becoming increasingly reliant on technology, AI assumes a greater significance.
The AI applications which are already being implemented has resulted in a big shift, causing an apprehension that the adoption of AI technology on a larger scale would eventually lead to job cuts, whereas in reality, it would lead to the creation of new jobs across industries. Adoption of AI technology would push the demand for a workforce that is highly skilled, enrolling in an artificial intelligence course in delhi could be a timely decision.
Now that we are about to reach the end of 2020, let us take a look at the possible impacts of AI in the future.
AI will create more jobs
Yes, contrary to the popular apprehension AI would end up creating jobs in the future. However, the adoption of AI to automate tasks means yes, there would be a shift, and a job that does not need special skills will be handled by AI powered tools. Jobs that could be done without error, completed faster, with a higher level of efficiency, in short better than humans could be performed by robots. However, with that being said there would be more specialized job roles, remember AI technology is about the simulation of human intelligence, it is not the intelligence, so there would be humans in charge of carrying out the AI operated areas to monitor the work. Not just that but for developing smarter AI application and implementation there should be a skilled workforce ready, a report by World Economic Forum is indicative of that. From design to maintenance, AI specialists would be in high demand especially the developers. The fourth industrial revolution is here, industries are gearing up to build AI infrastructure, it is time to smell the coffee as by the end of 2022 there will be millions of AI jobs waiting for the right candidates.
Dangerous jobs will be handled by robots
In the future, hazardous works will be handled by robots. Now the robots are already being employed to handle heavy lifting tasks, along with handling the mundane ones that require only repetition and manual labor. Along with automating these tasks, the robot workforce can also handle the situation where human workers might sustain grave injuries. If you have been aware and interested then you already heard about the “SmokeBot”. In the future, it might be the robots who will enter the flaming buildings for assessment before their human counterparts can start their task. Manufacturing plants that deal with toxic elements need robot workers, as humans run a bigger risk when they are exposed to such chemicals. Furthermore, the nuclear plants might have a robot crew that could efficiently handle such tasks. Other areas like pipeline exploration, bomb defusing, conducting rescue operations in hostile terrain should be handled by AI robots.
Smarter healthcare facilities
AI implementation which has already begun would continue to transform the healthcare services. With AI being in place CT scan and MRI images could be more precise pointing out even minuscule changes that earlier went undetected. Drug development could also be another area that would see vast improvement and in a post-pandemic world, people would need to be better prepared to fight against such viruses. Real-time detection could prevent many health issues going severe and keeping a track of the health records preventive measures could be taken. One of the most crucial changes that could be revolutionary, is the personalized medication which could only be driven by AI technology. This would completely change the way healthcare functions. Now that we are seeing chat bots for handling sales queries, the future healthcare landscape might be ruled by virtual assistants specifically developed for offering assistance to the patients. There are going to be revolutionary changes in this field in the future, thereby pushing the demand for professionals skilled in deep learning for computer vision with python.
Smarter finance
We are already living in an age where we have robo advisors, this is just the beginning and the growing AI implementation would enable an even smarter analytics system that would minimize the credit risk and would allow banks and other financial institutes to minimize the risk of fraud. Smarter asset management, enhanced customer support are going to be the core features. Smarter ML algorithms would detect any and every oddity in behavior or in transactions and would help prevent any kind of fraud from happening. With analytics being in place it would be easier to predict the future trends and thereby being more efficient in servicing the customers. The introduction of personalized services is going to be another key feature to look out for.
Retail space gets a boost
The retailers are now aiming to implement AI applications to offer smart shopping solutions to the future buyers. Along with coming up with personalized shopping suggestions for the customers and showing them suggestions based on their shopping pattern, the retailers would also be using the AI to predict the future trends and work accordingly. Not just that but they can easily maintain the supply and demand balance with the help of AI solutions and stock up items that are going to be in demand instead of items that would not be trendy. The smarter assistants would ensure that the customer queries are being handled and they could also be helping them with shopping by providing suggestions and information. From smart marketing to smarter delivery, the future of retail would be dominated by AI as the investment in this space is gradually going up.
The future is definitely going to be impacted by the AI technology in more ways than one. So, be future ready and get yourself upskilled as it is the need of the hour, stay updated and develop the skill to move towards the AI future with confidence.
