In this machine learning tutorial, you will learn what is machine learning, how does it solve critical problem, what would be the future for learning machine learning.
In general programming we (developers) write the code / logic to find out the end result, in Machine learning concept the result is produced by Machine (a generic set of algorithm) based on data we provide.
There are various machine learning algorithm to use depending on situation. below are the few commonly used machine learning algorithms.
In machine learning instead of writing entire code we provide data to get the output, and the output is decided by some predefined generic algorithm. (there are different types of algorithms; we can try each one to check the best expected output.)
Think of situation like online shopping, how online shopping sites recommend you things you to buy, they recommend based on your previous shopping data or based on product you click the most.
Machine learning mainly focuses on developing of programs that can access data dynamically then analyse it and learn it.
There are different types of machine learning algorithms; they are broadly classifieds into three types.
Supervised learning is something where Machine will predict based on previous training dataset, which means it knows the approximate correct answer (learned from previous dataset example).
In supervised learning we have input variable (a) and output variable (b) , and we use an algorithm to learn the mapping function from input to output (a to b).
The goal is, when next time we get a new input (a1) , we should be able to predict the output b1 (based on previous learning)
Supervised learning can be further classified into regression and classification.
Supervised machine learning algorithms are:
Unsupervised learning is we only have input data (a) and no corresponding output variable.
The Unsupervised learning goal is to understand the data structure or distribution pattern, so we can learn more about the data.
Logically yes, (but logic has limitation), actually some programming experience is helpful because you get familiar with things like writing different type of syntax, basic coding principal, different ways of writing conditional logic, those experience would defiantly help to understand machine learning faster, but, if you don’t have any previous experience then also you can start learning machine learning.
The most popular machine learning languages are Python and R Language, but there many other languages like Julia, Scala, Ruby, Octave, MATLAB,C, C++ are used for developing machine learning
Here are some machine learning implementation example.