Here are the top 5 PHP frameworks that every developer should learn:

1. Laravel

Laravel is a powerful, full-stack PHP framework with a great community and a focus on elegance and simplicity. It is a popular choice for web development projects of all sizes, from small blogs to large enterprise applications. Laravel is known for its expressive syntax, powerful features, and large ecosystem of libraries and packages.

2. Symfony

Symfony is another popular PHP framework with a strong focus on modularity and flexibility. It is the foundation for many other PHP frameworks, including Drupal and Magento. Symfony is a good choice for developers who need a framework that is highly customizable and can be adapted to a wide range of projects.

3. Yii

Yii is a high-performance PHP framework that is known for its speed and scalability. It is a good choice for developers who need to build web applications that can handle a lot of traffic. Yii is also a good choice for developers who are looking for a framework with a large and active community.

4. CodeIgniter

CodeIgniter is a lightweight PHP framework that is easy to learn and use. It is a good choice for developers who are just starting out with PHP or who need to build a simple web application quickly. CodeIgniter is also a good choice for developers who are looking for a framework with a small footprint.

5. Slim

Slim is a micro-framework that is designed to be as small and simple as possible. It is a good choice for developers who need to build small, RESTful APIs or web applications. Slim is also a good choice for developers who are looking for a framework that is easy to learn and use.

These are just a few of the many great PHP frameworks that are available. The best framework for you will depend on your specific needs and preferences.

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The following are the most important deep learning algorithms that programmers should know about in 2021.

Deep learning algorithms train machines and it uses artificial neural networks to perform sophisticated computations on large amounts of data. It is a type of machine learning that works based on the structure-function of the human brain. While deep learning algorithms feature self-learning representations, they depend upon ANNs that mirror the way the brain computes information.

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Convolutional Neural Network

CNN’s, also known as ConvNets, consist of multiple layers and are mainly used for image processing and object detection. Yann LeCun developed the first CNN in 1988 when it was called LeNet. It was used for recognizing characters like ZIP codes and digits. CNN’s are widely used to identify satellite images, process medical images, forecast time series, and detect anomalies

Long Short Term Memory Networks

LSTMs are a type of Recurrent Neural Network (RNN) that can learn and memorize long-term dependencies. Recalling past information for long periods is the default behavior. LSTMs retain information over time. They are useful in time-series prediction because they remember previous inputs. LSTMs have a chain-like structure where four interacting layers communicate uniquely. Besides time-series predictions, LSTMs are typically used for speech recognition, music composition, and pharmaceutical development.

Recurrent Neural Networks

RNNs have connections that form directed cycles, which allow the outputs from the LSTM to be fed as inputs to the current phase. The output from the LSTM becomes an input to the current phase and can memorize previous inputs due to its internal memory. RNNs are commonly used for image captioning, time-series analysis, natural-language processing, handwriting recognition, and machine translation.

Generative Adversarial Networks

GANs are generative deep learning algorithms that create new data instances that resemble the training data. GAN has two components: a generator, which learns to generate fake data, and a discriminator, which learns from that false information. The usage of GANs has increased over some time. They can be used to improve astronomical images and simulate gravitational lensing for dark-matter research. Video game developers use GANs to upscale low-resolution, 2D textures in old video games by recreating them in higher resolutions via image training.

Radial Basis Function Network

RBFNs are special types of feedforward neural networks that use radial basis functions as activation functions. They have an input layer, a hidden layer, and an output layer and are mostly used for classification, regression, and time-series prediction.

Multilayer Perceptions

MLPs are an excellent place to start learning about deep learning technology. MLPs belong to the class of feedforward neural networks with multiple layers of perceptrons that have activation functions. MLPs consist of an input layer and an output layer that is fully connected. They have the same number of input and output layers but may have multiple hidden layers and can be used to build speech recognition, image recognition, and machine-translation software.

