This is the first of a series of articles intended to make Machine Learning more approachable to those who do not have a technical training. I hope it is helpful.
Advancements in computer technology over the past decades have meant that the collection of electronic data has become more commonplace in most fields of human endeavor. Many organizations now find themselves holding large amounts of data spanning many prior years. This data can relate to people, financial transactions, biological information, and much, much more.
Simultaneously, data scientists have been developing iterative computer programs called algorithms that can look at this large amount of data, analyse it and identify patterns and relationships that cannot be identified by humans. Analyzing past phenomena can provide extremely valuable information about what to expect in the future from the same, or closely related, phenomena. In this sense, these algorithms can learn from the past and use this learning to make valuable predictions about the future.
While learning from data is not in itself a new concept, Machine Learning differentiates itself from other methods of learning by a capacity to deal with a much greater quantity of data, and a capacity to handle data that has limited structure. This allows Machine Learning to be successfully utilized on a wide array of topics that had previously been considered too complex for other learning methods.
Read more at Towards Data Science
