Machine Learning Explanation : Supervised Learning & Unsupervised Learning
What Is Machine Learning? What Is Supervised Learning? and What Is Unsupervised Learning? — Simple explanation regarding Machine Learning
What Is Machine Learning?
Machine Learning :
Making machines think like humans,
Machine Learning “think” based on data & apply algorithm to teach themselves
How can machines think like humans?
The answer is : Understand how humans think in the first place.
Nature’s model of human thinking
- Fetching massive data/information
- Sense
- Thinking process in the brain
- Make prediction
How this model can be used to machine?
The answer is : Making machines that learn.
Model of machine learning “thinking”
- From massive data
- Apply algorithm to teach themselves
- Process in the computer
- Make prediction
How to use machine learning?
The answer is : Understand case in the first place.
Machine learning type depending on case
How many types are there in machine learning?
There are two types of machine learning
- Supervised Learning
- Unsupervised Learning
Supervised Learning
Have a Rule
Have data input and it’s label for train
According to Wikipedia :
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a “reasonable” way. Architecture or framework of supervised learning is provided on Figure below :
There are two case in supervised learning
Classification & Regression
Classification
Classification is predicting discrete or categorical output
According to Wikipedia :
In statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. Examples are assigning a given email to the “spam” or “non-spam” class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Classification is an example of pattern recognition.
Example case in Classification :
There are methods or algorithms that can be used in case classification : Logistic Regression, Decision Tree Classification, Random Forest Classification, Neural Network Classification, etc
Regression
Regression is predicting continuous or numeric output
According to Wikipedia :
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the ‘outcome variable’) and one or more independent variables (often called ‘predictors’, ‘covariates’, or ‘features’).
Example case in Regression :
There are methods or algorithms that can be used in case regression : Linear Regression, Decision Tree Regression, Random Forest Regression, Neural Network Regression, etc
Unsupervised Learning
Pattern discovery by given input data only without any label.
According to Wikipedia :
Unsupervised learning is a type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision. In contrast to supervised learning that usually makes use of human-labeled data, unsupervised learning, also known as self-organization allows for modeling of probability densities over inputs. Architecture or framework of unsupervised learning is provided on Figure below :
There are three case in Unsupervised Learning
Clustering, Dimensionality Reduction, and Association Rule
Clustering
Clustering is grouping data based on similarity patterns
There are methods or algorithms that can be used in case clustering : K-Means Clustering, Affinity Propagation, Mean Shift, Spectral Clustering, Hierarchical Clustering, DBSCAN, etc
About Me
I’m a Data Scientist, Focus on Machine Learning and Deep Learning. You can reach me from Medium and Linkedin
My Website : https://komuternak.com/
