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Machine Learning

 Starting with Machine Learning: Basics

This is a small attempt from my side to cover some basics of machine learning for people without any background for the same.After reading this post we should be able to find answers to these questions:

1. What actually is Machine-learning?

2. What are its subparts or sub-systems?

3. What is the future of Machine-Learning(ML)?

4. How and where is ML applicable and used?

So to start-with Machine-Learning is a branch of AI(artificial intelligence),involved in study and building of new intelligent-systems(computers),that can learn themselves from a given set of data,without being explicitly programmed.It allows computers to handle new situations via data-analysis,self-training,observation and experience. 

There are many complex algorithms involved to make these systems smart enough to analyse the data,find a certain pattern or logic and then based on these patterns form  some definite conclusions.

Over the period of time when similar data is processed and similar conclusions  or results arrived, these systems start recognizing the pattern and prediction the result or outcomes.

There is always a confusion between Machine-Learning and Data-mining:

Machine learning mainly focuses on prediction, based on known properties learned from the training data.The major focus of machine learning research is to extract information from data automatically, by computational and statistical methods.
Data mining on the other hand focuses on the discovery of (previously) unknown properties in the data.

As already mentioned that ML involves many complex algorithms in the background, these algorithms or learning-styles can be broadly classified as:

1. Supervised Learning
2. Unsupervised Learning
3. Semi-Supervised Learning
4. Reinforcement Learning