Self Organizing Maps

Professor Teuvo Kohonen invented SOMs, which enable data visualization to reduce the dimensions of data through self-organizing artificial neural networks. Data visualization attempts to solve the problem that humans cannot easily visualize high-dimensional data. SOMs are created to help users understand this high-dimensional information.

Deep Belief Network

DBNs are generative models that consist of multiple layers of stochastic, latent variables. The latent variables have binary values and are often called hidden units. DBNs are a stack of Boltzmann Machines with connections between the layers, and each RBM layer communicates with both the previous and subsequent layers. Deep Belief Networks (DBNs) are used for image recognition, video recognition, and motion-capture data.

Restricted Boltzmann Machine

Developed by Geoffrey Hinton, RBMs are stochastic neural networks that can learn from a probability distribution over a set of inputs. This deep learning algorithm is used for dimensionality reduction, classification, regression, collaborative filtering, feature learning, and topic modeling. RBMs constitute the building blocks of DBNs.

Autoencoders

Autoencoders are a specific type of feedforward neural network in which the input and output are identical. Geoffrey Hinton designed autoencoders in the 1980s to solve unsupervised learning problems. They are trained neural networks that replicate the data from the input layer to the output layer. Autoencoders are used for purposes such as pharmaceutical discovery, popularity prediction, and image processing.

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The degrees will include MBA, MCA, and BBA with specialization in AI, Data Science, Full Stack Development, Business Analytics, Digital Marketing and E-commerce, and Cloud Computing.

Great Learning, an EdTech company for professional and higher education, announced collaboration with JAIN (Deemed-to-be University), Bengaluru, to offer contemporary online degree programs in high-demand domains seeking a skilled workforce. This collaboration offers MBA, MCA, and BBA programs with specialties in Digital Marketing, Data Science, Full Stack Development, Cloud Computing, and Artificial Intelligence.

These UGC recognized degree programs have been designed by academia and industry to help students master core concepts in various specializations. In addition to the degree from the University, the students will also receive an Advanced Professional Certificate from Great Learning.

These programs, which will be delivered online, working professionals too can continue to work and simultaneously get their degree in future-focused, high-growth domains. The MBA program in Digital Marketing and Data Science includes new-age skills like Artificial Intelligence, Data Analytics, Python, Predictive Analytics, and Web and Social Media Analytics, along with the traditional curriculum. The MCA programs cover some of the most sought-after skills by the industry including Python, SQL & NoSQL, Microsoft Azure, AWS, Data Visualization, Software Engineering, Business Analysis, and Data Communication.

the programs include practical projects that would help learners develop acumen in solving real-world business problems and gaining hands-on experience in the application of these skills. Upon the completion of the programs, learners will receive a master’s or bachelor’s degree from JAIN (Deemed-to-be University).

Great Learning will also provide placement assistance to the learners and connect them to the right job opportunities through its placement assistance program, GL Excelerate. The platform has partnered with 400+ leading organizations including JP Morgan, Barclays, Tata Consultancy Services, Accenture, HSBC, Bank of America, Capgemini, and Cognizant.

The launch of these programs comes after the recent announcement by the government under the New Education Policy that enables universities to offer online degrees.

Mohan Lakhamraju, Founder and CEO, Great Learning said, “With digital skills like Data Science, Analytics, AI, Machine Learning, Cloud Computing, Digital Marketing becoming critical building blocks for most roles across industries, it is essential to include them as a part of the curriculum.”

Raj Singh, Vice Chancellor, JAIN (Deemed-to-be University) said, “In the era when the Universities as we have known them, are beginning to change and the future is certainly going to belong to micro-credentials offered by the best faculty from across the world, two categories of the audience will be the beneficiaries; the ones who need to upskill for emerging job profiles without leaving their current jobs in ‘anything, anywhere and anytime’ mode and those who haven’t had access to quality education yet. By serving these two categories of learners through the UGC-approved online degrees, JAIN (Deemed-to-be University) will not only help the Government to achieve a higher Gross Enrolment Ratio (GER) but also make a sustainable impact on the society, as enshrined in its vision and mission. The specialized certifications by Great Learning in Industry 4.0 technologies will place the students a notch above their contemporaries.”

