Imagine an artificial neural network in which everyone can input their own data and see its output. If you do this, it will give you the opportunities to visualize how well a particular algorithm accomplishes a certain task. On top of that, if you are able to modify the source code of this program then maybe, one day you would be able to create something called meta-algorithm! The concept here is that after getting wonderful results from your initial algorithm, now you start writing programs just for improving algorithms! And I am sure that someday all the major projects will use machine learning techniques in order to complete them more quickly than before. This article understands some popular machine learning algorithms used in software development and describes how they will be changing the world of project management.
Just like there are lots of different types of algorithms in software development, machine learning can be classified into three major types: Supervised (classification and regression), Unsupervised (clustering) and Reinforcement Learning. Let’s get a bit deeper understanding of each type.
Unsupervised Machine Learning Techniques – An application using unsupervised learning is where we do not have an answer key i.e. input-output pairs for some data that you want to predict. One good example here is where we need to group similar instances spread across multiple groups. This could mean detecting spam e-mails or grouping special customers according to their spending habits, etc. For such cases, we need techniques like Artificial Neural Network (ANN), Self Organizing Map (SOM), etc.
Unsupervised machine learning is usually followed by supervised learning because its outputs are often used as input data to the supervised system.
Supervised Machine Learning Techniques – An application using this type of technology gives us training datasets where we have associated answers for given inputs. For example, if you want to predict how a patient will react to a drug while he/she suffers from several diseases at the same time, then there must be one more dataset associated with that case study and it should tell what happened in real life after taking the medicine. In order to solve tasks this way, we need classification tools like K-nearest neighbours or Support Vector Machines (SVM).
Supervised algorithms are followed by unsupervised ones, as you need first to understand the factors that will influence your target value and how much it will be affected. On top of that, you can use both outputs of supervised and unsupervised algorithms in order to perform certain tasks like clustering or grouping similar instances from a dataset.
Unsupervised Machine Learning – When we have some input data but we want to find hidden structures within this data so that we can transform them into useful information i.e. understanding the inter-relationship between various attributes associated with the samples… etc., and then an application is called unsupervised machine learning. In other words, if you create clusters of documents based on some attributes like colour, font size, etc. then you need clustering algorithms like K-means or hierarchical clustering.