Data generation sources like social media as 1st and winners are doing an excellent job. 2nd to this is payment data which is as big as social media or the Western world. Payments on mobile for e-commerce, online food orders, etc are almost 30 – 50 times more than in the U.S. as in Africa and Asia combined above. Off-course all this data is quality data for making more money as well as to improve the user experience. Data is also used as a yardstick for comparing algorithms.
Machine learning is the process of a machine attempting to accomplish a task, independent of human intervention, more efficiently and more effectively with every passing attempt i.e learning phase. At this point, AI- a machine which mimics the human mind, is still a pipe dream. In the middle we have the meat of the pipeline, the model, which is the machine learning algorithm that learns to predict given input data.
While there may be variances in methodology between machine learning and statistics, these distinctions do not sever the alliance between the two fields. The contrast lies in the fact that machine learning prioritizes enhancing efficiency and achieving optimal results, while the others focus on sample size, population, and developing hypotheses. The primary focus of machine learning is on producing accurate forecasts, even if the explanations behind those predictions are not easily comprehensible.