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Researchers have embraced transfer learning to address algorithm challenges

Advanced machines never fail to leave men in awe. But only researchers who worked behind the machines know how much time, cost and data it took to become a stage stealer. Training an algorithm that employs various features in a machine is quite nerve-wracking. But tech geeks have found a solution using transfer learning. Besides, companies are also unveiling a mixture of technologies like deep learning neural networks and machine learning to come up with futuristic machines.

We are often surrounded by the myth that number-crunching gets cheaper all the time. According to Moore’s law, the number of components that can be squeezed onto a microchip of a given size can double every two years with the amount of computational power available at a given cost. This idea might suggest the opinion that the cost of training a machine is falling. But that is not true. Just because data is everywhere and is easily available doesn’t mean they are open to use and inexpensive in any way. Even when the data is open for accessibility, training an algorithm takes much more effort than any other computational process. Industry analysts anticipate that worldwide spending on artificial intelligence will reach US$100 billion in 2024, double of what it is today.

The advantage of machine learning and artificial intelligence algorithm is that they can easily understand information, act and interact with our environment in the most natural and human way possible. But the performance of the models depends highly on the calculation power allocated, and the quantity and quality of data. A study conducted by Dimensional Research unravels that around 96% of organizations run into a problem with training data quality and quantity. Besides, the study also claims that most machine learning model projects require more than 100,000 data samples to perform effectively. A machine learning system is still programmed with standard one-and-zero logic, but it can modify its behavior to meet specialized goals based on patterns it discovers in the sample data. Henceforth, machine learning algorithm needs to be trained with good data, which means data is optimized according to the issue you are dealing with. Fortunately, transfer learning can help as it takes knowledge gained from a pre-trained model that was used to solve a specific task and applies it to a different, but a similar problem within the same domain. Additionally, a mixed array of technologies like deep learning neural networks and machine learning are also making the training process less burdening.

Transfer learning addresses algorithm challenges

Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task. The technology is seen as a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks, given the vast compute and time resources required to develop neural network models on these problems and from the huge jumps in a skill that they provide on related problems.

Remarkably, with the help of transfer learning, instead of starting the learning process from scratch, you start from patterns that have been learned when solving a different problem. This way, you leverage previous learning and avoid starting from nothing. Transfer learning is usually expressed through the use of pre-trained models that were trained on a large dataset to solve a problem similar to the one that we want to solve. One of the well-known examples of transfer learning is GPT-3, the largest natural language machine learning model ever built. GPT-3 is a language prediction model where an algorithm structure is designed to take one piece of language and transform it into what it predicts is the most useful following piece of language for the user. Behind the mechanism are machine learning, deep learning and transfer learning technologies that help the model to produce humanlike predictive text.

Other than this, big tech conglomerates like Microsoft, AWS, NVIDIA, IBM, etc. have leveraged the help of transfer learning toolkits to remove the burden of building models from scratch, address the data quality and quantity challenges and expedite production machine learning.

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Sean Nierat from PayPal has explained to The Paypers why there is a need of systems that not only make accurate predictions, but also that explain why they’ve arrived at a particular answer 

Machine learning (ML) is part of a burgeoning AI industry that could soon become a multitrillion-dollar opportunity for global businesses. It is being used by PayPal, and others to help pioneer advanced data-driven fraud prevention by enhancing human intelligence with a 360-degree view of each customer. Yet, as ML becomes ubiquitous, it’s increasingly being argued that we not only need systems to make accurate predictions but also ones that explain why they’ve arrived at a particular answer. 

Tackling bias with transparency 

We’ve come a long way from the old days of fraud prevention. It’s undeniable that bad actors are getting smarter, with huge volumes of readily accessible customer data at their disposal and a wealth of tools bought on the dark web. Sophisticated fraud built on these foundations demands an equally sophisticated response. That’s why PayPal uses advanced ML to continually optimize the complex rules written by our client’s in-house fraud and data science teams, and to apply these rules to large datasets in order to spot patterns that humans may miss. 

