Let us unite,
And fight against those who incite,
Let’s progress with minds without fear,
And succeed in every sphere,
We are a strong nation,
All we need is a little bit of dedication,
Let us open our eyes,
And see our ‘Dream India’ rise.
Let us work sincerely to make India a self – reliant and a developed nation, together with the loyal legislature that makes laws, accountable executive that carries out the laws and trustworthy judiciary that evaluates the laws.
Machine learns through experience
Like humans,
machines also learn from experience. They analyse the existing data, figure out the pattern, and find what to do with the new data.
Learning from Medical records
based on combination of attributes i.e., symptoms, it determines an outcome variable i.e., whether the patient is covid positive or not by learning from the existing data.
Learning from Energy usage patterns
smart homes optimize the energy usage by learning from the energy usage patterns of its residents (by capturing the motion of residents), hence saving the electricity bills.
Learning from User interests
learns from user interests and gives suggestions and recommendations accordingly
Learning to target customers
learns from the travel history of customer and gives discounts, coupons, offers to the customers travelling more often during a certain period, to attract them to travel more (to retain the customer to themselves)
Machine learning in finance
Simultaneously multiple micro transactions to multiple accounts.
Giving loans based on credit score of the customer. (Credit worthiness)
Machine learning in medicine
Equipment maintenance records
Combustion chamber, rockets
The unsupervised learning algorithm can be further categorized into two types of problems:
Clustering
Clustering is a method of grouping the objects into clusters such that objects with most similarities remains into a group and has less or no similarities with the objects of another group.
Cluster analysis finds the commonalities between the data objects and categorizes them as per the presence and absence of those commonalities.
Association
An association rule is an unsupervised learning method which is used for finding the relationships between variables in the large database. It determines the set of items that occurs together in the dataset.
Association rule makes marketing strategy more effective.
Such as people who buy X item (suppose a bread) also tend to purchase Y (Butter/Jam) item. A typical example of Association rule is Market Basket Analysis.
Unsupervised Learning algorithms:
K-means clustering
KNN (k-nearest neighbors)
Hierarchal clustering
Anomaly detection
Neural Networks
Principle Component Analysis
Independent Component Analysis
Apriori algorithm
Singular value decomposition
Advantages of Unsupervised Learning
Fast process as task of data labelling is not involved.
Provides unique and disruptive insights.
Disadvantages of Unsupervised Learning
The result of the unsupervised learning algorithm might be less accurate as input data is not labelled, and algorithms do not know the exact output in advance.
Difficult to measure accuracy
Dealing with high-dimensional data- when the dimension of data and the number of variables is more, the process becomes difficult.
As the name suggests, unsupervised learning is a machine learning technique in which models are not supervised using training dataset.
Instead, models itself find the hidden patterns and insights from the given data.
It is much similar as a human learns to think by their own experiences, which makes it closer to the real AI.
In real-world, we do not always have input data with the corresponding output (training or labelled data referred to as supervisor). So, to solve such cases, we need unsupervised learning.
Steps:
Step 1: The very first step is to load the unlabeled data into the system.
Step 2: Once the data is loaded into the system, the algorithm analyses the data.
Step 3: As the analysis gets completed, the algorithm will look for patterns depending upon the behavior or attributes of the dataset.
Step 4: Once pattern identification and grouping are done, it gives the output.
Example: Input dataset containing images of different types of fruits.
Now, let’s take these fruits and feed them to an unsupervised learning model.
The model determines the features associated with the data and understands that all the apples are similar in nature and thus groups them together.
Similarly, it understands that all the oranges have the same features and thus group them together and the same is the case with all the mangoes (in case we have mangoes in this example)
Here, the unsupervised learning algorithm will perform this task by clustering the image dataset into the groups according to similarities between images.
Example 2:
For instance, given a data base of movie reviews, you could identify clusters of users who rate action movies similarly, and use those correlations to predict how one member might like a particular movie he had not yet seen, but others have rated.
Supervised learning can be further divided into two types of problems:
Classification algorithms are used when the output variable is categorical, which means there are two classes such as Yes-No, Male-Female, True-false, etc.
Multiple classes may also be present.
The output variable or the dependent variable should be categorical in nature.
