It’s a common trend that even though a student studied the subject math in the classroom it is often difficult for him to grasp the things taught with accuracy. The same concept was realized by Salai Arjun, the founder of First Bench who claims that there should always be a balance maintained between the things being taught and the level of understanding of the things learned.
This particular urge to actively encourage a balance between math learning and understanding introduced First Bench into the market wherein individual assessment of students’ abilities are done and accordingly the future path is laid out for respective students. From the conceptualization of First Bench, their key goal is to develop an environment of interactive learning with the culmination of in-depth learning. First Bench has been operating since the past five years and has constantly been engaged in comprehending the study patterns, important behavioral feedbacks, and various other data which ultimately led to the development of the application of Machine Learning Technology and the implementation of Machine Learning Tools in their learning practices.
So, how do they actually function? With the help of Artificial Intelligence, First Bench makes use of Machine Learning Technology to operate in situations where human capability becomes limited. With a huge classroom size, it usually becomes tough for a teacher to offer attention to individual students. In such a situation, the Machine Learning Technology finds its valuable application. The Machine Learning Tools used by First Bench are highly engaged in assessing each student and providing tailor-made knowledge.
In the initial stage, the Machine Learning Tools assess individual students effectively before the commencement of any lesson. With the help of this Machine Learning Technology, this enterprise is successful in understanding the capability of each student to effectively comprehend the lessons to be taught. This type of Machine Learning Analysis takes into account the student’s knowledge about the basics and fundamentals of the topics to be learned.
Through Machine Learning Course and the proper implementation of Machine Learning Tools, the respective student’s answers are recorded. With the aid of this information, the Machine Learning Technology will conclude upon the learning course for a specific student and come up with suitable lessons. Thus, with this successful Machine Learning Analysis, a set of the learning system is devised for each student which is exclusive to individual people and is adaptive to the learning structure of the student.
In turn, what will be the results of such a Machine Learning Analysis which makes use of Machine Learning Technology? These Machine Learning tools will consistently assess the student’s performance and guarantee that each individual is able to efficiently learn the subjects and thereby, proceed to the next lessons. With this Machine Learning Analysis, it is obvious that along with the transition from one topic to another the student effectively develops the knowledge about each topic from a very basic level to a more challenging level. A result of this adaptive and efficient Machine Learning Analysis is that every student has the chance to proceed in their learning procedure by taking into account their abilities and inherent intelligence quotient.
The best thing about this is that not a single student is overlooked in this learning process. This method adopted by First Bench is an example of how AI and Machine Learning Technology are capable of adhering to individual students and their learning disabilities, thus creating a powerful learning system. With the success witnessed from the incorporation of Machine Learning Analysis a large number of schools are becoming increasingly interested in participating in such a program. Machine Learning sure has the potential to transform the complete education system in the upcoming generations.
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How is Machine Learning Impacting The Education Industry?
Machines today are being used more than ever due to the simplicity of their making and their ability to learn and create value to organizations. The story is not different in the education space either. Artificial intelligence and machine learning are being used to create modules for students, which are highly personalized and intuitive. One of the biggest benefits of using machine learning is because of the computers ability to process large volumes of data both historic and real-time and analyze it for predictive outcomes. Artificial Intelligence is already being used to grade papers (multiple choice questions) fairly and effectively in many schools across the world. It is also impacting the lives of specially-abled students by providing tools and equipment to study and succeed.
The education industry is moving beyond classroom and textbook learning to create more immersive programs for their students. Digital libraries are growing at a rapid pace due to emerging technologies such as big data, cloud computing, and AI. Another great example of machine learning usage is to categorize content in a manner where the student finds it easy to build on existing knowledge. This means that it gives a student the opportunity to learn at his/her own pace and succeed while doing so, thereby greatly boosting the morale of the child.
Here are a few ways machine learning has become a game changer in the education space.
Supporting Teachers
Machine learning helps teachers program a curriculum which is highly individualized for their students. Kids today are smart and fast learners due to their increased exposure to technology. Hence the subjects also need to be contemporary and relevant. All their students’ data in terms of marks, activities can be historically analyzed to create personalized lessons, thereby matching the child’s ability to learn and succeed.
Custom-Made Subjects
One of the biggest advantages of machine learning training is a personalized learning experience for individuals. Every child learns at a different rate and is proficient in different subjects. Once data is gathered on the child’s different abilities, a machine can analyze and build a program specific to the child’s needs thereby controlling the outcome as well as the rate of learning. Another aspect of this is it will help grade students fairly and as per their ability without any human bias.
Increase Retention
Through Machine Learning teachers can identify students who are likely to forget and help them with specialized chapters and techniques to retain the subject. Learning analytics tools such as Wooclap, Yet Analytics, BrightBytes provides precise predictive solutions through different learning ecosystems. This helps educators adapt and improve their content significantly mapping it according to the students’ needs.
Conclusion
Since we are discovering more ways machines can be used effectively in classrooms, one can predict that the growth trajectory and successful integration of machines are highly possible soon.