The problem with such systems is that they’re only as good as the data they’re trained on. Increasingly, organizations are concerned about unconscious bias emanating from this data, and the algorithms designed to interpret it. With ML used today in everything from mortgage application approvals to police facial recognition systems, there are important questions to answer – especially in a new era of intense regulatory scrutiny. 

Clear box vs black box 

This is where clear box ML or ‘explainable AI’ (XAI) approaches come into their own. Black box models like artificial neural networks (ANNs) or deep learning operate so that even the humans that designed them don’t know how decisions are made. However, with XAI, businesses gain vital insight into the whole process, from data collection to decision making. 

This additional clarity and transparency offers multiple benefits including: 

• improves business confidence in an XAI-powered prediction/ outcome; 
• enhances the ability to control and manage algorithms in line with business objectives; 
• increases accountability, as systems can be audited; 
• improves regulatory compliance efforts; 
• enables teams to identify new fraud patterns faster.

A new approach 

PayPal’s enterprise Fraud Protection offerings champion clear box, advanced ML through our use of explainability methods like LIME, Shapley, and RL-LIM. Our prediction engine delivers an interpretability plot for every single event, helping to drive customer confidence in the results and continued ongoing improvements. 

Our platform is purpose-built to handle both the complex fraud challenges businesses face today and to make the necessary adjustments to help address those of tomorrow. With PayPal, businesses can take a dynamic approach to fraud – streamlining the experience for good customers and adding protection layers when necessary. 

PayPal leverages fraud and risk knowledge from its 2-Sided-Network of over 330 million customers and 25 million merchants transacting 12 billion times a year as well as integrated third-party feeds to enable the processing and correlation of vast amounts of heterogeneous data to help deliver actionable business intelligence. 

Here’s how: 

• a purpose-built data lake stores structured and unstructured data from various sources; 
• powerful Device Recon analyses hundreds of mobile and desktop device characteristics and behaviours, and applies machine learning models for risk scoring and clustering; 
• easy-to-update rules and machine learning algorithms help businesses adapt to changing fraud schemes; 
• robust link analysis and data visualization help enable businesses to proactively uncover anomalous patterns indicative of fraud; 
• real-time complex authentication helps differentiate trusted from suspicious users.

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Dr. V.N. Manjunath Aradhya, Associate Professor and Head, Department of Computer Applications, JSS Science and Technology University, Mysuru, has developed a model for detecting COVID-19 from chest X-ray images.
This concept has an advantage of learning from a few samples. The model proposed is a multi-class classification model as it classifies images of four classes — pneumonia bacterial, pneumonia virus, normal, and COVID-19. It has also been experimentally observed that the model has a superior performance over contemporary deep learning architectures. The proposed concept is the first-of-its-kind in the literature and expected to open up several new dimensions in the field of machine learning.

This research article was recently accepted in one of the top tier Journal, Cognitive Computation, Springer. This work is a combined effort with Prof. D. S. Guru of University of Mysore (UoM) and Prof. Mufti Mahmud of Nottingham Trent University, UK. Recently, Dr. Aradhya also published papers on understanding and analysis of COVID-19 which is co-authored with Prof. G. Hemantha Kumar, Vice-Chancellor, UoM, according to a press release from Dr. S. A. Dhanaraj, Registrar of the University.

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In the last few years, there has been a dramatic shift, from industry to industry, capturing trends and successfully sustaining culture, as the world unlocked the digital age. Technology, with it, brought a global change which led to a transformation in business, society and lifestyle. This, despite as suggested by many, is not a product of the pandemic. COVID-19, however, fueled the process.

We, as humans, are committed to a growth-driven society and with technology, we aim to succeed and prosper. Digitization might be going down as one of the biggest innovations in human, something that has caused us to reconsider how we interact and process.

One thing that has been constant in the ever-evolving world are humans. Humans remain the same.