Example: Diagnosis
“Prone to lung cancer” (output variable) is the dependent variable and “Weight” and “Number of cigarettes smoked” are the independent variables.
Regression algorithms are used if there is a relationship between the input variable and the output variable. It is used for the prediction of continuous variables, such as Weather forecasting, Market Trends, etc.
Let’s say you have two variables, “Number of hours studied” & “Number of marks scored”. Here we want to understand how the number of marks scored by a student change with the number of hours studied by the student, i.e.
“Marks scored” is the dependent variable, and “Hours studied” is the independent variable.
You need to note that “marks scored” is the dependent variable and it is a continuous numerical.
Question: “How many hours should a student learn to get 60 points?”
Ans: The regression model would understand that there is an increment of 10 marks for every extra hour studied and to score 60 marks the student must study for 6 hours.
Example: Weather app in our mobile
This app predicts the weather of the entire next week. How does it do?
By analysing the previous data (say past 10 years weather report data) and predicts the pattern for the next week.
Here, since we deal with large amount of data, it may be difficult for humans to work on it. Hence, the machines are fed with large amount of data and made to predict the future data parts.
In supervised learning, models are trained using labelled dataset, where the model learns about each type of data.
Once the training process is completed, the model is tested based on test data (a subset of the training set), and then it predicts the output.
Example:
Task
Suppose we have a dataset of different types of shapes which includes square, rectangle, triangle, and Polygon. Now the first step is that we need to train the model for each shape.
Training Experience
If the given shape has four sides, and all the sides are equal, then it will be labelled as a Square.
If the given shape has three equal sides, then it will be labelled as a triangle.
If the given shape has six equal sides, then it will be labelled as hexagon.
Now, after training, we test our model using the test set, and the task of the model is to identify the shape.
Performance
The machine is already trained on all types of shapes, and when it finds a new shape, how well it classifies the shape based on number of sides and predicts the output.
Steps involved
References
The machines are trained using well “labelled” training data, and on basis of that data, machines predict the output.
The labelled data means some input data is already tagged with the correct output.
Supervisor is this training data (labelled data) which helps to predict the output correctly when a new input data point is given as input.
The aim of a supervised learning algorithm is to find a mapping function to map the input variable(x) with the output variable(y).
Step 1: The very first step of Supervised Machine Learning is to load labelled data into the system. This step is a bit time-consuming because the preparation of labelled data is often done by a human trainer.
Step 2: The next step is to train and build connections between inputs and outputs(function). This step is also known as the training model.
Step 3: Then comes the step known as the testing model. As the name suggests, you test the model by introducing it to a set of new data.
Here, the input is an independent variable, and the output is a dependent variable. The goal is to generate a mapping function that is accurate enough so that the algorithm can predict the output when we feed new input.
Example of labelled data:
We have a labelled dataset that consists of images of apples and oranges, with different attributes such shape, colour etc.
Consider the image of an apple shown above with the labels- shape, colour, and apple.
We train the model with this image. Then, we repeat the same training process with other images of both apples and oranges with their attributes.
What we are doing is-
Here, the input data is the independent variable and “Apple” or “Orange” is dependent variable as it is dependent on the input picture given.
Our goal is to generate a mapping function between the dependent and independent variable so we can determine the output when we feed a new data point.
Once the model is trained and the algorithm is built, the accuracy can be tested with the help of a test dataset.
When we feed the model with a new apple image, it scans the image and matches the attributes of the image with other trained images. Then depending upon the accuracy of the model, it returns the output ‘apple’.
When new data point is given as input, say,
The machine should be able to guess the output as “Apple”.
This labelled data or the training data (acts as supervisor), helps to predict the output as “Apple”.
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
Machine learning algorithms are readily available. For example, Python has many libraries which support these machine learning algorithms.
Using this we can:
Then why to learn the basics when instead we can use the pre-built algorithms that are readily available?
Still, we should learn the basics (the mathematics behind these concepts) of machine learning to be able to take an informed decision about the performance of the model.
We can fit the model to a data set and get some performance, but how to justify whether the performance given by the machine learning algorithm is proper or not?
The design of experiment
To be able to take the decisions with confidence we should know the fundamental concepts.
