Artificial Intelligence is the future for the current technology era and to make your application future more bright and
feel the gapes. We are here to solve using Machine Learning & Deep Learning algorithm techniques.
The traditional problems or goals of AI to research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. We are doing and focusing to use AI in education to get better result for teaching. Currently, We are doing focus in education fields and so far we are working to make education more predictive and get good result for both teaching and learning methods with the help of deep learning algorithm techniques.
Cognitive Services are a set of machine learning algorithms that Microsoft has developed to solve problems in the field of Artificial Intelligence (AI).
The goal of Cognitive Services is to democratize AI by packaging it into discrete components that are easy for developers to use in their own apps. Web and Universal Windows Platform developers can consume these algorithms through standard REST calls over the Internet to the Cognitive Services APIs.
The Cognitive Services APIs are grouped into five categories Vision, Speech, Language, Knowledge, and Search.
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The vision for AI in Education is one where they work together for the best outcome for students & teachers.
AI robot for self-service at banks and making finance prediction and calculation better than an expert do.
For faster turnarounds, industry requires the improvements that Artificial Intelligence can provide.
To improve the overall shopping experience and increase conversion with predictive modeling and micro-targeting.
Complex analytical tasks faster than human imagination are done on Big Data with the help of ML and AI.
AI will help in order to perform a variety of administrative and customer service tasks.
Machine learning is the scientific study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of training data(sample data) in order to make more predictions or better decisions without being explicitly programmed & human interference to perform the task.
Machine learning algorithm techniques are used in the applications of filtering records of the search result, detection of network intruders, and computer vision. Machine learning focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning.
In its application across business problems, machine learning is also referred to as predictive analytics and yes we are here to make it possible and give you a very good result.
Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called Artificial Neural Networks.
Deep learning teaches computers to do what comes naturally to humans. It is a key technology behind driverless cars, enabling them to recognize a stop sign or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.
In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state of the art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.
Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about the future.
And the future is all about data. let’s take an live example of Amazon. It uses predictive analytics to study the behaviors of over 200 million customers who produce over 1 billion GB of website data per year, which results in tailored product suggestions that earn the company over $2 billion in sales a year. But your company doesn’t have to be a retail giant to use predictive analytics. You just need data, well-defined goals, and the willingness to iterate.
Natural Language Processing
Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language. Natural language refers to the way we, humans, communicate with each other.
NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding.
While natural language processing isn’t new science, technology is rapidly advancing in human to machine communications, availability of big data, powerful computing, and enhanced algorithms.
You may speak and write in English, Spanish or Chinese. But a computer’s native language known as machine code or machine language is largely incomprehensible to most people. At your device’s lowest levels, communication occurs not with words but through millions of zeros and ones that produce logical actions.