What Is Machine Learning?

There's no argument about the fact that machine learning is one of the most powerful and influential technologies in the world. More than that, even though we feel we've seen everything, we are yet to see it's full potential. There's no doubt that machine learning will be making and breaking more headlines over time. Let's learn a little more about machine learning concepts by covering fundamental ideas.

Machine learning is a tool that turns information into data and knowledge used in machine learning companies. In the past 50 years or so there has been a big explosion in data concepts. This mass of data technology is pretty much useless unless and until we're analysing it and finding what kind of patterns it hides. Machine learning techniques find hidden valuables that underline different patterns. Not only that, machine learning techniques automatically find patterns in complex data that otherwise would be a big struggle to discover.

What then?

This hidden knowledge and different patterns can be useful in predicting different kinds of events in the near future and do all kinds of decision making that is complex. A lot of us are unaware of the fact that we interact with machine learning all day everyday. Whenever we google a particular piece of information, listen to music or even take a picture, machine learning is the engine that runs it all. This way we are constantly learning and unlearning, improving all the more from every interaction. Not only that, machine learning is the driving force for self driving cars, detecting cancer and creating new drugs.

Traditional software engineering combines human rules with data to give answers to a problem. Machine learning on the other hand makes use of data to get the rules behind a problem.

Machine learning versus traditional programming

To learn the rules that govern a particular phenomenon, machines need to go through a proper learning process  trying various rules and learning from the way they perform. This is why it is known as machine learning. There are numerous forms of machine learning, supervised, semi supervised, reinforcement and unsupervised learning. Each form of this learning has a variety of approaches but they all follow one underlying approach and theory.

Terminology

There is a large set of data examples that contain features that are important to solve a problem. Important pieces of data that help us understand a problem are then fed into a machine learning algorithm so that it can learn. The model then learns from  this data. It is the output you get once you train after an algorithm. For example, to produce a decision tree model, a decision tree algorithm would have to be trained. 

What is the exact process?

You need to collect data that the algorithm will learn from. Then the data preparation bit takes place which extracts important features and performs dimensionality reduction. This is also known as the fitting stage. This is the stage where the machine learning solutions algorithms

actually begins to learn by showing it the data that has been collected before hand and prepared.