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 to achieve 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 efficiency and 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 its abilities have recently started accelerating at a rate that has 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 is 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 is worth saying that if it is handled right, AI is  going to bring huge benefits to humanity as a whole. A world of machines that can work tirelessly and innovate and improve themselves is a world in which advances in economic efficiency will 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 a 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 enable the recognition and translation of spoken language into text by computers. It incorporates knowledge and research from 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 benefitting from machine learning products. 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 inventions like smart watches that monitor 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 go online to get what they need, and they depend on human-unsupervised service to make smart decisions. 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 a more personalized way of shopping.

Government
Government

Automated services and systems that perform public service make it smoother for the public to get through 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 for the financial industry to implement machine learning into its operations. Considering how sensitive the transactions in this business can be, having an extra measure of security is more than a good idea. This kind of technology can help identify opportunities specific to the finance industry, such as preventing fraud with cybersurveillance. The insights that it offers help investors pick the right time to trade. It can also produce data that’s useful for identifying and selecting the right clients.