Machine Learning Basics For Newbies

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Retail.

Machine learning is used to identify products that sell fast and those that do not. It helps retailers decide on the kind of products to introduce or remove from their stock. Also, machine learning algorithms can be very effective in finding two or more products that will sell together. This is basically done to encourage customer loyalty initiatives which, in turn, help different retailers develop and maintain loyal customers. Walmart, Amazon, Big Bazaar and other such retail chains extensively make use of machine learning.

Publishing and social media.

There are publishing firms like LexisNexis and Tata McGraw Hill that make use of machine learning to run queries and fetch documents required by their users online, based on their preferences and requirements. Google and Facebook also use these techniques to rank their search outputs and news feeds. Facebook provides a list of possible friends under Friend Suggestions using this.

Robot locomotion.

Robot locomotion is a collective term used for the different methods that robots use to transport themselves from one place to other. A major challenge in this field lies in developing capabilities for different robots to autonomously decide how, when and where to move. Machine learning helps them do this quite easily.

Apart from this, there are various decisions that robots need to take instantaneously while they perform activities, which is possible using different machine learning techniques.

Game playing.

A strategy game is one in which the player’s autonomous decision-making skills are significant in determining the final outcome. Almost all strategy games require internal decision tree style of thinking, and very high situational awareness. Machine learning meets all these requirements and, hence, is widely used in gaming.

Advantages of machine learning

Machine learning techniques help the system take decisions on the basis of training data in dynamic or uncertain situations.

It can handle multi-dimensional, multi-variety data, and can extract implicit relationships within large data sets in a dynamic, complex and chaotic environment.

It allows reduction of the time cycle and improves resource utilisation. It also provides different tools for continuous quality improvement in any large or complex process.

Another advantage of machine learning techniques is the increased usability of various applications of algorithms due to source programs like Rapidminer. Machine learning allows easy application and comfortable adjustment of parameters to improve classification performance.

Challenges of machine learning

A very common challenge is acquisition of relevant data. Once available data is secured, it often has to be pre-processed depending on the requirements of the specific algorithm used, which has a critical impact on the final results.

Sometimes, interpretation of results also becomes a challenge, as these need to be interpreted according to the algorithm chosen. Different machine learning techniques can be implemented in order to let the system decide on what action it needs to take and when it needs to be taken.

Machine learning can give an edge to automation, and has already helped in making the world a lot smarter. But machines have not stopped learning, and the next level of this technology is being worked on.

Reproduced from EFY’s Open Source For You magazine

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Vivek Ratan currently works as an automation test engineer at Infosys, Pune

 

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