Artificial Intelligence or, AI is an advanced technology that is busy taking the world in its strides. With virtual assistants, face recognition, NLP, object detection, data crunching becoming familiar terms it is no wonder that this dynamic technology is being integrated into the very fabric of our society. Almost every sector is now adopting AI technology, be it running business operations or, ensuring error-free diagnosis in the healthcare domain, the exponential growth of this technology is pushing the demand for skilled AI professionals who can monitor and manage the AI operations of an organization.
Since AI is an expansive term and branches off in multiple directions, the job opportunities available in this field are also diverse. According to recent studies, AI jobs are going to be the most in-demand jobs in the near future. Multiple job roles are available that come with specific job responsibilities. So, let’s have a look at some of these.
Machine Learning Engineer
An machine learning engineer is supposed to be one of the most in-demand jobs available in this field, the basic job of an ML engineer center round working on self-running software, and they need to work with a huge pile of data. In an organization, the machine learning engineers need to collaborate with data scientists and ensure that real-time data is being put to use for churning out accurate results. They need to work with data science models and develop algorithms that can process the data and offer insight. Mostly their job responsibility revolves around working with current machine learning frameworks and working on it to make it better. Re-training machine learning models is another significant responsibility they need to shoulder.
If recent statistics are to be believed the salary of a machine learning hovers around ₹681,881 in India.
Artificial Intelligence Engineer
AI engineers are indeed a specialized breed of professionals who are in charge of AI infrastructure and work on AI models. They work on designing models and then test and finally, they need to deploy these models. Automating functionalities is also important and most importantly they must understand the key problems that need AI solutions. AI engineers need to write programs, so they need to be familiar with several programming languages, having a background in Machine Learning Using Python could be a big help. Another important responsibility is creating smart AI algorithms for developing an AI system, as per the specific requirement that needs to be solved using that system.
In India, an AI engineer could expect the salary to be around ₹7,86,105 per year, as per Glassdoor figures.
Data Scientist
A data scientist is going to be in charge of the data science team and need to work on the huge volumes of data to analyze and extract information, build and combine models and employ machine learning, data mining, techniques along with utilizing numerous tools including visualization tools to help an organization reach its business goals. The data scientists need to work with raw data and he needs to be in charge of automating the collection procedure and most importantly they need to process and prepare data for further analysis, and present the insight to the stakeholders.
A data scientist could earn around ₹ 7,41,962 per year in India as per the numbers found on Indeed.
AI Architect
An AI architect needs to work with the AI architecture and assess the current status in order to ensure that the solutions are fulfilling the current requirements and would be ready to scale up to adapt to the changing set of requirements that would arise in the future. They must be familiar with the current AI framework that they need to employ to develop an AI infrastructure that is sustainable. Along with working with a large amount of data, an AI architect must be employing machine learning algorithms and posses a thorough knowledge of the product development, and suggest suitable applications and solutions.
In India an AI architect could expect to make around ₹3,567K per year as per Glassdoor statistics is concerned.
There are so many job opportunities available in the AI domain, and here only a few job roles have been described. There are plenty more diverse job opportunities await you out there, grab those, just get artificial intelligence certification in delhi ncr and be future-ready.
As AI is gradually being incorporated into businesses, it is only a matter of time before the workplace dynamics get completely revolutionized. Despite there being a misconception that the adoption of AI only will spell disaster for the job market but, that is far from the truth. Yes, for handling repetitive tasks that require absolutely no human intervention might be entirely handled by the AI powered robots, but, there will be a requirement for people who have undergone artificial intelligence certification in delhi ncr. As the workplaces around us gear up to include AI in their regime, employees with AI training background would prove to be invaluable assets in the days to come.
How the workplace is being transformed by AI
AI could streamline workplace operations right from the hiring process. Finding the right candidate for any job means wading through a huge number of candidate profiles, scanning resumes, and then scheduling interviews. AI can automate the entire process by taking care of every single segment and could also engage with candidates and do the initial screening. Using a tool like Koru helps employers match candidate profiles to the job requirement and enables them to shortlist ideal candidates in a jiffy.
Post hiring the onboarding process could also be handled by AI by deploying chatbots that can help the new workers integrate with the existing system. AI-powered tools are also being incorporated to train the employees and personalized training programs could be developed as a result. A case in point would be Cogito, which monitors calls and offers suggestions to the customer support department to improve their conversations.
The employees usually have to go through carrying out some mundane tasks. It could be scheduling meetings, preparing reports, and also looking through reports to extract relevant information. This takes away all their time and energy which they otherwise could have utilized doing something productive. The inclusion of AI can take this mundane workload off the shoulders of the employees and automate the entire process. Another benefit to consider would be to identifying areas in the workplace that needs immediate attention, as well as identifying obstacles standing in the way of getting productive. LaborWise is an excellent productivity analytics tool.