Category management is a categorisation of products according to buyers’ shopping habits. It is used within any retail store, including the supermarket or its website with the possibility of ordering goods for home delivery. Category management for one retail chain (for example, several supermarkets) is similar to physical stores and online trading. However, the categories may vary from point to point. This is because it depends on the customers who often visit the store.
What does a manager have to know when starting to work on categories in the supermarket?
It’s going to be difficult to analyze customer segments
Simply because they are going to be buying goods offline. When you move at least 50 per cent of your goods online to collect goods in-store or pay for home delivery, the whole situation will improve. Although, this still does not guarantee that the customer segments will meet your expectations. Yes, you can track customers through recurring patterns in receipts. You can also come up with hypotheses based on the data obtained from your competitors and other market analyses. Still, you won’t be able to study your customers under a microscope.
The cost of a product will often take precedence over its other qualities
It is a major problem for category management in supermarkets and retail in general. Most likely, grocery store visitors care more about the product’s price, and low cost always wins over other characteristics. Therefore, it will be more challenging to create categories: inevitably, you should be guided by the price of the goods.
At some point, it will be difficult to fight off competitors
You will be selling the same items which are available in other supermarkets. Some manufacturers may agree to cooperate exclusively with your chain of stores; however, this is unlikely because this arrangement is simply unprofitable for the supplier. Moreover, it’s impossible to lower the bar and start undercutting, that is, trading at a loss. After all, a business needs to earn money. In some instances, it may only be achievable through introducing discounts.
You will be able to test hypotheses only on a small number of consumers
Why? Most trade will occur in offline stores rather than in their online equivalents. And this, as we remember, complicates the task of marketers and, unfortunately, category managers. Only a small percentage of consumers shop online. That makes sense: you have to pay for delivery. You can’t select your products or ensure that all your fruit and vegetables are intact. Your delivery guy might also be late or you may face logistical problems like mixed-up orders.
How can you test your hypotheses on those customers who have turned to your supermarket website? First, you have to study your portal analytics: how users have found you, how they behave on the site, which tabs they open first, and how their filter products in various categories. If you have social media accounts, be sure to use them. The statistics offered by Instagram will demonstrate who your customers are and how they behave. By understanding the core of your audience accessing your resources, you can create category management hypotheses and test them on users before displaying the goods in stores. Of course, many of your customers will not shop online. Still, you can learn all about them, for example, through their choice of purchases on receipts.
The variety of categories will depend on the location of the supermarket
This limits your ability to influence the profits of the business. The categories will depend largely on areas where your shops are located, as well as people who live there and their buying power. For example, it is pointless to introduce a category of personal care products if jars of face cream have been sitting on the shelves for months in a particular store. It makes no sense to reduce the number of household goods with an above-average price tag if they sell like hotcakes. If you work in a supermarket in a deprived area, be prepared for a succession of identical categories with low-cost products.
Start learning category management now to secure a job within this field in a few weeks! This area has been actively booming in India and the surrounding regions. Therefore, your knowledge will definitely be in demand.
Of all the different types of media available to us today, podcasts may be the ones we have the least exposure to. Even while podcasts are less popular compared to other media forms like videos and blogs, the number of podcast listeners has risen steadily each year since it started in 2004. The Covid-19 pandemic and the worldwide lockdowns are what kickstarted my habit of listening to podcasts. Whether the episode features experts’ advice on topics like mental health and exercise, celebrity interviews, or film and TV show reviews, listening to podcasts has been both educational and entertaining for me.
When I talk to people about podcasts, a lot of them are confused about what it is. A podcast refers to a series of digital audio files prepared and distributed through RSS feeds or apps (Spotify, Apple Podcasts, etc), that can be accessed through the internet or downloaded. The audio files can be about anything – you name it, you can probably find podcast episodes on it. It can be news, comedy, science, and niche topics. You can find great reviews of new and old films, TV shows and series. Podcasting initially started as an easy and inexpensive way for individuals like bloggers and musicians to communicate their ideas and opinions and build a close community of similar-minded people. Comedian Joe Rogan’s The Joe Rogan Experience is the most popular podcast globally. Dear Hank and John by authors and YouTubers John and Hank Green discuss almost everything their listeners want answers to. Podcasts today have also been adopted by large and small companies, radio networks (BBC, for example), and media organizations like NPR and New York Times. For a lot of businesses, it is an effective content marketing technique for cultivating brand loyalty and customer relationships.
