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WHAT IS MACHINE LEARNING?
Machine learning is a method of analysing datawhich automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. It uses the history of data usages to enhance using same data for the same functions the next succeeding times.
Pattern recognition and the theory that computers can learn without being programmed to perform specific tasks, by keeping histories where the precursors for machine learning. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results.
WHY IS MACHINE LEARNING IMPORTANT?
Rising interests in machine learning is mainly due to things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage, which are essential for machine learning.
All of these things mean it's possible to quickly and automatically produce models that can analyse bigger, more complex data and deliver faster, more accurate results, even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities or avoiding unknown risks.
WHO'S USING IT?
Most industries working with large amounts of data have realised the importance and efficiency 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. Some of such organisations, where machine learning has been found to be helpful after various researches include
• Financial services
• Government
• Health care
• Marketing and sales
• Oil and gas
• Transportation
WHAT'S REQUIRED TO CREATE GOOD MACHINE LEARNING SYSTEMS?
• Data preparation capabilities.
• Algorithms – basic and advanced.
• Automation and iterative processes.
• Scalability.
• Ensemble modelling.
WHAT ARE SOME POPULAR MACHINE LEARNING METHODS?
Supervised learning algorithms are trained using labelled examples, such as an input where the desired output is known. For example, a piece of equipment could have data points labelled either “F” (failed) or “R” (runs). The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors. It then modifies the model accordingly. Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabelled data. Supervised learning is commonly used in applications where historical data predicts likely future events. For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim.
Unsupervised learning is used against data that has no historical labels. The system is not told the "right answer." The algorithm must figure out what is being shown. The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns. Or it can find the main attributes that separate customer segments from each other. Popular techniques include self-organizing maps, nearest-neighbour mapping, k-means clustering and singular value decomposition. These algorithms are also used to segment text topics, recommend items and identify data outliers.
Semi supervised learning is used for the same applications as supervised learning. But it uses both labelled and unlabelled data for training – typically a small amount of labelled data with a large amount of unlabelled data (because unlabelled data is less expensive and takes less effort to acquire). This type of learning can be used with methods such as classification, regression and prediction. Semisupervised learning is useful when the cost associated with labelling is too high to allow for a fully labelled training process. Early examples of this include identifying a person's face on a web cam.
Reinforcement learning is often used for robotics, gaming and navigation. With reinforcement learning, the algorithm discovers through trial and error which actions yield the greatest rewards. This type of learning has three primary components: the agent (the learner or decision maker), the environment (everything the agent interacts with) and actions (what the agent can do). The objective is for the agent to choose actions that maximize the expected reward over a given amount of time. The agent will reach the goal much faster by following a good policy. So the goal in reinforcement learning is to learn the best policy.
WHY USE GPUS FOR MACHINE LEARNING?
GPUs might be a great way to handle the heavy computing necessary to make many forms of artificial intelligence a reality.
Although machine learning has been around for decades, two relatively recent trends have sparked widespread use of machine learning: the availability of massive amounts of training data, and powerful and efficient parallel computing provided by GPU computing. GPUs are used to train these deep neural networks using far larger training sets, in an order of magnitude less time, using far less data centre infrastructure.