Machine Learning Part_2
Machine Learning can be divided into 3 categorizations: Supervised, Unsupervised and Reinforcement Learning.
Let see the difference between Supervised, Unsupervised and Reinforcement in simply 😊.
Supervised Machine Learning
The below table shows a data set which is consist with rows of data. When we working with machine learning we need datasets for different purposes.
Let see some terminologies we are going to use in Machine Learning.
We call a row as an observation, an example or a tuple.
Features: Features are the input variables that describing our data.
Labels: A Label is the target that we’ve trying to predict.
Then what is Supervised Machine Learning???
First, we are training the model using Features and Labels. Then we are learning to create models that combine inputs, to produce useful predictions even on previously unseen data. We can divide supervised machine learning into two categories; Classification and regression.
What is classification???
If we can select one of known label according to given feature; we known it as classification.
Classification can be divided into two categories; Binary Class and Multiclass
Binary class- If we can select one of two known labels, we say it belongs to binary class.
As an example: — Labels like ‘yes’ or ‘no’
Multi class- if we can select one of the labels out of multiple known labels, we say it belongs to multi class.
As an example: — We can categorize the types of songs (Labels) as below.
Rock, Ballads, Soul, Novelty, and Folk etc.
What is Regression???
If we can’t predicate the label accurately. It used to predict a continuous value. Predicting prices of a house given the features of the house like size, price etc.
What is Unsupervised learning???
Unsupervised learning is a machine technique, where you do not need to supervise the model. Instead, you need to allow the model to work on its to discover information. It mainly deals with the unlabeled data.
Example:
Let’s, take the case of a baby and her family dog.
She knows and identifies this dog. Fes weeks later a family friend brings along a dog and tries to play with the baby.
Baby has not seen this dog earlier. But it recognizes many features (2 ears, eyes, walking on 4 legs) are like her pet dog. She identifies the new animal as a dog. This is the unsupervised learning.
Let’s look at what is Reinforcement Learning
Reinforcement learning is all about making decisions sequentially. In simple words we can say that the output depends on the state of the current input and next input depends on the output of the previous input. In Reinforcement learning decision is dependent, so we give labels to sequences of dependent decisions.
Ex — Chess
Let’s learn about some architectures related to machine learning in chapter 😊.
