Household Electricity Consumption – Machine Learning Algorithm

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Power supply, generation, and its billing generate a huge amount of data. ML actually makes it possible to learn from this data and use an algorithm to accurately predict future occurrences like volumes of load and its demand, snag identification, efficiency and power loss reduction, problems and logistics involved in metering and billing and everything in between from power generation to its billing and beyond.
Machine learning courses in India could teach you how to understand ML and data analytics, so you aid ML to perform at its best in predicting outcomes. The Algorithm in ML for household electricity consumption works on data drawn from smart meters, solar panels, and data regarding the usage of electricity at different times of the day.
This huge data comprises the multi-variable time-series, and the algorithm can successfully predict future consumption. In real terms, the ML algorithm can predict such information as to help make the power generation and supply system more efficient.
Obviously, there are many steps involved in helping the machine take data in its raw multivariate form and enabling it to arrive at the future consumption prediction. This is where Machine learning courses come in handy. You can learn the techniques of ML involving predictive strategies like the direct methods and the recursive ones.
A good idea is to also incorporate learning of Big Data Hadoop training courses that can help one understand strategies, working of ML and data analytics. The logic of the process of algorithm development would be developing

  • The framework development for evaluation of non- linear, linear, and ML ensemble algorithms.
  • Evaluation of ML as it uses the strategy of forecasting the time-series both by the direct daily method and the recursive method.

Again such processes involve

  1. Describing the problem.
  2. Preparing and loading the data set.
  3. Evaluating the model.
  4. Recursive forecasting.
  5. Multi-Step direct forecasting.

Through highly accurate predictions ML helps the algorithm to plan future power generation, reduce transmission losses, tweak the metering, billing and collection systems and so much more. Once you master such algorithms, ML and data analytics, the scope of applying ML to various and everyday issues on a real-time basis, open the wide world of opportunity and good remuneration to you.
Yes, ML and data analytics use Python framework which has immense scope for progress basically because it can predict the outcomes of simple and complex tasks, single and multi-variate tasks, and even makes single and complex predictions by learning from the data, filling in the missing values, creating new values and so on. And to learn an ML course is essential. Start today and soon you will be able to master such tasks quite easily.

Reference:
https://machinelearningmastery.com/multi-step-time-series-forecasting-with-machine-learning-models-for-household-electricity-consumption/

Developing ML Models in Multivariate, Multi-Step Forecasting of Air Pollution Time-Series

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Machine Learning Courses in India

The ML algorithms can be applied forecast weather and air pollution for the subsequent 3-days. This is challenging because of the need to accurately predict across multivariate input with noisy dependencies that are complex and multi-step, multi-time input data while forecasting and performing the same prediction across many sites.
‘Air Quality Prediction’ or the Global Hackathon EMC dataset provides weather conditions across various sites and needs accurate predictions of measurement of air-quality to provide a 3-day weather forecast.

The Need for Machine Learning

The primary benefits of Machine learning courses are that with them you can learn to operate the tools from a Python open source library and gain expertise in

  • Providing for missing values, transforming the time-series data and successfully create models that are worked by the trained and supervised-learning algorithms.
  • Evaluate and develop both linear and nonlinear algorithms to handle the multivariate, multi-step multi-time series forecast.

The Need for Data Analytics

A real-time problem when working with this dataset is that of missing values and multiple variables drawn from many physical sites. This means integrating and helping the ML algorithm predict and forecast accurately. You will need data analytical skills to achieve this.
The Big Data Hadoop training courses can provide you with skills and learning in

  • Imputing values that are missing, helping algorithms with supervised learning by transforming the input data time-series and creating requisite number of models using the data and the algorithm.
  • How to evaluate and develop suites of nonlinear and linear algorithms for multiple-stepped forecasting of a time series.

