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Machine Learning - Introduction



So everyone have heard of term "machine learning", but only has a fuzzy idea about it. I imagine there are a lot of people who tried reading the wikipedia article, got frustrated and gave up wishing someone would just give them a high-level explanation. That’s what this is.
  

So, What is Machine Learning

Machine Learning is a subfield within Artificial Intelligence that builds algorithms that allow computers to learn to perform tasks from data instead of being explicitly programmed.

Tom M. Mitchell provided a more formal definition, which says,

"A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E."

In simple words, machine learning is a set of techniques used to program computers and make decisions automatically. How does it make decisions ? It makes decisions by detecting (or learning) pattern in the past data and generalising it on the future data. There can be different forms of decisions such as predictions of the house prices or the weather or customer behavior, or classifications, like whether a spoken word in a recording is "world" or whether a photograph contains a face.

 

Categories of Machine Learning algorithms  

 At this point we have to talk about three general categories of Machine Learning algorithms: Supervised Learning, Unsupervised Learning, and , Reinforcement Learning algorithms

Supervised Learning:

It is one of the most commonly used types of machine learning algorithms. In these types of ML algorithms, we have input and output variables and the algorithm generates a function that predicts the output based on given input variables. It is called 'supervised' because the algorithm learns in a supervised (given target variable) fashion. This learning process iterates over the training data until the model achieves an acceptable level. Supervised learning problems can be further divided into two parts:

  1. Regerssion
  2. Classification

 

Unsupervised Learning:

In unsupervised machine learning algorithms, we only have input data and there is no corresponding output variable. The aim of these type of algorithms is to model the underlying structure or distribution in the dataset so that we can learn more about the data. It is called so because unlike supervised learning, there is no teacher and there are no correct answers. Algorithms are left to their own devices to discover and present the structure in the data.  

Reinforcement Learning:

In reinforcement learning algorithm, the machine is trained to act given an observation or make specific decisions. It is learning by interacting with an environment.The machine learns from the repercussions of its actions rather than from being explicitly taught.It is essentially trial-and-error learning where the machine selects its actions on the basis of its past experiences and new choices. In this, machine learns from these actions and tries to capture the best possible knowledge to make accurate decisions.
An example of reinforcement learning algorithm is Markov Decision Process.

Some Real-Life Applications:


  • Anti-spam: ML algorithms help spam filtration algorithms to better differentiate spam emails from anti-spam mails. 
  • Face detection/Face recognition: ML can be used in mobile cameras, laptops, etc. for face detection and recognition. For instance, cameras snap a photo automatically whenever someone smiles much more accurately now because of advancements in machine learning algorithms.  
  • Speech recognition: Speech recognition systems have improved significantly because of machine learning. For example, look at Google now.
  • Game playing: There can be two ways in which ML can be implemented in games, i.e., during the design phase and during runtime.
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