In the past, steam, electricity, computer & internet have been the drivers of the industrial revolutions. As we move closer to the era of artificial intelligence, along with algorithms and advanced computation facilities, artificial intelligence will be driven by the availability of real-world data. The challenge, however, is to combine human intelligence with artificial intelligence for a better future. Humans need to be the nucleus of this era.

But, if this is the fact and the future, then who is the best bridge to connect the human and the technology?

The answer is the digital natives. Generation Z, born with the internet, is the perfect traverse between humans and technology. They are unlike other generations who, during the rise of social media, smartphones and the instant accessibility of information, either grew up without or came into adulthood. Unlike other generations, they did not need to learn or adapt to technology, they were born with it as their external organ. This also gives them the advantage of not knowing a non-digital world, allowing them to be an evolved version of the same human character.

According to WP Engine, “65% of Gen Z think artificial intelligence will have a positive impact and 75% believe the Internet will bring us closer together”.

Stepping into the professional space or workforce, these ‘Humans 2.0’ demand change from the management and the workplace technology. With newer ideas and digital innovations, Generation Z or as I call them, the integrated human beings, require an update from the organisation, the society and the space that they work in.

There are challenges in how the world functions today. Social order, management and communication are facing problems. With these challenges, how are we going to look at the future? With technology, no matter how advanced, we can generate faster transformation. For example, Tech Mogul, Elon Musk, unveiled a brain chip implant to allow people who are paralyzed to operate technology, such as smartphones or robotic limbs, with their thoughts.

Even so, the question remains, how will we move to the future in a systematic way? How will we balance evolution and innovation together? Could we become a successful world where greatness is normal?

Generation Z is not just another age. It is a new systematic innovation. Gen Z can change the direction, speed and penetration of technology. They drive the future with us based on altitude and dialogue. This becomes even more important for India which has the largest number of youth. It becomes one of the leaders in the penetration of technology.

Gen Zers have become the ambassadors of the digital age. With them as the representatives, another wave of digital shift is yet to hit the world, where everything will have an e/AI in front of them. We are on a path from e-commerce, e-banking, e-filing to e-culture, e-trust, e-innovation. But, if we want all the internet of things to be linked together for growth, how will we adapt it?

It is through technology that Gen Z will remind the world the meaning of humans and the understanding of hierarchy. For the future, every innovation, origin and purpose is digital. With e-humanity and e-culture, technology is the only means for growth.

It’s time to pass this torch of responsibility to the Gen Z. How we understand the fact and the future lies upon them. The only question is how well do they understand their responsibility.

The post Learning From Youth Culture: Generation Z And Technology appeared first on Artificial Intelligence.

The extension of the lockdown brought in social distancing, which not only impacted businesses but also shut down schools and colleges. This disruption has forced students, as well as working professionals, transition to online courses. The pandemic has also provided opportunities for data scientists to upskill themselves using online data science courses.

Responding to this, many ed-tech companies have come up with a variety of online courses for data science enthusiasts to use their content for free. These courses usually have experienced faculties and professors, along with the interactive live sessions, which can help students get a better understanding of the field.

These online courses in data science have left students with many options. The market is crowded, and there is more supply than demand for these online courses. And therefore, ed-tech companies need to build a comprehensive online data science course that can stand out in the market. Here is how businesses can create an all-inclusive online course for data science.

How To Frame The Course & What It Should Include

Framing a comprehensive data science online course depends on the depth and breadth of the course. Framing the course would help in understanding what to include in the course and whether to cover the general topic of data science or have specialised courses related to data visualisation, Python specialisation, and big data training. Each course targets different individuals, and therefore, ed-tech companies need to create courses focused on different interests in the field of data science. 

