A Quick Introduction to Machine Learning

What is data science?

First, what is data science exactly? It’s the intersection of three things: expertise in a particular domain, computer programming skills, and mathematics and statistics. Data scientists, computer scientists, statisticians and other types of scientists can all use machine learning in their work. Data science techniques such as artificial intelligence and machine learning are used to solve analytically complex problems.

What are artificial intelligence and machine learning?

Artificial intelligence, or A.I., is an area of study within the field of computer science dedicated to solving problems commonly associated with human intelligence, such as memory, problem solving and pattern recognition. One example of A.I. Would be a computer which has been programmed to recognize all possible sequences of moves in order to play the game of chess. Machine learning, or M.L., on the other hand, is a subset of A.I. where the computer learns without having been programmed for specific tasks. Instead of having lines of code telling the computer exactly what to do, in machine learning, the computer learns patterns in data and applies those patterns to predict an outcome. So when playing chess, a computer is not randomly choosing a move after assessing all possible options, but rather it’s using data gathered from millions of previously played games not just to ensure that its move is valid, but to ensure the sequence is most likely to result in a win.

Why use machine learning?

Machine learning is a tool that allows for the development, adjustment and fine tuning of complex models in order to make more accurate predictions using high volumes of data. Think of it like a human brain: as it receives more data, the model improves and can draw better conclusions, leading to stronger predictions. Machine learning is also used to automate repetitive and tedious tasks that would otherwise take many hours to complete, such as sorting and categorizing online news articles.