Machine Learning or “Automatic learning” is an area that has been with us for a few years. This field of Artificial Intelligence’s objective is that the algorithms, the coding rules of our objectives for solving a problem, learn by themselves. Hence the “machine learning.” That is that the algorithms themselves generalize knowledge and induce it from the behaviors they are observing.

For their learning to be good, accurate, and practical, they need data. The more, the merrier. Hence, when Big Data (this new paradigm of large amounts of data) bursts in, Machine Learning began to rub its hands regarding the future that awaited it. The patterns, trends, and interrelationships between the variables that the Machine Learning algorithm observes can now be obtained with greater precision thanks to the availability of data.

And what do these Machine Learning algorithms allow to do? Many things. This cheat sheet helps us, through a workflow, to select the best method of solving the problem that we have: classify, relate variables, group our records by behavior, reduce dimensionality, etc. You see, as we mentioned in the previous post, that statistics are ubiquitous.

These techniques according to data science colleges in Hyderabad have been with us for several decades now. They have always been instrumental in obtaining knowledge, help make decisions in the business world, etc. Its use has always been more focused on industries with large data availabilities. For example, the BFSI (Banking, Financial Services, and Insurance) sector has always considered data a critical asset. And it has always been a sector where Machine Learning has had a lot of weight.

With the rise of the Social Internet and large technology companies that generate data at a significant volume, speed, and variety (Google, Amazon, etc.), this is generalized to other sectors. The use of Big Data is beginning to generalize, and Machine Learning undergoes a kind of “renaissance.”

Now, they become a crucial part of many companies’ day-to-day life, who see how the large volume of data also helps them obtain more value from the way they work. In the following illustration generated by Google Trends on the search volume of both terms, it can be seen how “Machine Learning” is illuminated again when Big Data enters the “mainstream”.

And why has Big Data been so good for Machine Learning? Because as the word “learning” comes to illustrate, algorithms need data, first to learn, and second to obtain results. When data was limited, we were in danger of under-fitting problems. That is, to train the model little, and that it lost precision. Suppose we used all the data to train the model. In that case, the opposite could happen to us, “overfitting” problems, which would generate models that are too tight to the sample and perhaps not very generalizable to other cases.

Training the model with data and the problems of “underfitting” and “overfitting.”

This problem with Big Data disappears. We have so much data that we should not be concerned with balancing “training data” and “data to test and test the model and its efficiency/accuracy.” The optimization of the performance of the model (the “Just Right” of the previous graph) can now be chosen with greater flexibility since we can have data to reach that breakeven point.

With this panorama of efficient algorithms (Machine Learning) and a lot of raw material to make them work well (Big Data), you will understand why not only are there many sectors of activity where opportunities are now auspicious (the “Rethinking industries” section of the following graph), but also for technological and business development, it is an era, this one of Big Data, very interesting and valuable.

In recent years we have seen a lot of development in terms of database technology. Companies have a lot of internal data, which complements external data from the “Social Internet”. Thus, Machine Learning will accompany us during the coming years to get value from them.

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