The Entire Process

Developing this algorithm and making it successfully predict with accuracy the weather forecast over the next 72 hours in an environment that has multiple variables, multiple data sets, some missing data, lots of ways to develop the code on the Python platform has nine parts.
Namely,

  • Description of the problem.
  • Evaluation of models.
  • ML Model creation.
  • Data preparation using ML.
  • Creating a Test Harness for model evaluation.
  • Linear Algorithms evaluation.
  • Nonlinear Algorithms evaluation.
  • Lag Size tuning.

Benefits of ML, in this case, are handling features that are irrelevant, the ability to support between variables noise and noisy features, and the ability to support inter-variable relationships. ML forecasting provides both recursive and direct forecasts.
Benefits of data analytics relevant here are in preparing data, feature engineering, lag-tuning the meteorological variables, creating models across many sites, and tuning the algorithm itself.
Enrol in the most suitable course that will help you learn how to develop the algorithm for air pollution forecasting.
Reference:
https://machinelearningmastery.com/how-to-develop-machine-learning-models-for-multivariate-multi-step-air-pollution-time-series-forecasting/

How AIML Can Facilitate a Holistic Digital Transformation of SMEs

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Using AI digitised mobility-efficient business management empowers SMEs to expand to any region globally with literally no associated monetary or infrastructural deterrents. Especially in processes like strategy-based planned sales, financial management, supply chain logistics, and marketing management where the focus should rightly be on the operational aspects rather than offline management of these which reduce enterprise efficiency.
Notable benefits of machine learning courses in India are learning better workflow management, enabling operational management to reach out, service and retain the all-important customer base. Increased cost-reduction, increased satisfaction levels of customers, doing away with time-consuming redundant offline process management and the obvious maximising of profit margins and enterprise efficiency result.

Role of Machine Learning-ML and AI

Issues are unique to every enterprise. Solutions should emerge from the workflow and be need-specific to the enterprise and its segment. Automating the logistics of the supply chain processes and sales can be optimised by ML and AI to build solutions meeting the needs and precise requirements of any business or industry with a high level of precision and customisation through the proper use of the huge data repository available with them.

Data and Challenges

Data is the backbone of automation and readily available with SME’s. Greater volumes in the database ensure tweaking for quickening and process efficiency. Big data Hadoop training courses help streamlining data, identifying and eliminating unnecessary recurrent processes and automating the process for fixed quicker and efficient outcomes is what ML, data analytics and AI intuitive combinations does when customizing processes and big data.
This indirectly frees-up the crucial time-component spent on customer interactions. ML and AI bring huge benefits in pattern recognition and predictive analysis. Their use helps deliver effective business solutions with quick outcomes by identifying and automating recurring procedures and patterns. Thus the digitization of marketing and sales drive profit and efficiency in the enterprise.

Customer Service Paradigms

In today’s scenario the pervasive use of the internet, use of digital tools, mobile apps and smart-phones create a huge database of young consumers under-35, who use and prefer digital methods to offline methods. Gainful insights are provided through their feedback, need for value-enhanced solutions, customer interaction and resolutions for customer satisfaction.
The success of SME’s depends on adapting and catering to this sector which forms nearly two-thirds of the total Indian population. Many shy away from building a digital infrastructure citing prohibitive costs involved. But, as per digital customers and a study by Google-KPMG, SMBs and SMEs have the potential to grow twice as fast with the adaptation of ML and AI.
Do we need to say anything more for machine learning courses?

Impact in Machine Learning and Artificial Intelligence on Real Estate and Trends!

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Impact in Machine Learning and Artificial Intelligence on Real Estate and Trends!

Have you ever been on the lookout to buy a house or an apartment and found yourself buried in heaps of redundant information? Your dreams of owning property were either forced out the window or grew even more complicated thanks to inadequate information. Well, thankfully this is effectively becoming a thing of the past with the entrance of AI into the world of real estate.

Artificial Intelligence is finally taking over every arena and domain in the world and comes as a welcome relief rather than a cause of alarm amongst us. Real estate is relatively late to the game, consequently, only a few elements of the sector are currently benefiting from machine learning.

