Imagine, the time has come, when machines can learn like humans. All nightmares we have seen in T2 are becoming reality. So what is ML?
Machine learning is a method of data analysis that allows a business model automatically make adjustments based on regularly used patterns.
Machine learning s computers to explore example inputs and build algorithms that can make data-driven predictions or decisions. Processing and visualization of the Big Data have provided a good platform to Machine learning algorithms to achieve better results for the organization using different techniques such as clustering, classification and product recommendations. Thanks to machine learning businesses are now able to analyze bigger, more complex data and deliver faster, more accurate results. This can lead to identifying incredibly profitable opportunities or avoiding unknown risks much sooner in comparison to other analytics technologies. Machine learning automates tasks that would otherwise need to be performed by a person. In other words, machine learning allows algorithms learn through experience and program things.
To make it more clear, here are some examples of machine learning:
- Amazon product recommendation,
- Facebook’s ability to spot friends faces,
- Netflix’s movie recommendations,
- Dating applications matching a person with potential dates.
Who can use machine learning?
- Financial services
To identify important insights in data such as investment opportunities, and prevent fraud.
Government authorities have multiple sources of data that must be processed. That helps to increase efficiency and save money.
- Health care
Modern machine learning is very popular in health care industry, because of the ability to assess a patient’s health in real time. Also using computing powers to analyze statistics of drugs performance is giving amazingly accurate medical information.
- Marketing and sales
E-commerce organizations use machine learning to analyze client’s buying history to recommend and promote products based on previous transactions. Real time buyer behaviour is a must for every modern e-commerce.
- Mining agencies
They use machine learning for finding new energy sources, predicting failure, analyzing minerals in the ground.
Public transportation and other transportation organizations use machine learning to identify patterns and trends. That helps to make routes more efficient and more profitable.
How does machine learning work?
Algorithms are trained to use labeled examples, such as an input, where the desired output is known. For example, a learning algorithm receives a set of inputs along with the corresponding correct output, then the algorithm compares its actual output with the correct outputs to find errors. And then, it modifies the model accordingly. This type of algorithm uses patterns to predict values of the label on additional unlabeled data. This kind of machine learning is used where historical data predict likely future events.
There is another type of algorithms, where an algorithm does not use historical labels and the system doesn’t give the correct answer. The algorithm must figure out what is being shown. These algorithms explore the data and find some structure within. They work perfectly on transactional data. For instance, they can find similar customers and offer them similar marketing campaigns. Or they can distinguish particular customer segments from one another. It is common for nearest-neighbor mapping and singular value decomposition.
There is one more type of algorithm, which uses both labeled and unlabeled data. It usually takes a small amount of labeled data and unlabeled data (cheaper than labeled data and require less effort to get). An example of this is identifying faces on webcams.
There is also reinforcement learning, which is used in navigation, games and robotics.In this case, an algorithm discovers through trials and errors what brings the best rewards.
To sum it up, a person is able to make one or two models a week, while machine learning can make thousands per minute. To get the most out of machine learning, you should know how to pair algorithms with the appropriate tools and processes.