Security is a key factor for any organization and given the rise in cybercrime, having access to cutting edge technology can prevent such threats. AI can rise to the occasion and help to identify threats analyzing scores of data in real-time. A great example of this would be DeepArmor, which is used for the prevention of malware. AI could easily detect anomaly which otherwise is impossible and could also explore previous datasets to identify areas that are vulnerable to such attacks. AI robots can also be used to handle hazardous work situations which could endanger a human worker.
Application of AI means that the organization would be empowered by cutting edge business knowledge. The accumulated data gets parsed quickly and employees get access to valuable information to devise the strategies accordingly. It saves time, it saves labor and most importantly it removes errors. The companies are quickly able to spot any changes in the pattern that needs immediate attention.
Be it the inclusion of RPA or, some other tool, AI is making workplaces function more efficiently and the future already sounds promising. However, the bias surrounding AI needs to be tackled and most importantly employees should be encouraged to upgrade themselves by enrolling in an artificial intelligence training institute in Gurgaon.
Internet of Things or IOT devices are a rage now, as these devices staying connected to the internet can procure data and exchange the same using the sensors embedded in those. Now the data which is being generated in copious amount needs to be processed and in comes IoT Analytics. This platform basically is concerned with analyzing the large amount of data generated by the devices. The interconnectivity of devices is helping different sectors be in sync with the world, and the timely extraction of data is of utmost significance now as it delivers actionable insights. This is a highly skilled job responsibility that could only be handled by professionals having done artificial intelligence course in delhi.
This particular domain is in the nascent stage and it is still growing, however, it is needless to point out that IoT analytics holds the clue to business success, as it enables the organizations to not only extract information from heterogeneous data but also helps in data integration. With the IoT devices generating almost 5 quintillion bytes of data, it is high time the organizations start investing in developing IoT analytics platform and building a data expert team comprising individuals having a background in Machine Learning Using Python. Now let’s have a look at the ways IoT analytics can boost business growth.
Optimized automated work environment
IoT analytics can optimize the automated work environment, especially the manufacturing companies can keep track of procedures without involving human employees and thereby lessening the chances of error and enhancing the accuracy of predicting machine failure, with the sensors monitoring the equipments and tracing every single issue in real-time and sending alerts to make way for predictive maintenance. The production flow goes on smoothly as a result without developing any glitch.
Increasing productivity
In an organization gauging the activity of the employees assumes huge significance as it directly impacts the productivity of the company, with sensors being strategically placed to monitor employee activity, performance, moods and other data points, this job gets easier. The data later gets analyzed to give the management valuable clues that enable them to make necessary modifications in policies.
Bettering customer experience
Regardless of the nature of your business, you would want to make sure that your customers derive utmost satisfaction. With IoT data analytics in place you are able to trace their preferences thanks to the data streaming from devices where they have already left a digital footprint of their shopping as well as searching patterns. This in turn enables you to offer tailor-made service or products. Monitoring of customer behavior could lead to devising marketing strategies that are information based.
Staying ahead by predicting trends
One of the crucial aspects of IoT analytics is its ability to predict future trends. As the smart sensors keep tracking data regarding customer behavior, product performance, it becomes easier for businesses to analyze future demands and also the way trends will change to make way for emerging ones and it enables the businesses to be ready. Having access to a future estimate prepares not just businesses but industries be future ready.
Smarter resource management
Efficient utilization of resources is crucial to any business, and IoT analytics can help in a big way by making predictions on the basis of real-time data. It allows companies to measure their current resource allocation plan and make adjustments to make optimal usage of the available resources and channelizing that in the right direction. It also aids in disaster planning.
Ever since we went digital the streaming of large quantity of data has become a reality and this is going to continue in the coming decades. Since, most of the data generated this way is unstructured there needs to be cutting edge platforms like IoT analytics available to manage the data and processing it to enable industries make informed decisions. Accessing Data Science training, would help individuals planning on making a career in this field.
When we take a look at a video or, a bunch of images we know what’s what just by taking one look, it is our innate ability that gradually developed. Well, sophisticated technologies such as object detection can do that too. It might sound futuristic but it is happening now in reality. Object detection is a technique of the AI subset computer vision that is concerned with identifying objects and defining those by placing into distinct categories such as humans, cars, animals etc.