What Is Machine Learning?

Machine learning is mostly a computer algorithm that assimilates every ounce of data it is fed, analyses it, adapts to and evolves with this information. Essentially a program uses all of this to create a better version of itself. If you were to look for homes on a website or continuously search for a set of parameters, machine learning will pick up on this and tailor searches to make them more precise and even send recommendations on related listings your way. You are likely to not only have a large number of choices you will be pleased with and a quick search that makes you a homeowner in no time.

Applications in Real Estate

A real estate company can watch their sales numbers explode thanks to AI-driven programs that bring the right customers to them. If you employ this software, you could soon be up for the title of ‘sales executive of the month’ thanks to the number of customers coming your way. Algorithms also improve upon sales campaigns and perfect the entire marketing sales process to bring tenant and landlord together.

With insights, you learn how to ensure listings can become more attractive to search engines. A seller could help a potential customer clarify all their queries through a bot that learns from each question from various customers. However, you run the risk of a bot being so robotic that it comes across as rude or frustrates a customer by repeating the answer a script provides it. While AI is enhancing enough to do away with this drawback slowly.

Once you own your dream home, you will realize machine learning comes into play, mainly with property management. Automation plays a big part in ensuring the lighting, temperature and in general, the HVAC systems of a building are keeping everyone in it satisfied, while enabling the owner to manage bills more effectively.
The most exciting realm of AI for real estate lies in virtual or augmented reality.

They currently exist in simpler forms such as 360° views of a home, so you get a better feel for your home before you even buy it. However, this function is available in a limited capacity with researchers still sorting the multiple bugs that accompany it.

Irrespective of its pros and cons, machine learning, is here to stay inside and outside the world of real estate. Whether you actively use it to better your real estate experience or not, it has a part to play in improving every element of the process.

Things You Need to Know About AI and Supply Chain Management!

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Things You Need to Know About AI and Supply Chain Management!

You have just walked out of your house and forgotten to turn off the lights. You take out your smartphone and ask your voice-activated assistant to do it for you without having to step back into the house. How did it happen? The world as we know it today has entered a realm of endless possibilities thanks to Artificial Intelligence. A machine that is capable of replicating human intelligence to perform everything from the most mundane tasks to running businesses successfully, AI has seamlessly integrated into our lives.

All this began, 60 years ago when computers started learning the ‘checkers’ strategy in 1956 at Dartmouth College. Fast forward to the late 90s and we started using machines or AI for logistics, data mining, medical diagnostics, and other areas. Now you may be wondering, how does a device do it? Well a typical AI takes stock of its environment and starts evaluating a course of action in a human-like fashion to achieve its goals.

Now, how do we use this super-intelligent machine in businesses and supply chain management? Believe it or not, AI has a significant impact on digitization and business due to its ability to make decisions and risk assessments. For example, with the use of AI, a warehouse manager will be able to successfully forecast and order the next inventory that needs to come in without any hassles.

Here are some more benefits of AI in the Supply Chain –

Improved Customer Service

Organizations are increasing the use of AI to talk to their customers and resolve issues in real-time. AI platforms are intuitive and speak in different languages and solve complex customer queries thereby leading to an improved customer service experience.

Artificial Intelligence uses in procurement

What if, you as the procurement manager for an organization could automate the whole process of onboarding vendors using AI? Businesses today are already using AI to reduce costs, mitigate fraudulent activities, and save time during procurement by enabling machines to make decisions through data analytics.

Physical Prototyping is Outdated

Gone are the days when innovators would physically build a prototype and add on its functions. Today, we have machines that recognize the gestures and movements of hands and then render it to create 3D models of products. This implies that they directly talk to product developers in the digital space and make models in real-time. Another example of its implementation would be switching on a button of a prototype which can be done with a simple gesture by the product designer.