A comprehensive course needs to be appropriately structured where the learning should first start with basic programming knowledge like Python programming and SQL programming where students would be taught how to write functions, control flow, build basic applications and understand common data analysis libraries. Secondly, it should involve probability and statistics, which would include descriptive statistics, inferential statistics, and probability theory. Thirdly, it should consist of mathematics with calculus and linear algebra, followed by data wrangling where students would be taught how to access databases, clean the data and transformations using pandas and scikit-learn. The courses should also include data visualisation and exploratory data analysis, and lastly machine learning and deep learning techniques such as supervised learning, unsupervised learning, reinforcement learning, CNN, RNN, among others.

Apart from including the overall view of the subject, course developers should also focus on exploring newly emerging aspects of data science such as model explainability, storytelling as well as writing production-level code, which are critical for data scientists to drive a data-driven decision in organisations. While storytelling knowledge will help data scientists to layout business information better in front of stakeholders, explainability skill will assist in gaining the trust of clients and customers.

Alongside, as companies nowadays are relying on democratising data science, students need to learn AutoML tools like Auto-Keras, Auto-sklearn, Auto-PyTorch, to name a few, that can help data scientists to build machine learning pipelines for streamlining the workflows. Besides, these online courses for data science should also involve the understanding of ethics in data science, which will help aspirants differentiate right from wrong in terms of using data and the company’s sensitive information. This will also help newcomers of the industry understand the implication of privacy and the impact of data science on modern society.

Not only should it have an industry-ready updated curriculum but should also involve experts from the industry who can share their real-world experiences with the students. Another essential aspect that can be included is business knowledge, which can be extremely important for newcomers to survive in hierarchy-based organisations. Ed-tech companies should also create platforms, communities and forums for learners to collaborate and clear their doubts.

A general course will provide an overview of the field and attract a more significant number of learners. However, once they progress in their course/career, they will be required to diversify to more specialised knowledge. This necessitates that ed-tech companies include those specialised courses to address that learner-base.

To target specific industry requirements, ed-tech companies can also provide custom-made course packages that can help students gain more industry-based knowledge. Some of the beginners’ courses involve learning about data analysis, data visualisation, Python and R. However, some of the specialised courses cover in-depth knowledge about deep learning and machine learning. 

Many businesses nowadays are deploying end-to-end data science platforms like DataRobot, DataBricks, and other analytics platforms, to aid data scientists in integrating machine learning capabilities in their projects, and therefore, online data science courses must include these to simplify their project workflows. Furthermore, edtech companies should also create courses on the best practices for collaborating to open-source projects and competing on platforms like Kaggle and other hackathons. Learning is not all, one needs to showcase skills to increase visibility. This can be achieved if aspirants compete online and contribute to open-source projects on GitHub.

How It Should Be Showcased & Taught To Students

Many ed-tech companies have also started offering free online data science courses for students to learn their content for free and upskill during this lockdown period. Considering online courses are conducted remotely, ed-tech companies need to make their online data science courses interactive for students to get the maximum out of it. A comprehensive online data science course should include live sessions for students to interact with their trainers and mentors and discuss challenges faced by them. 

Alongside, courses can also include multiple students in live sessions for sharing insights, difficulties and perspectives with a broader audience. Mentorship and one-on-one interaction is another aspect that can be included in the course, as it benefits several newcomers who are willing to start their career in this competitive field. These courses should involve professional experts who can guide these students with required assessments to make them industry-ready. These online courses could also be video-based, combined with interactive learning and discussion boards, which will keep students motivated throughout the session.

The Quality Of The Course

To have a quality flow of learning, ed-tech companies need to design their curriculum with updated industry requirements, which can be done by incorporating experts from the industry who can guide students with their real-life experience. Ed-tech companies also need to study the market and make necessary changes to their curriculum, which can satisfy the needs of new-age employers.

Another way of assessing the quality of the course is by getting it reviewed by a selected group of data science experts and students to judge its ease of understanding and the clarity of the course. The online curriculum needs to be different from in-person classes. A quality online data science course should provide opportunities to students as well as newcomers to have real-world experience, allowing them to network with industry experts, and urge them to participate in hackathons, and attend seminars to build a personal portfolio for job interviews.