Machine Learning Tech Can Enhance Wildfire Modelling
Firefighting is expensive and machine learning tools are helping in analyses of forest fires to predict and prevent future disasters, here is everything you need to know on Machine Learning.
Every year destructive wildfire destroys many forests across the globe. With climate change and global warming, there is a growing concern amongst scientists and world leaders regarding how to combat natural calamities. In the U.S. alone millions of dollars are poured into disaster management and rehabilitation.
There is significant research being conducted in the space of wildfire disaster management and one of the biggest investments in technology is towards artificial intelligence and machine learning. Risk modelers such as Egecat, RMS, AIR is not developing fully fledged versions of the probable places which have a high vulnerability to wildfire and what factors influence the activity. Several factors such as climate change, weather conditions, and region create a conducive environment for a forest fire to break out.
These can be assessed by artificial intelligence tools. Machines are inherently well-versed when it comes to picking up information quickly and this is known as machine learning. It can analyze a richer dataset than traditional forecasting systems, thereby helping researchers make informed decisions quickly. Once a high-risk scenario is detected, drones can be commissioned to ensuing fires. This leads to effective utilization of resources such as firefighters, water and medication thereby helping the government protect their citizens.
Due to this rapid growth in ability, machine learning can help in urban planning and revolutionize disaster management and resource planning.
Here are the top ways a machine learning course is helping governments and organizations combat wildfire.
Aiding Rescue
One of the most important things when it comes to any natural disaster is rescue and rehabilitation. Time is of the essence during this crucial time. Finding survivors by using artificial intelligence tools which skim through social media data is a key development. Another component in machine learning is the ability to process historical data and deliver better disaster response management abilities i.e. using the limited resources in the best way possible.
Predictability of Wildfires
Machines can analyze vast amounts of historical and real-time data to get an understanding of the likely places where wildfires will hit. There are also able to determine the factors that influence the magnitude of the fire. These possible predictions can help researchers prepare ahead of time and help mitigate the damage.
Insurance Risk Assessment
There is a massive potential for machine learning to grow in the insurance industry when it comes to assessment and allocation. Real-time data processed by machines can be used in complement with prediction tools to help understand the risks and allocate resources better, thereby cutting down on the losses. Insurers can align their interest in disaster resilience, safety and urban development in partnership with the government due to machine learning.
Conclusion
When forest fires are detected early using machine learning, it can help firefighters deal with blazes, help in recovery and prevention.
The Ultimate Glossary of Terms About Machine Learning
Machine learning is an artificial intelligence application which provides computer systems with the ability to learn on its own and improve with experience without any explicit requirement of additional programming. Machine learning has its focus on developing computer programs whole can access data and utilize the data to learn on its own.
Some of the commonly used terminology used in Machine Learning are as follows:
- Adam Optimisation
It is an algorithm utilized to train models of deep learning and is an extension of the Stochastic Gradient Descent. In this algorithm, the average is run employing both gradients and using the gradient’s second moments. It is useful for computing the rate of adaptive learning for every parameter.
- Bootstrapping
It is a form of the sequential process wherein each subsequent model tries to correct the errors in the earlier models. Each model is dependent on its previous model.
- Clustering
It is a form of unsupervised learning utilized for discovering inherent groupings within a set of data. For instance, a grouping of consumers on the basis of their buying behavior which can be further used to segment the customers. It provides useful data which the companies can exploit to generate more revenues and profits.
- Dashboard
It is an informative tool which aids in the visual tracking, analysis of data by displaying key indicators, metrics and data points on a single screen in an organized manner. Dashboards are often customizable and can be altered based upon the preference of the user or according to the requirement so of a project.
- Deep Learning
It is a form of a Machine Learning algorithm which utilized the concepts of the human brain towards facilitation of the modeling of arbitrary functions. It requires a large volume of data, and the flexibility of this algorithm enables multiple outputs of different models at the same time.
- Early Stopping
It is a technique of avoiding overfitting while training an ML model using iterative methods. Early stoppings are set in such a manner that it halts the performance of improvement on validation sets.
- Goodness of Fit
It is a model which explains a proper fitment with a set of observations. Its measurements can be summarised into the discrepancies between its observed values with that of the expected values using a certain model.
This Machine Learning Course is a good fir when the errors on the models which are on training data along with the minimum test data. With time, this algorithm learns the errors in a model and corrects the same.
- Iteration
It is the number of times the parameters of an algorithm is updated during training of a dataset on a model.
- Market Basket Analysis
It is a popular technique utilized by marketers for identification of the best combination of services and products which are frequently purchased by consumers. It is also known as product association analysis.
- MIS
Also known as Management Information System, it is a computerized system comprising of software and hardware which serve as the heart of a corporation’s operations. It compiles data from various online and integrated systems, conducts an analysis on the gathered data, and generates reports which enable the management to make informed and educated business decisions.
- One Shot Learning
This form of machine learning trains the model which a single example. These are generally utilized for product classification.
