FEBRUARY 20209is based on machine learning. The application can tell that you would probably like a movie based on the movies you had watched previously. Note that probability is a percent-age and not a binary value.Prediction of the FutureThe design of the machine learning program to create rules for pre-diction has numerous applications. Some of them are listed below and definitely not limited to these.1. Based on the shopping pat-terns of a customer on a retail web-site, newer product recommenda-tions which have a high likelihood of being purchased are shown to the customer. The rules for the "likelihood of being purchased" are determined by a machine learning program. These rules adapt to cus-tomers changing needs. An exam-ple is that the rules that apply when the customer is single aredifferent from when the customer is in a re-lationship which aredifferent when the customer is a parent. Machine learning learns the new rules based on changes in behaviors.2. Techniques in machine learn-ing, like deep learning, enable the ability to relate unrelated factors that affect the outcome. For exam-ple, a deep learning algorithm can recommend a shoe for a customer based on a rich feature set which includes, the type of material, col-or, the narrowness of the tip, the height of the heels, the grip of the sole, patterns on the front and many more factors.3. Course recommendation sys-tems can help students determine the courses in which they have a better probability of being success-ful. Factors considered in this al-gorithm are time if day, professor, work schedule, and more. 4. Recruiters can use machine learning programs that sort re-sumes based on the likelihood of a candidate to succeed based on the job requirements for the role and the candidates past experience. 5. Automobile companies can use machine learning programs that can predict the wearing of spare parts based on the past data of spare part replacement. Analyzing under what circumstances the spare parts were replaced in the past would help in identifying andreplacingthe spare parts before they are dam-aged thereby reducing breakdowns.In all of the above examples, the outcome of the programs is a pre-diction. Machine learning programs can be seen as a predictive analytics system that can continuously adapt itself to the changing nature and be-havior of the inputs and the outputs. Traditional analytics systems derive information from the data available, while a machine learning-powered predictive analytics system helps in predicting an outcome based on the data available. The traditional ana-lytics systems provide information for deciding the course of action, while a machine learning-powered predictive analytics system recom-mends the course of action. Thus, machine learning programs con-nect the present to the future.Word of CautionBefore going all out for machine learning there is certain caution that needs to be exercised.1. The prediction of the outcome is only as good as the data that is available. Even a great algorithm when operating on poor data can produce poor predictions.2. 100 per cent accurate ma-chine learning programs suffer from a phenomenon of "over-fitting" wherein it works only with the data used for training.3. There is a risk of bias in ma-chine learning programs. It has the vulnerability of taking in biased data and produce a biased outcome. Hackers can exploit the nature of the prediction systems to change the outcomes. Hence the data and rules need to be kept transparent to gain more trust in the machine learning program.Before concluding it is neces-sary to touch upon the dreaded outcome of machine learning. Can machines replace humans? The answer is "No, not yet". Technolo-gy is far from what has been por-trayed in movies where machines take over human roles. However, machine learning programs cer-tainly increase the humans' depen-dency on machines more than ever and when used wisely can help in a constructive manner. The only thing you must remember before spreading your wings or going for a broader spectrum with multiple specializations in content is, never should your plans affect the value of your current product
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