Machine Learning is a part of Data Science where the efficiency of a system improves itself by repeatedly performing the tasks by using data instead of explicitly programmed by programmers. Now let’s understand the difference between three techniques of Machine Learning- Supervised, Unsupervised and Reinforcement Learning.
1. Supervised Learning:
Supervised Learning input is provided as a labelled dataset, a model can learn from it to provide the result of the problem easily. Supervised Learning deals with two types of problem- classification problems and regression problems.
1.1 Classification problems:
This algorithm helps to predict a discrete value. It can be thought, the input data as a member of a particular class or group. For instance, taking up the photos of the fruit dataset, each photo has been labelled as a mango, an apple, etc. Here, the algorithm has to classify the new images into any of these categories. Examples:
- Naive Bayes Classifier
- Support Vector Machines
- Logistic Regression
1.2 Regression problems:
These problems are used for continuous data. For example, predicting the price of a piece of land in a city, given the area, location, number of rooms, etc. And then the input is sent to the machine for calculating the price of the land according to previous examples. Examples-
- Linear Regression
- Nonlinear Regression
- Bayesian Linear Regression
2. Unsupervised Learning:
This learning algorithm is completely opposite to Supervised Learning. In short, there is no complete and clean labelled dataset in unsupervised learning. Unsupervised learning is self-organized learning. Its main aim is to explore the underlying patterns and predicts the output. Here we basically provide the machine with data and ask to look for hidden features and cluster the data in a way that makes sense. Example
- K – Means clustering
- Neural Networks
- Principal Component Analysis
3. Reinforcement Learning:
It is neither based on supervised learning nor unsupervised learning. Moreover, here the algorithms learn to react to an environment on their own. It is rapidly growing and moreover producing a variety of learning algorithms. These algorithms are useful in the field of Robotics, Gaming etc.
For a learning agent, there is always a start state and an end state. However, to reach the end state, there might be a different path. In Reinforcement Learning Problem an agent tries to manipulate the environment. The agent travels from one state to another. The agent gets the reward(appreciation) on success but will not receive any reward or appreciation on failure. In this way, the agent learns from the environment.
There are some differences among Supervised-vs-Unsupervised-vs-Reinforcement Machine Learning. Let’s see the differences:
||Learns by using labelled data
||Trained using unlabelled data without any guidance.
||Works on interacting with the environment
|Type of data
||No – predefined data
|Type of problems
||Regression and classification
||Association and Clustering
||Exploitation or Exploration
||Linear Regression, Logistic Regression, SVM, KNN etc.
||K – Means,
C – Means, Apriori
|Q – Learning,
||Discover underlying patterns
||Learn a series of action
||Risk Evaluation, Forecast Sales
||Recommendation System, Anomaly Detection
||Self Driving Cars, Gaming, Healthcare
In this tutorial, I have shown differences among different types of machine learning. Hope you have enjoyed the tutorial. If you want to get updated, like my facebook page https://www.facebook.com/LearningBigDataAnalytics and stay connected.