Machine Learning

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Machine Learning

Most industries working with large amounts of data have recognized the value of machine learning technology. By gleaning insights from this data – often in real time – organizations are able to work more efficiently and gain an advantage over competitors.

As a company, our experiences include building various ranking, classification, and neural nets algorithms to make intelligent predictions and maximize performance. Our engineers have successfully built an analytics dashboard for a recruitment platform to make intelligent recommendations to job seekers and employers.

What is Machine Learning?

Machine Learning is an idea to learn from examples and experience, without being explicitly programmed. Instead of writing code, you feed data to the generic algorithm, and it builds logic based on the data given.

Supervised Learning
Supervised Learning

In supervised learning, the system tries to learn from the previous examples that are given. Classification and regression are examples of supervised learning.

Unsupervised Learning
Unsupervised Learning

In unsupervised learning, The algorithm is left to itself to discover interesting structures in the data. Association and clustering are some of the examples of unsupervised learning.

Reinforcement Learning
Reinforcement Learning

A computer program interacts with a dynamic environment in which it must perform a particular goal (such as playing a game with an opponent or driving a car). The program is provided feedback in terms of rewards and punishments as it navigates its problem space.

Why ML?

Machine learning models can be used to improve efficiencies, identify risks or new opportunities and have applications across many different sectors. They either predict an exact value (e.g. next week’s sales) or predict a grouping, for example in a risk portfolio, whether the customer is high risk, medium risk or low risk. Machine learning brings together computer science and statistics to harness that predictive power.

Machine learning — and AI in general — has been around for a while. But it’s recently started accelerating at a rate that’s surprised a lot of people. As recently as 2014, most experts thought it would be 10 years before a machine beat the world’s best players at Go. DeepMind proved them wrong. It’s becoming increasingly apparent that many tasks we once thought would be the domain of humans alone for the foreseeable future — if not forever — will be accomplished by machine learning systems much sooner than expected.

It’s worth saying that AI is — if handled right — going to bring huge benefits to humanity as a whole. A world of machines that can work tirelessly, innovate and improve themselves, is a world in which advances in economic efficiency make everything that came before look like the Dark Ages.

ML

Machine Learning at Fusemachines

Intelligent Character Recognition(ICR)
Intelligent Character Recognition(ICR)

ICR is handwriting recognition system that allows fonts and different styles of handwriting to be learned by a computer during processing to improve accuracy and recognition levels.ICR is an extended technology of OCR(Optical Character Recognition).

Automatic Speech Recognition(ACR)
Automatic Speech Recognition(ASR)

ASR is the inter-disciplinary sub-field of computational linguistics that develops methodologies and technologies that enables the recognition and translation of spoken language into text by computers.It incorporates knowledge and research in the linguistics, computer science, and electrical engineering fields.

Implementation of Machine Learning

Most industries working with large amounts of data have recognized the value of machine learning technology. By gleaning insights from this data – often in real time – organizations are able to work more efficiently or gain an advantage over competitors.

Industries that yield profit from processing large amounts of data are obviously benefitting from products of machine learning. By sorting through the data these industries are able to identify opportunities that are profitable for their revenue. This trend is spreading like wildfire so all industries will soon depend on implementing ML into their production.

Healthcare
Healthcare

Machine learning has brought forth cool inventions like smart watch that monitors your pulse rate, blood pressure etc. The technology is also helpful for medical practitioners for better and accurate diagnosis of a patient’s conditions.

Marketing and Sales
Marketing and Sales

Consumers are all gathered online to get what they need, they depend on human unsupervised service to make smart decision. Machine learning has presented so many ways to analyze and predict client behaviour for marketers. With the consumer’s past data marketers can predict the sort of purchase they will make. They are also able to give the customers more personalized way of shopping.

Government
Government

Automated services and systems that perform public services make it smoother for the public to get through the tasks in the government agencies. Public safety and other government concerned utilities are obligated to function due to machine learning’ gives better meaning. With machine learning criminal activities like online fraud and identity theft can be averted.

Financial services
Financial Services

It only makes sense that the financial industry to implement machine learning into their operations. Being how sensitive the transactions in this business can be, having an extra measure of security is more than a good idea. Kind of technology that helps in identifying opportunities and specific to finance industry, preventing fraud with cybersurveillance. The insights that it offers helps the investors pick the right time to trade. It can also produce data that’s useful to identify and select the right clients.