One of the greatest advantages of listening to podcasts is how easy it is to access. Users can listen to any podcast from their mobile phones at any time of the day. If you are like me, you will appreciate how it makes a lot of your daily chores like cleaning or washing dishes fun. It makes multitasking so much easier. You can listen and learn new things while you go through your daily chores or travelling to work/class. It is also a very effective way of reducing the time you spend looking at the laptop or mobile phone screen. Listening to podcasts is a great way of learning and unlike visual media, it doesn’t exert strain on your eyes. It also keeps your brain active as you use your imagination more with podcasts. The vast variety of topics on which podcasts are made also means you get to learn unique stuff often directly from the subject experts. That is a lot of education and entertainment guaranteed.
Many popular podcasts have seasons with a series of episodes in each season. These episodes may be released daily, weekly, or at any time the podcaster wishes. You can subscribe to podcasts and get notified when a new episode is posted. People get into podcasting because it is a comparatively easier and cheaper way of producing your own show. You do not need to depend on radio broadcast stations and can talk about anything you like. You just have to make sure you have the right audio recording equipment and audio editing software, and choose the right topic to talk about. Podcasts can go as short as a few minutes or be longer than an hour. You can use any of the popular podcast hosting websites and apps to search for podcasts on the topics you like.
As regular consumers of the media and the internet, all of us must have come across some form of content marketing at some point. Content marketing refers to the creation and distribution of valuable, relevant content for a clearly defined audience to cause a profitable customer action. The goal of content marketing is to provide valuable information to the audience through content such that they willingly consume it rather than avoiding it. We build brand loyalty and trust by providing solutions to problems faced by them.
There are mainly 5 forms of content marketing we can see today. They are:
Many brands use blogs, often including it as part of their website. It is one of the most popular forms of content marketing techniques, being very cost-effective for small businesses. Blog posts usually contain written material about useful information on topics related to your product or service. When the written material provides relevant knowledge and effective solutions to customers’ problems, they earn an audience and build good customer relationships. The length of the post depends on the topic of the blog, audience demographics and the writer’s unique writing style. Whether it is long or short in length, make use of catchy headlines and simple language. Search Engine Optimization (SEO) also comes into play in driving web traffic to the blog.
The immersive field of podcasting has seen a rise in its number of listeners every passing year. Podcasts are audio content posted on topics ranging from interviews, discussions to reviews on the internet. Even though it is a smaller market compared to blogging, it still garners a loyal group of listeners because of how easy it is to indulge in podcasts. Unlike blogs and videos, the audience can listen to podcasts while doing anything else like travelling or cleaning. Listening to the podcaster’s voice, the information they give and their opinions on stuff builds a stronger relationship with the audience, They choose topics closely related to their product or service and post content consistently to various podcast hosting platforms like Spotify, Google Podcast, Apple Podcast, etc. Quality audio recording equipment and software are also important for good podcasts.
Nobody can deny how popular videos are among the general public. All of us watch YouTube at least once a day and short-form videos on platforms like TikTok and Instagram are all the rage. Videos could be vlogs, reviews for products, step-by-step guides or even animated/sketched whiteboard videos. While it is expensive and time-consuming, it is also very efficient as it can communicate quicker than any other form of content marketing, provided it is produced in the right setting with good characters and equipment. Whether they are product reviews or instructional videos, they are the closest you get to having real contact with someone. The video content you created can also be used in other forms, like in social media and blogs.
Infographics refer to long representations of graphs, statistics, and other relevant information. You may have across interesting graphical representations of information and statistics related to various interesting topics. These simple, valuable graphics are useful in summarizing and breaking down the important points of a complicated, large area of study or a survey. A well-made infographic can grab a lot of attention. It usually gets shared a lot on the internet and on social media. They can be supporting content for blogs or standalone content.
Everybody has social media these days and it goes without saying that it is one of the easiest and most effective forms of content marketing. Posting consistently and making relevant and shareable content is essential for social media marketing. When you have such a large amount of people online exposed to your content, you need to make sure your content is relatable and engaging with the audience. Make use of social media analytics to figure out which social media platform and what kind of content (written, videos, etc) works best with your audience.