It combines machine learning and deep learning to enable machines to identify different objects. However, image recognition and object detection these terms are often used interchangeably but, both techniques are different. Object detection could detect multiple objects in an image or, in a video. The demand for trained experts in this field is pretty high and having a background in deep learning for computer vision with python can help one build a dream career.
Object detection has found applications across industries. Let’s take a look at some of these applications.
Tracking objects
It is needless to point out that in the field of security and surveillance object detection would play an even more important role. With object tracking it would be easier to track a person in a video. Object tracking could also be used in tracking the motion of a ball during a match. In the field of traffic monitoring too object tracking plays a crucial role.
Counting the crowd
Crowd counting or people counting is another significant application of object detection. During a big festival, or, in a crowded mall this application comes in handy as it helps in dissecting the crowd and measure different groups.
Self-driving cars
Another unique application of object detection technique is definitely self-driving cars. A self-driving car can only navigate through a street safely if it could detect all the objects such as people, other cars, road signs on the road, in order to decide what action to take.
Detecting a vehicle
In a road full of speeding vehicles object detection can help in a big way by tracking a particular vehicle and even its number plate. So, if a car gets into an accident or, breaks traffic rules then it is easier to detect that particular car using object detection model and thereby decreasing the rate of crime while enhancing security.
Detecting anomaly
Another useful application of object detection is definitely spotting an anomaly and it has industry specific usages. For instance, in the field of agriculture object detection helps in identifying infected crops and thereby helps the farmers take measures accordingly. It could also help identify skin problems in healthcare. In the manufacturing industry the object detection technique can help in detecting problematic parts really fast and thereby allow the company to take the right step.
Object detection technology has the potential to transform our world in multiple ways. However, the models still need to be developed further so that these can be applied across devices and platforms in real-time to offer cutting-edge solutions. Pursuing a Python Certification course can help develop the required skills needed for making a career in the field of machine learning.
In an era that is being significantly impacted by AI, it is but obvious that many AI-powered innovations would gradually seep through and influence the traditional work settings. RPA or, Robotic Process Automation is a key AI solution that is changing the way businesses function by automating the tasks performed by humans and increasing productivity along the way.
RPA is all about automating tasks that are mundane and repetitive, it is business process automation that automates the time consuming repetitive tasks to increase productivity and efficiency while speeding up the process and minimizing errors. Having artificial intelligence certification in delhi ncr, is vital for professionals aspiring to work in such cutting edge technology driven environment.
The benefits of RPA:
RPA, is no doubt a technology that has the power to revolutionize the way humans traditionally work. The process has certain benefits to offer, let’s find out what those are.
Cost-effective and efficient: When an organization employs bots, it can expect to cut down the cost significantly. Not just that but, the maintenance of human workers can be challenging, humans are not physically able to work beyond a set time limit. The robots can easily work long hours, they do not need breaks and are less expensive than human counterparts.
Increases productivity: When human employees are freed from their mundane activities they can put their skills to better use. They can work on clients, or, handle some other areas that require specific human skills. When the workers are entrusted with productive work they derive job satisfaction and feel motivated to focus on tasks that generate higher revenue.
Better for the environment: RPA ensures the organization follows the path of digitization which results in a reduction in the usage of papers. All the files that are to be handled are in the digital format eliminating the need to use papers.
Fewer chances of error: When humans are involved in carrying out mundane tasks, the room for error would be there too. However, once the process gets automated and handled by bots the chance of error gets eliminated and the tasks get performed more accurately and efficiently.
Improved IT: When RPA capabilities make their way into the organization one can expect a higher level of efficiency, but, the best part of RPA is that, unlike other technologies no new IT system needs to be devised, the existing system can be used by the RPA system, which saves the organization the pain of bearing the cost of substituting the existing system.
Smarter analytics: When RPA is employed there can be no more errors in the analytics. The system collects every microdata possible regarding the organization and helps the management get actionable insight. The RPA system also works great for monitoring purposes.
Seamless communication and security: RPA can ensure that all the information is updated and reaches every segment seamlessly. Earlier any change would need to be communicated by manually updating and it was less efficient, time-consuming and tiring. However, with RPA in place every tiny change made gets automatically updated and the system also keeps the data secure.
Given the benefits offered by RPA, it is no doubt that most organizations would be eager to adopt the technology. However, the demand for professionals who are well proficient in handling AI, Big Data is also on the rise. Getting a degree from a reputed artificial intelligence training institute in Gurgaon, might help one gab the dream job in this sector.
