Some concepts and pillars of Machine Learning

 

To better understand what Machine Learning is and how it works, you have to start by 

understanding why it is used.

 We human beings are daily confronted with problems we seek to solve. For example: How
build a stronger bridge? How to increase our profits?
How to eliminate cancer? Or just which road to taketo go to work ?

To help us in our researches, we invented the computer. which makes it possible to solve 

in a few minutes calculations which would take millions of years to complete. But you should

know that a computer really only knows how to do one thing: solve calculations that we give him.

 In 1959, Arthur Samuel, a computer  scientist who pioneered the study of artificial 

intelligence, described machine learning as “the study that gives computers the ability to 

learn without being explicitly programmed.”

Machine Learning is getting computers to program themselves. If programming is automation, then machine learning is automating the process of automation.

Writing software is the bottleneck, we don’t have enough good developers. Let the data do the work instead of people. Machine learning is the way to make programming scalable.

• Traditional Programming: Data and program is run on the computer to produce the output.
  
• Machine Learning: Data and output is run on the computer to create a program. This program can be used in traditional programming.

Machine learning is like farming or gardening. Seeds is the algorithms, nutrients is the data, the gardner is you and plants is the programs.

Types of Learning

There are four types of machine learning:

• Supervised learning: (also called inductive learning) Training data includes desired outputs.  This is spam this is not, learning is supervised.
• Unsupervised learning: Training data does not include desired outputs. Example is clustering. It is hard to tell what is good learning and what is not.
• Semi-supervised learning: Training data includes a few desired outputs.
  
• Reinforcement learning: Rewards from a sequence of actions. AI types like it, it is the most ambitious type of learning.

Supervised learning is the most mature, the most studied and the type of learning used by most machine learning algorithms. Learning with supervision is much easier than learning without supervision.

Inductive Learning is where we are given examples of a function in the form of data (x) and the output of the function (f(x)). The goal of inductive learning is to learn the function for new data (x).

• Classification: when the function being learned is discrete.
• Regression: when the function being learned is continuous.
• Probability Estimation: when the output of the function is a probability.