Introduction to Machine Learning

Ashish Katri

a year ago

Machine Learning
Machine Learning
Let’s first understand an overview of Machine Learning then we learn more in details about Linear Regression.

So, 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

Traditional Learning Vs Machine learning
Traditional Learning Vs Machine learning
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.
Thanks for reading…
Happy Learning…

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