10 Algorithms Machine Learning Engineers Need To Know About
With the fast mechanization brought about by the technological revolution, the word manual is slowly getting lost amidst the crowd and will very soon completely vanish. As Big Data has whisked the tech industry, Machine Learning is gaining importance and has robustly handled huge amount of data making accurate predictions.
In an era of constant progress, we can only guess what astounding invention and discovery is to come next. The data-crunching machines that have been seamlessly executing the advanced techniques.
Machine learning is a subset of the Artificial Intelligence, which is a broader term and concept. Where Artificial Intelligence aims to make computers smarter and intelligent, Machine Learning has come up with ways to do that. In short, it is application of Artificial Intelligence. With the use of algorithms, that iteratively learn from data, machine learning improves the functionality of computers without being explicitly programmed.
If you are a Data Scientist or a machine learning passionate, you can work your way around machine learning projects using categories in which the algorithms of machine learning have been broken down.
Using a pre-defined set of “training examples”, the program is trained facilitating its ability to reach on conclusions when new data is fed. The program is trained until the desired level of accuracy is reached.
The program is given a bunch of data and it has to detect pattern and relationship on its own. The system has to infer function to describe the pattern from the unclassified data.
Here the system interacts with the environment and produces actions discovering errors or rewards.
All three techniques are employed in the ten common machine learning algorithms.
The algorithm can be visualized by placing items according to their weight. But the problem arises when you can’t actually weigh it, you have to do a bit of guessing here by looking at its height and width. With this visual analysis, you reach on a result. This is how linear regression works.
A relationship is formed by mapping the dependent and independent variable on a line. The line is called regression line and is represented by Y= a*X + b.
The coefficients a & b are derived by reducing the sum of the squared difference of distance between data points and the regression line.
From a set of independent variables, discrete values are estimated. It helps in the prediction of an event by fitting data to a logit function.
The following methods are used to improvise the logistic regression models:
It uses supervised learning algorithm to classify problems. Decision tree is the support tool that uses a tree-like graph for decisions or probable consequences, chance-event outcomes, resources costs and utilities. The population is split into two or more homogeneous sets based on the independent variable.
In Support Vector Machine or SVM, raw data is plot in n-dimensional space.