Due to the pandemic, there was a complete turn of events, and the children are attending classes online at peak times. Though the situation is getting normal these days, it is essential and beneficial to have a school ERP software that helps maintain accurate academic and operational excellence information.
What is School ERP Software?
A school ERP is an advanced technology where you can perform a set of programs specially made to perform administrative tasks of a school in an appropriate manner. This is also known as school management ERP software that can manage all the tasks without the presence of a person, pen, and paper.
This is a software platform where all the people, including students, teachers, principals, parents, and management, can access the software to know vital information.
Uses:
● Teachers can spend their most time teaching and progress of the student
● Parents get aware of their child’s performance
● Easy to handle all the administrative tasks
● Principals can observe and take action, respectively
Different modules are present in the ERP software, do consider while choosing the best one for your school.
1. Online Registration:
A student can get registered on a school database by filling out all the details like date of birth certificate, Aadhar card, parents information, etc. This is a kind of eligibility, where schools allow admission from any side of the world. Parents have to fill out the form about the student details and then submit the form.
Benefits:
This process is time-saving; you can access it globally and eco-friendly.
2. Student Information Module:
This is the primary and most significant module and acts as a central part of the entire software. This is the main part of the software where all the modules are interlinked to this module to check all the data with just one click. Any information can generate in the software system, including fees due, attendance, etc.
Benefits:
It is easy to use, simple to get the details, and a hassle-free process. Some divisions are separated session-wise to simplify the process.
3. School Fee Management:
It helps every school collect the school fees online, and the software can also maintain an accurate database of every transaction. There is a process where notifications can be sent to parents in case of late payment, due fees, etc.
Benefits:
It is a simple process for both parents and school management; the benefits include no late fees, online payment, reconciliation, and checking for defaulters.
4. Exam and Result Management:
This module can present wonderful report cards and check them with just one click. Parents can view these report cards using the school ERP software, and it is a great advantage for parents as they cannot visit the school frequently.
Benefits:
The software is simple and easy to analyze student performance by comparing the past and present results. There are no irrelevant results or misprinting; it is highly reliable and shows accurate results.
5. School Library Management:
This is an excellent module as the software can handle a lot of books, magazines, textbooks, videos, newspapers, school albums, etc., by an automated process.
Benefits:
No need to worry about issuing the books; you can check the database for a specific book that is available or not. No delays while you are issuing the book.
Bottom Line:
<
p class=”MsoNormal” style=”line-height: 15.693333625793457px;margin-bottom: 8pt”>You can easily select the best ERP software for your school by considering all the modules, making things and processes more simple for your convenience.
When plastic waste is burnt, a complex weave of toxic chemicals is released. Breaking down polyvinyl chloride (PVC) — used for packaging, toys, and coating electrical wires — produces dioxin, an organochlorine that belongs to the family of Persistent Organic Pollutants (POPs). A recent Dioxin Assessment Report brought out by the United States Environment Protection Agency (USEPA) says the risk of getting cancer from dioxin is ten times higher than reported by the agency in 1994.
Yet the Delhi government is giving the green signal to a gasification project which will convert the garbage into energy without removing plastic waste. Former transport minister Rajendra Gupta, the promoter of this project, says this is not necessary.
He claims no air pollution will be caused and that the ash produced can be used as manure. An earlier waste-to-energy project set up in Timarpur failed. The new one, built with Australian assistance, will cost ₹ 200 crores. It will generate 25 megawatts of power and gobble 1,000 tonnes of garbage every day.
“Technologies like gasification are a form of incineration,” says Madhumita Dutta, a central coordinator with Toxics Link, New Delhi. Incineration merely transfers hazardous waste from a solid form to air, water, and ash, she points out.
Toxins produced during incineration include acidic gases, heavy metals as well as dioxins and furans. “The ‘manure’ will be hazardous and a problem to dispose of,” says Dutta.
Municipal solid waste contains a mix of plastics. Breaking down this waste emits hydrochloric acid which attacks the respiratory system, skin, and eyes, resulting in coughing, vomiting, and nausea.