Conclusion

One of the greatest modern inventions, AI will continue to grow by leaps and bounds in the coming years. While we continue to expand its capabilities, we must be cognizant of its challenges and risks.

Reference:
https://www.financialexpress.com/industry/artificial-intelligence-the-next-big-thing-in-supply-chain-management/329033/

https://www.scmr.com/article/8_fundamentals_for_achieving_ai_success_in_the_supply_chain

https://medium.com/@KodiakRating/6-applications-of-artificial-intelligence-for-your-supply-chain-b82e1e7400c8

The Major Industries that Artificial Intelligence will Transform by the Year 2020!

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In simple terms, Artificial Intelligence (or AI) is defined by the capability of a computer or a computer-controlled robot to perform tasks, which normally require human intelligence and skills such as visual perception and decision making.

Conceptualized initially as the means to impart intelligence to inanimate objects, AI is impacting multiple industries today including manufacturing, healthcare, and education. According to a Gartner report, AI will create 2.3 million new jobs by the year 2020, while eliminating 1.8 million at the same time. By the year 2022, 1 in every five workers will rely on AI to perform their non-routine task.

 

AI in 2020

How AI is transforming every industry that it touches

The use of AI-based technologies is transforming every industry that it touches by generating a business value projected to value $1.2 trillion globally in the year 2018, marking a 70% increase from 2017. Furthermore, this figure is predicted to reach $3.9 billion globally by the year 2022. According to Gartner, AI is generating business value through the following three sources:

  • Enhanced customer experience
  • New revenue generation through the increase in sales of existing products (or services) or through new products (or services)
  • Reduction in the cost of production and delivery of existing products (or services)

According to Svetlana Sicular of the Gartner research team, “AI will improve the productivity of many job roles while creating millions of skilled management positions including entry-level jobs.” AI will positively impact the technology job market by creating 2 million new jobs globally by the year 2025.
AI in 2020
Svetlana Sicular also adds that most predictions about job losses due to AI technologies is associated with job automation, but ignores the benefits of AI augmentation that complements both human and machine intelligence. For instance, AI and robotics can be leveraged to identify and automate labor-intensive tasks performed by retail workers, thus reducing labor and distribution costs.

Despite the immense potential of AI, the mass-scale adoption of this technology still faces numerous hurdles (including the following), which needs to be immediately tackled:

  • The supervised or structured form of learning by AI systems, which does not imitate the way humans learn naturally from our environment.
  • Lack of creativity and abstract level thinking on the part of AI machines that can process raw data and convert them into intuitive and easy-to-grasp concepts.
  • Being a relatively new concept, AI does not enjoy full public support and trust, thus halting its increased adoption.

AI-based virtual agents (including chatbots) are taking over the handling of simple customer requests from a call center or customer support executives, thus improving business revenue and freeing up employee time for more complex activities and decision making. While virtual agents are accounting for 46% of the AI-based business value in 2018, it will account for only 26% by the year 2022.

Right from our smartphones to self-driving vehicles, Artificial Intelligence is enabling the faster execution of tasks with more accuracy and increased knowledge. This article summarises how AI technology will transform many industries along with the many hurdles that it needs to overcome.

Artificial Intelligence – The Big Game Changer For Business!

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Artificial Intelligence as a concept seemed very distant and tucked away till firms like Google, Amazon, and Facebook brought it into our daily lives and we started seeing its impact in our day to day activities and interactions. Today realizingly or unknowingly we are surrounded by AI in almost every aspect of life from Health to fitness, Finances, Entertainment, education, business and selling, marketing and market research media, and lots more.

Let us try and understand the impact of AI on the key aspects of economic inequality & business cycles and business productivity.

Economic inequality is one of the biggest changes that artificial intelligence is bringing to our doorstep. Artificial intelligence poses the greatest threat to people employed in low skilled or unskilled repetitive jobs, and as more and more companies incorporate AI into their business model, the disparity between low skilled and highly skilled workers is set to increase.

