Let’s first understand
an overview of Machine Learning then we learn more in details about Linear
Regression.
What’s
is Machine Learning?
Here, I will
not give the theoretical definition. I will try give you a simple definition of
Machine Learning.
Machine
Learning: It is the
field of Computer Science that gives computer’s the ability to learn without
being explicitly programmed.
Now I know
you may have a doubt that then what’s the difference between Traditional programming
Vs Machine Learning.
Traditional
Programming
In Traditional
programming, we give data as an input and also the program/tasks to be done to
the computer and then the computer follows the program/instructions and
produces the outputs.
Machine
Learning
In Machine
learning, we provide data and its output as an input to the computer and try to
find a program (trained model) which can be used to predict the output of the
other data points.
Machine
Learning is broadly divided into 3 types:
1. Supervised Learning
It is a task of learning a
function/equation that maps an input to output based on the given set of
“input-output” pair.
Further, Supervised
Learning is classified into 2 types:
Regression:
It’s a technique to find a best-fitting line to the given data points.
Classification:
It’s a technique to a linear or non-linear Separator to the given data points.
2. Unsupervised Learning
It is a study of how systems can
infer a function to describe a hidden structure/pattern from the unlabeled
data. Here we will only have input data and no corresponding output data.
Unsupervised learning is
further divided into 2 types:
Clustering:
It’s a process to discover hidden groupings in the data points.
Association:
It’s a process to discover the rules that describe the huge data.
3. Reinforcement Learning
A reinforcement
learning algorithm, or agent, learns by interacting with its environment. The
agent receives rewards by performing correctly and penalties for performing
incorrectly. The agent learns without
intervention from a human by maximizing its reward and minimizing its penalty.
This is a very small introduction to Machine Learning and its types. In further articles and tutorials, you will find in-depth knowledge. So stay tuned.
I hope you enjoyed reading this article and finally, you came
to know about Introduction to Machine Learning.
For more such blogs/courses on data science, machine
learning, artificial intelligence and emerging new technologies do visit us at InsideAIML.