Polyethylene generates volatile compounds like formaldehyde and acetaldehyde, both suspected carcinogenic. Breathing styrene from polystyrene can cause leukemia. Polyurethane is associated with asthma. Dioxin released by PVC is a powerful hormone disrupter and causes birth defects and reproductive problems. There is no threshold dose to prevent it and our bodies have no defense against it.
“Even the best run incinerators in the world have to deal with stringent norms, apart from contaminated filters and ash, making them hugely expensive to operate,” says Dutta. In Germany, air pollution devices accounted for two-thirds of the cost of incineration. Despite such efforts, the European Dioxin Inventory noted that the input of dioxin into the atmosphere was the highest from incineration.
“India does not have the facility to test dioxin and the cost of setting one up is prohibitively expensive,” says Dutta.
Besides, Indian garbage has a low calorific content of about 800 cal/kg, since it has high moisture and requires additional fuel to burn. Toxics Link calculates that the electricity generated from such technology will cost between ₹ 5-7 per unit, which is six times higher than conventional energy. India has chosen a dioxin preventive route and burning of chlorinated plastics is prohibited under Municipal Solid Waste and Biomedical Rules.
Nearly 80 percent of Indian garbage is recyclable or compostable. Resident associations, the informal sector, and the municipal corporation can make Delhi’s garbage disappear in a sustainable manner. “Instead, the government promotes the end of pipeline solutions,” says Dutta.
The National Education Policy 2020 envisages an inclusive and structural change in the educational system. It focuses on ‘Equitable and Inclusive Education’ which reverberates the idea that no child should be left behind in terms of educational opportunity because of their background and socio-cultural identities. It has taken into account the concerns of the Socio-Economically Disadvantaged Groups (SEDGs) which includes female and transgender individuals, Scheduled Castes, Scheduled Tribes, OBCs, minorities and other categories. This policy aims at bridging the social category gaps in access, participation, and learning outcomes in school education.
Department of School Education and Literacy (DoSEL), Ministry of Education is implementing Samagra Shiksha scheme, effective from 2018-19. Bridging gender and social category gaps at all levels of school education is one of the major objectives of the scheme. The Scheme reaches out to girls, and children belonging to SC, ST, Minority communities and transgender. The Scheme also focuses on the identified Special Focus Districts (SFDs) on the basis of adverse performance on various indicators of enrolment, retention, and gender parity, as well as concentration of SC, ST and minority communities.
Under Samagra Shiksha, there is a provision of Kasturba Gandhi Balika Vidyalayas (KGBVs). KGBVs are residential schools from class VI to XII for girls belonging to disadvantaged groups such as SC, ST, OBC, Minority and Below Poverty Line (BPL). The KGBVs are set up in Educationally Backward Blocks. The objective behind establishing KGBVs is to ensure access and quality education to girls from disadvantaged groups by setting up residential schools and to reduce gender gaps at all levels of school education.
Currently there are 5627 sanctioned KGBVs across the country in which 6,65,130 girl students belonging to various disadvantaged groups are enrolled.
The Scheme also covers Children with Special Needs (CwSN) from pre-primary to senior secondary level across the country. There is a dedicated Inclusive Education component for the education of CwSN as an integral part of Samagra Shiksha. In keeping with the principle of inclusion, CWSN are provided support through specific student oriented interventions i.e. aids and appliances, teaching aids, assistive devices, identification and assessment camps, teaching and learning materials, orientation program for the parents, education administrators, community, sports events, world disability day, braille books/kits, corrective surgeries, transportation allowances, escort allowances, uniforms (under RTE), stipend for girls, training of general teachers, financial assistance for special educators, use of ICT etc.
Under Student Oriented Component assistance is provided to States/Union Territories @ Rs. 3500/- per Child with Special Needs for both school going and children who cannot attend school (home based education) pertaining to the severity of the disability.
Girls with disabilities receive special focus and efforts under the scheme to help them gain access to schools, as also to provide motivation and guidance for developing their potential. An amount of Rs. 200 per month for 10 months ( Rs.2000/- PA), through Direct Benefit Transfer, is given to girls as stipend to encourage them to come to school.
In the year 2021-22, girls’ stipend is provided to 6.12 lakhs girls with special needs. The total outlay approved was Rs.122.57 Crores.
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