If we consider three major sectors – agriculture, industrial and service, we will observe that the manpower employed in agriculture has reduced the most over the years (although output hasn’t reduced that sharply), and the services sector has seen the steepest rise in a number of people employed.

Every threat and weakness also brings in an opportunity wrapped in strength, and AI is no different. Although a lot of jobs are at risk due to artificial intelligence, AI is laying the field open for several other alternate professions. Programming and testing professionals, coding heroes, data scientists, they all are going to be in great demand in the coming years. Upskilling and learning a new skill is something the workforce would need to embrace if they are to keep their careers on the burn instead of fizzling out.

Artificial Intelligence has impacted almost every facet of business already and looks set to forcefully influence many other areas too. One of the ways in which artificial intelligence is in the way business cycles occur and repeat. Business cycles are the periodic changes from great prosperity and increasing revenue to economic downfall and losses. The business cycles are impacted by AI because AI pushes these cycles closer together and makes them shorter.

We have the growth phase and the consolidation phase during the upswing, but these are now happening more swiftly because AI helps to improve by course correction in a much shorter time. Thousands of data points are analyzed with the help of AI, which then trains itself to come closer to the required output. Compared to that, humans would go through the cycle at a much slower rate.

Let us take an easy example to understand how business cycles are affected by artificial intelligence. In the banking sector, the first quarter of the financial year is usually the most popular for customers to open new accounts to align with the start of a new financial year. From the point of view of the banks themselves, the last quarter is a big quarter to push for new accounts and deposits, primarily because they are rushing to fulfill their annual targets. The remaining two quarters are comparatively less rushed.

What happens to this scenario when the bank applies artificial intelligence to its systems? For the first and last quarters, the system could throw up the details of existing customers who would be likely to need new accounts, so that the bank officials could target them. This data-crunching could begin in the last quarter (for first-quarter acquisitions) and in the third quarter (for last quarter acquisitions).

For the second and third quarters too, which are usually leaner, the prospective clients for new acquisitions could be highlighted. What this whole setup would do after repeating for a few cycles is that the usual cycle of quarters would be disrupted, and acquisitions of new clients would be more uniform throughout the year.

The future looks very exciting for industries who are using artificial intelligence, and the possibilities seem immense.

Customer Service Trends – 2018 Is Making Operations Become Faster?

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A recent publication shows that a smart AI strategy ensures transformative customer service in times where the customer is spoilt for choices in every area, and how firms can use AI as a weapon to offer uniquely differentiated products on the back of its usage. Unlike common perception, the authors have demonstrated how usage of AI will make operations faster, more effective, and cheaper yet more human. It has various updates on chatbots and how they enhance the customer self-service experience at L1.

Usage of prescriptive AI for quelling headcount growth by taking over routine tasks and allowing agents to focus on deeper customer experiences.

There has been a  steady rise of virtual assistants like Siri & Alexa and they will become independent local hubs of customer experience. Visual engagement avenues such as co-browsing & screen sharing and the expected uptick in usage across age groups.

IOT will transform business models as It will allow production companies to provide proactive services for their high-end products, through preemptive on-site /user monitoring and reporting to a centralized center versus reactive service upon a product breakdown.

Usage of Robotic Process automation for improved delivery of repetitive tasks and end to end automation of basic processes allowing humans to take escalations. Machine learning along with this allows them to learn through interactions that they go through to become more cognitive and intelligent.

Enhanced field service by equipping agents with enough information and parts to ensure that they get the customer’s job done in a single visit. This also covers the usage of augmented reality along with digital interactions for deeper interactions in the physical world without physical presence.

The emergence of “superagents- equipped with AI tools” where companies will relook & redefine their workforce basis their skills and charge a premium for the usage of these services. There will also be seen a rise in customer service ecosystems where firms will use a combination of AI, their resources, and partners to see the customer through their entire journey and not just a portion of it.