The post HOW TO BUILD AN ONLINE COURSE ON DATA SCIENCE appeared first on Artificial Intelligence.

As a kid, the term “Scientist” always fascinated me and made me wonder about the wonderful experiments that Scientists would conduct wearing those white coats. Scientists were considered to be higher-level professionals at that time in this field. Scientists were the knowledgeable ones and had a plethora of smartness. But today, the word Scientist is a designation that is used for different purposes. Data science is one of the upcoming fields and “Data Scientists” has become a very famous term in relation to this. 

Data Science is an interdisciplinary field of study comprising three major fields, Mathematics and Statistics, Computer Science knowledge and also information about the domain Operations/Marketing/Finance. Knowledge of all three bubbles helps in solving any problem in the data science domain. Computer science programming and fundamentals are required for the implementation of any solution to a problem.

The fundamental subjects of Networking, Data Structures and Algorithms, Operating Systems, Programming, Databases etc. are essential skills for any Data Scientist to solve Data Science problems. A computer engineer can very well relate to the different complexities faced in any kind of problems and be active in understanding its implications. A computer science engineer also possesses the intellect to deal with all kinds of structural problems. Good knowledge of computer science fundamentals is a must for getting to learn data science.

Coming to mathematics and statistics, concepts of both of these fields are very necessary to become data scientists. Knowledge about the basic probabilities, its advanced concepts, derivatives, integrations, linear algebra and basic calculus. These concepts form the base of the machine learning algorithms and higher concepts. Preliminary statistics and mathematics are very important for all kinds of back end operations that are performed using any programming language. 

Once you have gained knowledge on the basics, getting a clear understanding of all the elementary descriptive statistics, prescriptive statistics, distributions, hypothesis testing etc is essential for understanding the baseline of how things will work around different problems. Depending on the problem statement, the application of one of the statistical methods will have to be identified to get on to building a solution for it. Solving preliminary problems can help you understand the application of each of the mathematical concepts.

Moving on to domain knowledge about Operations/Marketing/Finance, this is something that you will always gain at your workplace depending on the tasks that you have at hand. There could be two approaches for gaining domain knowledge, the first being you take up a course which specializes in a particular domain. This will give you a lot of theoretical knowledge on the subject that you are pursuing and also gives you practical knowledge as and when you work on different dummy case studies and also encounter some real-life problems on the desk.

Secondly, you directly obtain all of this knowledge from your workplace. So as long as you have the other two bubbles in place, you can keep your third bubble of domain knowledge for the desk job. Getting to know about business knowledge is very important. The way you approach any problem statement will change altogether depending on the domain. This is one bubble which cannot be ignored as it forms the fundamentals of solving data science problems.

The three bubbles mentioned above form the core of the “Scientists” approach to data science. Having a good understanding of all the concepts is primary to getting yourself stronger on each of the bubbles. Most of the algorithms have a mathematical construct and use mathematical terminology. Hence, mathematics will help build the base for the understanding of the various machine learning algorithms. Data Science has been made easy by people who have built libraries which consist of all the machine learning algorithms implementations and they can be easily used to perform any of the mathematical operations.

All the machine learning algorithms have mathematical derivations and this bridge is well built between programming and mathematics using python libraries like numpy, pandas, matplotlib etc. Python is one of the languages which has well-established tools for data science and most importantly, they are easy to learn and use. Numpy and pandas allow you to perform mathematical operations on structured data. Numpy has numerous functions which are implemented to make mathematics in programming easier. Pandas is a library which allows you to read data into a structured manner and allows us to perform any kind of operations on it. These form the base of data prepping when the intention is to use machine learning to solve the problem.

Learning all the core concepts and understanding them is central to arriving at a solution in an organised manner. Data Science is a different kind of science and has to be approached in a scientific manner to get to its core. If the approach towards learning is right, it will seem much easier. It is a very interesting field as nothing is defined in relation to the problem. After understanding the problem, the approach used for solving it is identified.