- Pattern Recognition
It is a form of machine learning which focuses on recognizing regularities and patterns in data. Some examples of pattern recognition used in many daily applications include face detection, optical character recognition, object detection, facial recognition, classification of objects etc.
- Range
It is the difference between the lowest and the highest value in a data set.
Which Skills are Required for Machine Learning Jobs?
Machine language is generally related artificial intelligence, which provides the machine or computers with the ability to complete certain tasks like diagnosis, planning, prediction, recognition or robot control. It consists of different algorithms, which you can use to teach the machines to change and grow when exposed to new data.
The process of implementing machine language is somewhat similar to data mining because the process looks through data and searches for the same pattern. Now that you have an idea of what machine learning is, let’s have a look at the skills that are required to get a machine learning job.
Also Read: Future of Machine Learning in India
Computer Science & Programming Skills
Some of the fundamentals of computer science are essential when you are looking to learn machine learning. Concepts like data structure, algorithms, complexity and computability, along with computer architecture are essential for artificial intelligence. In fact, you should also have knowledge of programming languages like C, C++, Java, Python and R, among others. A little bit knowledge of assembly language doesn’t hurt either.
Probability & Statistics
Conditional probability, and its characteristics and the techniques derived from it plays a key role in the machine learning algorithms. Moreover, you should also know about the different terms of statistics like mean, median and mode along with variance and standard deviation. These are all necessary to not only observe the pattern but also validate the data that is received through different means. Some machine learning algorithms are in essence an extension of the common statistical operation procedures.
Applied Mathematics & Algorithms
You need to know not only how to solve a problem but also how to implement it in short executable steps when it comes to machine learning. Algorithms help you to understand how to break down a problem into executable steps, and that is why this is important. In addition, you also need to know about gradient, convex optimization and its application in daily life, so that you can implement it in machine learning.
Operating Systems
When it comes to machine learning, most of the coding is done in Linux or some version of it. So, you need to be versatile with Unix or a version of Linux, which is in use presently. You also need to know about the Linux tools, which will make your life easier in the long run. Some examples include grep, find, sort and tr.
Software Engineering & Designing of Systems
When you are designing a machine learning tool, you are also designing an advanced software. So, at the end of the day, you need to know how to design system, and how you can implement your ideas in that. You also need to understand how different algorithms interact with your system, and how you can speed up the process without compromising on the resource space.
Now that you know about the skills required for machine learning jobs, it is time to get started on acquiring these skills. In case you have some of these skills, make sure you hone them so that you can implement it and build a great system when the time comes. Good luck!
Related Article: What is The Easiest Way To Learn Machine Learning?
Want To Learn Artificial Intelligence And Machine Learning? Where Can You Start?
These are exciting times to be a part of the technology industry, what with newer and fascinating fields being discovered every day. With the tech companies leading the way, the newer fields like Artificial Intelligence and Machine Learning are finding takers in multiple domains. Probably, this is the reason the demand for machine learning courses is at an all-time high.
Every second person, in the technology domain, you interact with would wax eloquent about how they are going to learn machine learning or how they have been taking the latest and most difficult artificial intelligence courses. Hearing this can be quite an unnerving experience, especially if you are a newbie in the field and looking to find out some machine learning courses that can help you unravel this mysterious world.
But, fear no more, as in this article, we try to help you find your initial foothold in this domain and slowly but surely come up to speed in your quest to learn machine learning. The first thing that confuses newcomers and throws them off-track when they begin to learn machine learning is the different terms and their interrelationships. What do the terms – Artificial Intelligence, Deep Learning, Neural Network Programming, Machine Learning – mean and how are they related.
If you somehow can navigate through the maze of big data and machine learning courses and get a hang of these terms, another big question comes up. Do I need to learn programming, statistics as well as calculus? Is this the right direction for my career, even though I do not understand either of this? Is there a way to learn machine learning without being proficient in all these?
There are no straight forward answers to these questions. But below are a few pointers that may help you to take your first step as well as identifying the correct artificial intelligence courses for yourself.
First and foremost, we need to understand the interrelationship between artificial intelligence and machine learning. Artificial intelligence is mostly trying to mimic human intelligence and behaviour by the machines including creativity, learning and reactions to any situation. As you start to learn machine learning with the help of machine learning courses, you realize that machine learning is nothing but a subset of artificial intelligence, dealing with pattern recognition and self-learning.
Now, the next fundamental question – do you need to learn programming language or statistics to complete artificial intelligence courses? You definitely need to have some basics in both as the statistics help you understand what you are doing, and the programming language shows how it is done. In case either of these is unknown to you, it is recommended you start your quest to learn machine learning with the courses for these.
You can find some excellent courses to learn programming languages like R or Python, models and algorithms basics through – imarticus.org Or also can contact on – info@imarticus.com or 1-800-267-7679 Or else you can visit our different training locations in India – Mumbai, Pune, Thane, New Delhi, Banglore, Chennai, Gurgaon, Hyderabad and Ahmedabad.
Keep Learning…!!