With the above, we are looking at a new world order where Artificial intelligence will impact every aspect of our lives knowingly or unknowingly and transform the way we lead our lives including individual privacy and experiences.

Quit Playing Games With Artificial Intelligence – Its Serious Business Now!

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The gaming industry is no longer a simple and cheap way to keep restless children occupied during their summer vacations. People of all ages actually spend hours together in front of their gaming consoles playing a variety of games. The quality of games has improved beyond recognition today, keeping serious gamers glued to their games for long periods of time, helping make these games economically feasible.

One big change in recent years that has turned the gaming industry around is the development of artificial intelligence and virtual reality. Let us see a few ways in which this has happened.

Gaming Realism

We have seen how virtual reality helps to generate 3D images or an overall environment that interacts with reality. We have all been amazed and impressed to see a real character in a VR environment wave his or her hands in the air and conjure up a screen on which different dials and buttons are available. Similar virtual reality environments inside games have added a touch of the realistic to these games.

Adaptive Environment

Unlike the static code of earlier games, where a certain action X by a character or the player would result in a fixed outcome Y only. But with the introduction of artificial intelligence, the environment and the responses to actions could be varied, with the game throwing up different responses in different scenarios.

The Move to Responsive

Most of the activities we do today are moving from the computer to our mobile phones, like watching sports or news or looking at weather forecasts, ordering takeaway, booking tickets, etc. The situation is no different for the gaming industries. They are having to adapt to this scenario and create games that are easy to view and easy to maneuver on mobile phones. Responsiveness is the new buzzword.

Heavy Computing Power

This is a phenomenon observed in all our gadgets like computers, mobile phones etc. There has been a surge in computing power. This has affected gaming as well, with super-fast responses from the characters. This is because the gaming consoles now carry unbelievable computing power.

Machine Learning

Artificial intelligence in gaming consoles is encouraging the gaming programs to learn from past experience and adjust its responses accordingly, making the gaming experience more difficult for the players. The games are getting smarter because of the use of these artificial intelligence tools, making them all the more challenging for the players.

Real-Time Reactions

Games earlier were one-dimensional collections of graphics and code which threw up situations for the gamers, to which they would provide certain reactions, to which the game would again provide a certain response. This was done with the help of a detailed algorithm which dictated the machine’s response. But now, with AI, the events happening within the game would influence the reaction of the computer, and these changes in reaction would also go into its knowledge bank and contribute to its machine learning.

Developer Skills

One more aspect of the industry changes in the gaming industry is that the developers writing the code for these games now have to contend with all the changes listed above, and therefore have to pick up adequate skills for incorporating elements of artificial intelligence and virtual reality in these games.

Industry Change

The gaming industry is seeing far-reaching changes as a result of the addition of virtual reality elements into games. The gaming experience becomes much more rich and intense for the player, therefore making them more willing to fork out much higher prices for the games they buy. Advertisements linked to different games have also become more visible, providing gaming companies to look at a rich stream of revenue.

Should You Fear Machine Learning?

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Machine learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. The goal of machine learning is to get computers to learn in a similar manner to humans.

Machine learning is a type of artificial intelligence that helps computers learn without having to be programmed by a person. These computers are programmed in a way that focuses on data that they receive on a regular basis. This data can then help the machine “learn” what preferences are and adjust itself accordingly.

Nowadays, the development in Artificial Intelligence (AI) has brought us to the stage where organizations are using various algorithms, analysis, and experience to learn and program themselves without human intervention.

This type of procedure will create changes in too many industries. The use of machine learning has grown exponentially in the past few years, and you may not realize how widely it is used.
Following stats is just tip of the iceberg:

  • 85% of customer interactions will be managed without humans by 2020.
  • 38% of jobs could be replaced by AI/machine learning by the 2030s.
  • 20% of top executives rely on machine learning to run their businesses.
  • 48% projected growth in the Automotive Industry by 2025.


Source: jigtechnologies.com; elearninginfographics.com; pwc.co.uk; mckinsey.com