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Artificial intelligence (AI) is one of the major technological innovations in recent times, set to revolutionise industries across verticals. In simple terms, AI is the capacity of a computer/machine to collect, anticipate, analyse information, recognise patterns and, consequently, perform actions as opposed to natural human intelligence.

AI has permeated industries throughout the years, aiding and executing taxing responsibilities such as customer service (voice assistant services), in automobiles, robotics, etc. Its presence has likewise penetrated the education sector, which can be further corroborated by the surge in edtech startups in India.

With the rise of AI, the Indian learning and e-learning landscape have seen considerable change. Here’s how the power of AI is being capitalised in the field of education:

Modernisation of education

AI aids the process of channeling focus on core concepts of all subjects, while embedding interdisciplinary concepts on the same platform. AI has allowed education to be personalised for students to fit their unique needs. It aids the process of designing and developing technology, which facilitates quick resolution of queries with independent support to students as opposed to a teacher teaching a class of 20+ students.

Meeting unique needs

AI also brings value on the table by procuring information on the unique requirements on students, courses, and their syllabi to develop a holistic and robust learning trajectory. Leading and competing players in the e-learning market have harnessed AI’s ability to recognise patterns, and congregate data by mapping online movement/footprints from numerous sources to decipher the finest techniques of learning for students in general and individuals in particular. It has filled the gaps, which a normal classroom lacks, through clever coding, algorithms, and big data, enabling students to choose subjects, concepts that suit them best in accordance with where their interests lie. In this manner, content isn’t imposed on students unlike in schools.

Ultimately, the student excels in their academic goals and paves way for a brighter future. The same ability throws statistics on how quickly or slowly an individual can digest information besides preferences, interests, and degree of involvement to further help e-learning platforms tailor their offerings for them. AI has ensured a personalised learning journey while making education more relevant to students, parents, and teachers.

Feedback that supports growth

AI lends support by monitoring and mapping the learning graph of a student or an individual. Once the data has been acquired on the individual, it’s scrutinised using advanced analytics for insight generation (strengths and weaknesses) to develop tailored plans for an individual to ensure that the person gets the most out of his/her course online.

Learning can be fun

Artificial intelligence can turn learning into an enjoyable experience. One of the newest ways teachers can engage students online or in a classroom is by using simulation. Leading gaming companies have been using simulation to engage their customers for decades. Many medical and flying institutes have started using computer-powered simulations to put candidates/students in a real-life situation wherein students can react and interact, based on which one can be judged.

This ability can be further used to enable partnerships on a global scale, similar to an electronic student exchange program, where students or individuals could come together and provide answers to solutions or create a collective craft.

Beyond learning

We have adopted AI in all forms, from chatbots to adtech in our lives, making our life simple and easier. We have embraced a greater scientific approach, which enhances strategy, targeting, insight, creativity, knowledge, and experience. Basic and redundant processes are made easy, like the creation of profiles, which require consolidating information of students, which also, at times requires constant updating. Another understated potential is regular updates to the online alumni community. To that end, a feed framework can be created on a subscription basis, to update students with developments in the field and enable them to take calculated steps to enhance their career curve.

Segmentation framework

Another facet of how AI is used in the edtech industry is in the segmentation framework model. Under this, AI aids the study of different data sources that are available to understand their structures for profiling. The data, which is profiled, addresses different objectives, for instance:

• Assessment: Whenever an individual poses a question, with the help of advanced analytics, AI can determine the level of understanding of the student/individual. It helps in assessing whether the analytical intelligence of the student/individual is on par with the level of the concept and whether they have a proper understanding of the subject.

• Cross-selling of content: AI identifies the learning patterns of a student or an individual basis, which it provides them with supplementary learning content to ensure that the individual grasps the concept in its entirety.

• Dropout: When a student or an individual chooses to opt out of a course or arbitrarily drops out, AI is able to determine the reason by determining how interested the individual was in the subject.

With the baseline data and touch points, educators can then proceed to develop predictive model layers over the data to draw and retrieve inferences, which will form the foundation of all future engagements.

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